ArtPlayer/docs/uncompiled/artplayer-plugin-danmuku-mask/index.js

// modules are defined as an array
// [ module function, map of requires ]
//
// map of requires is short require name -> numeric require
//
// anything defined in a previous bundle is accessed via the
// orig method which is the require for previous bundles

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    typeof globalThis !== 'undefined'
      ? globalThis
      : typeof self !== 'undefined'
      ? self
      : typeof window !== 'undefined'
      ? window
      : typeof global !== 'undefined'
      ? global
      : {};
  /* eslint-enable no-undef */

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    function localRequire(x) {
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  for (var i = 0; i < entry.length; i++) {
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  if (mainEntry) {
    // Expose entry point to Node, AMD or browser globals
    // Based on https://github.com/ForbesLindesay/umd/blob/master/template.js
    var mainExports = newRequire(mainEntry);

    // CommonJS
    if (typeof exports === 'object' && typeof module !== 'undefined') {
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        return mainExports;
      });

      // <script>
    } else if (globalName) {
      this[globalName] = mainExports;
    }
  }
})({"8uLJa":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "default", ()=>artplayerPluginDanmukuMask);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _tfjsBackendWebgl = require("@tensorflow/tfjs-backend-webgl");
var _tfjsBackendCpu = require("@tensorflow/tfjs-backend-cpu");
var _bodySegmentation = require("@tensorflow-models/body-segmentation");
function artplayerPluginDanmukuMask(option = {}) {
    return (art)=>{
        const { template: { $video, $danmuku } } = art;
        let segmenter = null;
        let canvas = null;
        let ctx = null;
        let animationFrameId = null;
        let isInitialized = false;
        const config = {
            solutionPath: option.solutionPath || "https://cdn.jsdelivr.net/npm/@mediapipe/selfie_segmentation",
            modelSelection: option.modelSelection || 1,
            smoothSegmentation: option.smoothSegmentation !== undefined ? option.smoothSegmentation : true,
            minDetectionConfidence: option.minDetectionConfidence || 0.5,
            minTrackingConfidence: option.minTrackingConfidence || 0.5,
            selfieMode: option.selfieMode || false,
            drawContour: option.drawContour || false,
            foregroundThreshold: option.foregroundThreshold || 0.5,
            opacity: option.opacity || 1,
            maskBlurAmount: option.maskBlurAmount || 3
        };
        async function initTensorFlow() {
            try {
                await _tfjsCore.setBackend("webgl");
            } catch (error) {
                console.warn("WebGL backend not available, falling back to CPU");
                await _tfjsCore.setBackend("cpu");
            }
        }
        async function initSegmenter() {
            await initTensorFlow();
            const model = _bodySegmentation.SupportedModels.MediaPipeSelfieSegmentation;
            const segmenterConfig = {
                runtime: "mediapipe",
                modelType: "general",
                solutionPath: config.solutionPath,
                modelSelection: config.modelSelection,
                smoothSegmentation: config.smoothSegmentation,
                minDetectionConfidence: config.minDetectionConfidence,
                minTrackingConfidence: config.minTrackingConfidence,
                selfieMode: config.selfieMode
            };
            try {
                segmenter = await _bodySegmentation.createSegmenter(model, segmenterConfig);
                console.log("Segmenter initialized:", segmenter);
                isInitialized = true;
            } catch (error) {
                console.error("Error initializing segmenter:", error);
                isInitialized = false;
            }
        }
        function setupDanmukuStyle() {
            Object.assign($danmuku.style, {
                maskMode: "alpha",
                maskSize: "contain",
                maskRepeat: "no-repeat",
                backgroundSize: "contain",
                backgroundRepeat: "no-repeat"
            });
        }
        function createCanvas() {
            canvas = document.createElement("canvas");
            ctx = canvas.getContext("2d");
        }
        function makeWhiteTransparent(imageData) {
            const data = imageData.data;
            for(let i = 0; i < data.length; i += 4)if (data[i] > 250 && data[i + 1] > 250 && data[i + 2] > 250) data[i + 3] = 0;
            return imageData;
        }
        async function segmentBody() {
            if (!isInitialized || $video.paused || $video.ended) {
                animationFrameId = requestAnimationFrame(segmentBody);
                return;
            }
            try {
                canvas.width = $video.videoWidth;
                canvas.height = $video.videoHeight;
                const segmentation = await segmenter.segmentPeople($video);
                if (!segmentation || segmentation.length === 0) {
                    animationFrameId = requestAnimationFrame(segmentBody);
                    return;
                }
                const foregroundColor = {
                    r: 255,
                    g: 255,
                    b: 255,
                    a: 255
                };
                const backgroundColor = {
                    r: 0,
                    g: 0,
                    b: 0,
                    a: 255
                };
                const mask = await _bodySegmentation.toBinaryMask(segmentation, foregroundColor, backgroundColor, config.drawContour, config.foregroundThreshold);
                await _bodySegmentation.drawMask(canvas, $video, mask, config.opacity, config.maskBlurAmount);
                const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
                ctx.putImageData(makeWhiteTransparent(imageData), 0, 0);
                $danmuku.style.maskImage = `url(${canvas.toDataURL()})`;
            } catch (error) {
                console.error("Error in segmentBody:", error);
            }
            animationFrameId = requestAnimationFrame(segmentBody);
        }
        async function startSegmentation() {
            if (!isInitialized) await initSegmenter();
            if (!canvas) createCanvas();
            setupDanmukuStyle();
            segmentBody();
        }
        function stopSegmentation() {
            $danmuku.style.maskImage = "none";
            if (animationFrameId) {
                cancelAnimationFrame(animationFrameId);
                animationFrameId = null;
            }
        }
        art.on("ready", startSegmentation);
        art.on("destroy", stopSegmentation);
        return {
            name: "artplayerPluginDanmukuMask",
            start: startSegmentation,
            stop: stopSegmentation
        };
    };
}
if (typeof window !== "undefined") window["artplayerPluginDanmukuMask"] = artplayerPluginDanmukuMask;

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6","@tensorflow/tfjs-core":"21Ftl","@tensorflow/tfjs-backend-webgl":"bXLEs","@tensorflow-models/body-segmentation":"5k877","@tensorflow/tfjs-backend-cpu":"bzQr6"}],"5dUr6":[function(require,module,exports) {
exports.interopDefault = function(a) {
    return a && a.__esModule ? a : {
        default: a
    };
};
exports.defineInteropFlag = function(a) {
    Object.defineProperty(a, "__esModule", {
        value: true
    });
};
exports.exportAll = function(source, dest) {
    Object.keys(source).forEach(function(key) {
        if (key === "default" || key === "__esModule" || Object.prototype.hasOwnProperty.call(dest, key)) return;
        Object.defineProperty(dest, key, {
            enumerable: true,
            get: function() {
                return source[key];
            }
        });
    });
    return dest;
};
exports.export = function(dest, destName, get) {
    Object.defineProperty(dest, destName, {
        enumerable: true,
        get: get
    });
};

},{}],"21Ftl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Required side effectful code.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
var _baseSideEffects = require("./base_side_effects");
// TODO(mattSoulanille): Move this to base_side_effects.ts
// It is here for now because custom bundles need to avoid calling it, and they
// only replace the index.js file, not the base_side_effects file.
var _registerOptimizers = require("./optimizers/register_optimizers");
// All exports from this package should be in base.
var _base = require("./base");
parcelHelpers.exportAll(_base, exports);
(0, _registerOptimizers.registerOptimizers)();

},{"./base_side_effects":"8sUPD","./optimizers/register_optimizers":"k7xLP","./base":"ATETN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8sUPD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Required side effectful code for tfjs-core
// Set up Engine and ENV
var _engine = require("./engine");
// Register backend-agnostic flags.
var _flags = require("./flags");
// Register platforms
var _platformBrowser = require("./platforms/platform_browser");
var _platformNode = require("./platforms/platform_node");
// Set up OpHandler
var _buffer = require("./ops/buffer");
var _cast = require("./ops/cast");
var _clone = require("./ops/clone");
var _print = require("./ops/print");
var _tensor = require("./tensor");
(0, _engine.getOrMakeEngine)();
const opHandler = {
    buffer: (0, _buffer.buffer),
    cast: (0, _cast.cast),
    clone: (0, _clone.clone),
    print: (0, _print.print)
};
(0, _tensor.setOpHandler)(opHandler);

},{"./engine":"1g2qo","./flags":"5yJGC","./platforms/platform_browser":"k39df","./platforms/platform_node":"2Z7zs","./ops/buffer":"dYyY7","./ops/cast":"b4VDA","./ops/clone":"jmQ7S","./ops/print":"5RyfW","./tensor":"cJNoh"}],"1g2qo":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Engine", ()=>Engine);
parcelHelpers.export(exports, "getOrMakeEngine", ()=>getOrMakeEngine);
parcelHelpers.export(exports, "ENGINE", ()=>ENGINE);
/**
 * A implementation of the add op for use within engine and tape.
 *
 * This allows us to avoid a circular dependency between add.ts and engine.
 * It is exported to be available in tape tests.
 */ parcelHelpers.export(exports, "add", ()=>add);
var _backend = require("./backends/backend");
var _environment = require("./environment");
var _globalUtil = require("./global_util");
var _kernelNames = require("./kernel_names");
var _kernelRegistry = require("./kernel_registry");
var _log = require("./log");
var _profiler = require("./profiler");
var _tape = require("./tape");
var _tensor = require("./tensor");
var _tensorUtil = require("./tensor_util");
var _util = require("./util");
function isRegisteredKernelInvocation(kernelInvocation) {
    return kernelInvocation.kernelName != null;
}
class EngineState {
    constructor(){
        // Public since optimizers will use it.
        this.registeredVariables = {};
        this.nextTapeNodeId = 0;
        this.numBytes = 0;
        this.numTensors = 0;
        this.numStringTensors = 0;
        this.numDataBuffers = 0;
        // Number of nested tf.grad() statements when computing higher-order
        // gradients. E.g. `1` for first-order gradients and `2` for second-order
        // gradients. Used to track if the tape should be removed after a backprop.
        this.gradientDepth = 0;
        // Number of nested kernel calls. When kernel depth is greater than 1, we turn
        // off the tape.
        this.kernelDepth = 0;
        this.scopeStack = [];
        /**
         * Keeps track of the number of data moves during a kernel execution. We
         * maintain a stack since kernels can call other kernels, recursively.
         */ this.numDataMovesStack = [];
        this.nextScopeId = 0;
        this.tensorInfo = new WeakMap();
        this.profiling = false;
        this.activeProfile = {
            newBytes: 0,
            newTensors: 0,
            peakBytes: 0,
            kernels: [],
            result: null,
            get kernelNames () {
                return Array.from(new Set(this.kernels.map((k)=>k.name)));
            }
        };
    }
    dispose() {
        for(const variableName in this.registeredVariables)this.registeredVariables[variableName].dispose();
    }
}
class Engine {
    constructor(ENV){
        this.ENV = ENV;
        this.registry = {};
        this.registryFactory = {};
        this.pendingBackendInitId = 0;
        this.state = new EngineState();
    }
    async ready() {
        if (this.pendingBackendInit != null) return this.pendingBackendInit.then(()=>{});
        if (this.backendInstance != null) return;
        const sortedBackends = this.getSortedBackends();
        for(let i = 0; i < sortedBackends.length; i++){
            const backendName = sortedBackends[i];
            const success = await this.initializeBackend(backendName).success;
            if (success) {
                await this.setBackend(backendName);
                return;
            }
        }
        throw new Error(`Could not initialize any backends, all backend initializations ` + `failed.`);
    }
    get backend() {
        if (this.pendingBackendInit != null) throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make ` + `sure to await tf.ready() or await tf.setBackend() before calling ` + `other methods`);
        if (this.backendInstance == null) {
            const { name, asyncInit } = this.initializeBackendsAndReturnBest();
            if (asyncInit) throw new Error(`The highest priority backend '${name}' has not yet been ` + `initialized. Make sure to await tf.ready() or ` + `await tf.setBackend() before calling other methods`);
            this.setBackend(name);
        }
        return this.backendInstance;
    }
    backendNames() {
        return Object.keys(this.registryFactory);
    }
    findBackend(backendName) {
        if (!(backendName in this.registry)) {
            // If the backend hasn't been initialized but we have a registry entry for
            // it, initialize it and return it.
            if (backendName in this.registryFactory) {
                const { asyncInit } = this.initializeBackend(backendName);
                if (asyncInit) // Backend is not ready yet.
                return null;
            } else return null;
        }
        return this.registry[backendName];
    }
    findBackendFactory(backendName) {
        if (!(backendName in this.registryFactory)) return null;
        return this.registryFactory[backendName].factory;
    }
    registerBackend(backendName, factory, priority = 1) {
        if (backendName in this.registryFactory) {
            _log.warn(`${backendName} backend was already registered. ` + `Reusing existing backend factory.`);
            return false;
        }
        this.registryFactory[backendName] = {
            factory,
            priority
        };
        return true;
    }
    async setBackend(backendName) {
        if (this.registryFactory[backendName] == null) throw new Error(`Backend name '${backendName}' not found in registry`);
        this.backendName = backendName;
        if (this.registry[backendName] == null) {
            this.backendInstance = null;
            const { success, asyncInit } = this.initializeBackend(backendName);
            const result = asyncInit ? await success : success;
            if (!result) return false;
        }
        this.backendInstance = this.registry[backendName];
        this.setupRegisteredKernels();
        // Reset the profiler.
        this.profiler = new (0, _profiler.Profiler)(this.backendInstance);
        return true;
    }
    setupRegisteredKernels() {
        const kernels = (0, _kernelRegistry.getKernelsForBackend)(this.backendName);
        kernels.forEach((kernel)=>{
            if (kernel.setupFunc != null) kernel.setupFunc(this.backendInstance);
        });
    }
    disposeRegisteredKernels(backendName) {
        const kernels = (0, _kernelRegistry.getKernelsForBackend)(backendName);
        kernels.forEach((kernel)=>{
            if (kernel.disposeFunc != null) kernel.disposeFunc(this.registry[backendName]);
        });
    }
    /**
     * Initializes a backend by looking up the backend name in the factory
     * registry and calling the factory method. Returns a boolean representing
     * whether the initialization of the backend suceeded. Throws an error if
     * there is no backend in the factory registry.
     */ initializeBackend(backendName) {
        const registryFactoryEntry = this.registryFactory[backendName];
        if (registryFactoryEntry == null) throw new Error(`Cannot initialize backend ${backendName}, no registration found.`);
        try {
            const backend = registryFactoryEntry.factory();
            /* Test if the factory returns a promise.
            Done in a more liberal way than
            previous 'Promise.resolve(backend)===backend'
            as we needed to account for custom Promise
            implementations (e.g. Angular) */ if (backend && !(backend instanceof (0, _backend.KernelBackend)) && typeof backend.then === "function") {
                const promiseId = ++this.pendingBackendInitId;
                const success = backend.then((backendInstance)=>{
                    // Outdated promise. Another backend was set in the meantime.
                    if (promiseId < this.pendingBackendInitId) return false;
                    this.registry[backendName] = backendInstance;
                    this.pendingBackendInit = null;
                    return true;
                }).catch((err)=>{
                    // Outdated promise. Another backend was set in the meantime.
                    if (promiseId < this.pendingBackendInitId) return false;
                    this.pendingBackendInit = null;
                    _log.warn(`Initialization of backend ${backendName} failed`);
                    _log.warn(err.stack || err.message);
                    return false;
                });
                this.pendingBackendInit = success;
                return {
                    success,
                    asyncInit: true
                };
            } else {
                this.registry[backendName] = backend;
                return {
                    success: true,
                    asyncInit: false
                };
            }
        } catch (err) {
            _log.warn(`Initialization of backend ${backendName} failed`);
            _log.warn(err.stack || err.message);
            return {
                success: false,
                asyncInit: false
            };
        }
    }
    removeBackend(backendName) {
        if (!(backendName in this.registryFactory)) throw new Error(`${backendName} backend not found in registry`);
        if (this.backendName === backendName && this.pendingBackendInit != null) // There is a pending promise of the backend we want to remove. Make it
        // obsolete.
        this.pendingBackendInitId++;
        if (backendName in this.registry) {
            this.disposeRegisteredKernels(backendName);
            this.registry[backendName].dispose();
            delete this.registry[backendName];
        }
        delete this.registryFactory[backendName];
        // Unset the backend if it is active.
        if (this.backendName === backendName) {
            this.pendingBackendInit = null;
            this.backendName = null;
            this.backendInstance = null;
        }
    }
    getSortedBackends() {
        if (Object.keys(this.registryFactory).length === 0) throw new Error("No backend found in registry.");
        return Object.keys(this.registryFactory).sort((a, b)=>{
            // Highest priority comes first.
            return this.registryFactory[b].priority - this.registryFactory[a].priority;
        });
    }
    initializeBackendsAndReturnBest() {
        const sortedBackends = this.getSortedBackends();
        for(let i = 0; i < sortedBackends.length; i++){
            const backendName = sortedBackends[i];
            const { success, asyncInit } = this.initializeBackend(backendName);
            if (asyncInit || success) return {
                name: backendName,
                asyncInit
            };
        }
        throw new Error(`Could not initialize any backends, all backend initializations ` + `failed.`);
    }
    moveData(backend, dataId) {
        const info = this.state.tensorInfo.get(dataId);
        const srcBackend = info.backend;
        const values = this.readSync(dataId);
        const refCount = srcBackend.refCount(dataId);
        // Delete the tensor from the old backend and move it to the new
        // backend.
        srcBackend.disposeData(dataId, true);
        info.backend = backend;
        backend.move(dataId, values, info.shape, info.dtype, refCount);
        if (this.shouldCheckForMemLeaks()) // Track the number of moves during a kernel execution to correctly
        // detect memory leaks.
        this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++;
    }
    tidy(nameOrFn, fn) {
        let name = null;
        if (fn == null) {
            // Called with only 1 argument.
            if (typeof nameOrFn !== "function") throw new Error("Please provide a function to tidy()");
            fn = nameOrFn;
        } else {
            // Called with 2 arguments.
            if (typeof nameOrFn !== "string" && !(nameOrFn instanceof String)) throw new Error("When calling with two arguments, the first argument to tidy() must be a string");
            if (typeof fn !== "function") throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");
            name = nameOrFn;
        // TODO(nsthorat,smilkov): Do operation logging and performance
        // profiling.
        }
        let result;
        return this.scopedRun(()=>this.startScope(name), ()=>this.endScope(result), ()=>{
            result = fn();
            if (result instanceof Promise) console.error("Cannot return a Promise inside of tidy.");
            return result;
        });
    }
    scopedRun(start, end, f) {
        start();
        try {
            const res = f();
            end();
            return res;
        } catch (ex) {
            end();
            throw ex;
        }
    }
    nextTensorId() {
        return Engine.nextTensorId++;
    }
    nextVariableId() {
        return Engine.nextVariableId++;
    }
    /**
     * This method is called instead of the public-facing tensor.clone() when
     * saving a tensor for backwards pass. It makes sure to add the clone
     * operation to the tape regardless of being called inside a kernel
     * execution.
     */ clone(x) {
        const y = ENGINE.runKernel((0, _kernelNames.Identity), {
            x
        });
        const inputs = {
            x
        };
        const grad = (dy)=>({
                x: ()=>{
                    const dtype = "float32";
                    const gradInputs = {
                        x: dy
                    };
                    const attrs = {
                        dtype
                    };
                    return ENGINE.runKernel((0, _kernelNames.Cast), gradInputs, // tslint:disable-next-line: no-unnecessary-type-assertion
                    attrs);
                }
            });
        const saved = [];
        this.addTapeNode(this.state.activeScope.name, inputs, [
            y
        ], grad, saved, {});
        return y;
    }
    /**
     * Execute a kernel with the given name and return the output tensor.
     *
     * @param kernelName The name of the kernel to execute.
     * @param inputs A map of input names to tensors.
     * @param attrs A map of attribute names to their values. An attribute is a
     *     primitive (non-tensor) input to the kernel.
     * @param inputsToSave A list of tensors, inputs to save for the backprop
     *     computation.
     * @param outputsToSave A list of booleans, specifying which output to save
     *     for the backprop computation. These are booleans since the output
     * tensors are not visible to the user.
     */ runKernel(kernelName, inputs, attrs) {
        if (this.backendName == null) // backend has not been initialized yet (backend initialization is lazy
        // can be deferred until an op/ kernel is run).
        // The below getter has side effects that will try to initialize the
        // backend and set properties like this.backendName
        // tslint:disable-next-line: no-unused-expression
        this.backend;
        const hasKernel = (0, _kernelRegistry.getKernel)(kernelName, this.backendName) != null;
        if (!hasKernel) throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);
        return this.runKernelFunc({
            kernelName,
            inputs,
            attrs
        });
    }
    shouldCheckForMemLeaks() {
        return this.ENV.getBool("IS_TEST");
    }
    checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos) {
        const numDataIdsAfter = this.backend.numDataIds();
        // Count the number of data ids associated with the result of the kernel.
        let numOutputDataIds = 0;
        outInfos.forEach((info)=>{
            // Complex numbers allocate 3 data ids, one for 'real', one for
            // 'imaginary', and one for the container that holds the former two.
            numOutputDataIds += info.dtype === "complex64" ? 3 : 1;
        });
        // Account for the number of moves during kernel execution. A "data move"
        // can happen in the middle of a kernel execution, placing a new (key,value)
        // pair in the data storage. Since data moves have net zero effect (we
        // always remove the data from the old backend), we have to cancel them out
        // when detecting memory leaks.
        const numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1];
        const dataIdsLeaked = numDataIdsAfter - numDataIdsBefore - numOutputDataIds - numMoves;
        if (dataIdsLeaked > 0) throw new Error(`Backend '${this.backendName}' has an internal memory leak ` + `(${dataIdsLeaked} data ids) after running '${kernelName}'`);
    }
    /**
     * Internal helper method to execute a kernel Func
     *
     * Use `runKernel` to execute kernels from outside of engine.
     */ runKernelFunc(kernelParams) {
        let outputs;
        let saved = [];
        const isTapeOn = this.isTapeOn();
        const startingBytecount = this.state.numBytes;
        const startingNumTensors = this.state.numTensors;
        if (this.shouldCheckForMemLeaks()) this.state.numDataMovesStack.push(0);
        let kernelFunc;
        if (this.backendName == null) // backend has not been initialized yet (backend initialization is lazy
        // can be deferred until an op/ kernel is run).
        // The below getter has side effects that will try to initialize the
        // backend and set properties like this.backendName
        // tslint:disable-next-line: no-unused-expression
        this.backend;
        let out;
        const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
        // Create the kernelFunc from either a registered kernel OR passed in
        // forward/backward functions (used by custom grad). In this context a
        // kernelFunc wraps a kernel implementation with some bookkeeping.
        if (isRegisteredKernelInvocation(kernelParams)) {
            const { kernelName, inputs, attrs } = kernelParams;
            if (this.backendName == null) // backend has not been initialized yet (backend initialization is lazy
            // can be deferred until an op/ kernel is run).
            // The below getter has side effects that will try to initialize the
            // backend and set properties like this.backendName
            // tslint:disable-next-line: no-unused-expression
            this.backend;
            const kernel = (0, _kernelRegistry.getKernel)(kernelName, this.backendName);
            _util.assert(kernel != null, ()=>`Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);
            kernelFunc = ()=>{
                const numDataIdsBefore = this.backend.numDataIds();
                out = kernel.kernelFunc({
                    inputs,
                    attrs,
                    backend: this.backend
                });
                const outInfos = Array.isArray(out) ? out : [
                    out
                ];
                if (this.shouldCheckForMemLeaks()) this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
                const outTensors = outInfos.map((outInfo)=>{
                    // todo (yassogba) remove this option (Tensor) when node backend
                    // methods have been modularized and they all return tensorInfo.
                    // TensorInfos do not have a rank attribute.
                    if (outInfo.rank != null) return outInfo;
                    return this.makeTensorFromTensorInfo(outInfo);
                });
                // Save any required inputs and outputs.
                // Do not save unless we are recording to the tape. Otherwise it would
                // cause a mem leak since there would be no backprop for these tensors
                // (which would otherwise dispose them).
                if (isTapeOn) {
                    const tensorsToSave = this.getTensorsForGradient(kernelName, inputs, outTensors);
                    saved = this.saveTensorsForBackwardMode(tensorsToSave);
                }
                return outTensors;
            };
        } else {
            const { forwardFunc } = kernelParams;
            // Running a customGrad op.
            const saveFunc = (tensors)=>{
                // Do not save unless we are recording to the tape. Otherwise it would
                // cause a mem leak since we would never run backprop, which disposes
                // the kept tensors.
                if (!isTapeOn) return;
                saved = tensors.map((tensor)=>this.keep(this.clone(tensor)));
            };
            kernelFunc = ()=>{
                const numDataIdsBefore = this.backend.numDataIds();
                out = this.tidy(()=>forwardFunc(this.backend, saveFunc));
                const outs = Array.isArray(out) ? out : [
                    out
                ];
                if (this.shouldCheckForMemLeaks()) // Scope name is used to print a more helpful error message if needed.
                this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);
                return outs;
            };
        }
        //
        // Run the kernelFunc. Optionally profiling it.
        //
        const { inputs, attrs } = kernelParams;
        const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;
        let kernelProfile;
        this.scopedRun(// Stop recording to a tape when running a kernel.
        ()=>this.state.kernelDepth++, ()=>this.state.kernelDepth--, ()=>{
            if (!this.ENV.getBool("DEBUG") && !this.state.profiling) outputs = kernelFunc();
            else {
                kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, ()=>kernelFunc());
                if (this.ENV.getBool("DEBUG")) this.profiler.logKernelProfile(kernelProfile);
                outputs = kernelProfile.outputs;
            }
        });
        if (isTapeOn) this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);
        if (this.state.profiling) this.state.activeProfile.kernels.push({
            name: kernelOrScopeName,
            bytesAdded: this.state.numBytes - startingBytecount,
            totalBytesSnapshot: this.state.numBytes,
            tensorsAdded: this.state.numTensors - startingNumTensors,
            totalTensorsSnapshot: this.state.numTensors,
            inputShapes: Object.keys(inputs).map((key)=>inputs[key] != null ? inputs[key].shape : null),
            outputShapes: outputs.map((item)=>item.shape),
            kernelTimeMs: kernelProfile.timeMs,
            extraInfo: kernelProfile.extraInfo
        });
        return Array.isArray(out) ? outputs : outputs[0];
    }
    /**
     * Saves tensors used in forward mode for use in backward mode.
     *
     * @param tensors the list of tensors to save.
     */ saveTensorsForBackwardMode(tensors) {
        const saved = tensors.map((tensor)=>this.keep(this.clone(tensor)));
        return saved;
    }
    /**
     * Returns a list of tensors to save for a given gradient calculation.
     *
     * @param kernelName name of kernel to look up gradient for.
     * @param inputs a map of input tensors.
     * @param outputs an array of output tensors from forward mode of kernel.
     */ getTensorsForGradient(kernelName, inputs, outputs) {
        const gradConfig = (0, _kernelRegistry.getGradient)(kernelName);
        if (gradConfig != null) {
            const inputsToSave = gradConfig.inputsToSave || [];
            const outputsToSave = gradConfig.outputsToSave || [];
            // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs
            // specified in inputsToSave will be saved.
            let inputTensorsToSave;
            if (gradConfig.saveAllInputs) {
                _util.assert(Array.isArray(inputs), ()=>"saveAllInputs is true, expected inputs to be an array.");
                inputTensorsToSave = Object.keys(inputs).map((key)=>inputs[key]);
            } else inputTensorsToSave = inputsToSave.map((inputName)=>inputs[inputName]);
            const outputTensorsToSave = outputs.filter((_, i)=>outputsToSave[i]);
            return inputTensorsToSave.concat(outputTensorsToSave);
        }
        // We return an empty list rather than throw an error because the kernel we
        // are looking up may not actually be relevant to backproping through the
        // overall function
        //
        // See 'does not error if irrelevant (pruned) ops are missing grads' test
        // in gradients_test.ts for an example.
        return [];
    }
    /**
     * Internal method used by public APIs for tensor creation. Makes a new
     * tensor with the provided shape, dtype and values. It always
     * creates a new data id and writes the values to the underlying backend.
     */ makeTensor(values, shape, dtype, backend) {
        if (values == null) throw new Error("Values passed to engine.makeTensor() are null");
        dtype = dtype || "float32";
        backend = backend || this.backend;
        let backendVals = values;
        if (dtype === "string" && _util.isString(values[0])) backendVals = values.map((d)=>_util.encodeString(d));
        const dataId = backend.write(backendVals, shape, dtype);
        const t = new (0, _tensor.Tensor)(shape, dtype, dataId, this.nextTensorId());
        this.trackTensor(t, backend);
        // Count bytes for string tensors.
        if (dtype === "string") {
            const info = this.state.tensorInfo.get(dataId);
            const newBytes = (0, _util.bytesFromStringArray)(backendVals);
            this.state.numBytes += newBytes - info.bytes;
            info.bytes = newBytes;
        }
        return t;
    }
    /**
     * Internal method used by backends. Makes a new tensor
     * that is a wrapper around an existing data id. It doesn't create
     * a new data id, only increments the ref count used in memory tracking.
     * @deprecated
     */ makeTensorFromDataId(dataId, shape, dtype, backend) {
        dtype = dtype || "float32";
        const tensorInfo = {
            dataId,
            shape,
            dtype
        };
        return this.makeTensorFromTensorInfo(tensorInfo, backend);
    }
    /**
     * Internal method used by backends. Makes a new tensor that is a wrapper
     * around an existing data id in TensorInfo. It doesn't create a new data id,
     * only increments the ref count used in memory tracking.
     */ makeTensorFromTensorInfo(tensorInfo, backend) {
        const { dataId, shape, dtype } = tensorInfo;
        const t = new (0, _tensor.Tensor)(shape, dtype, dataId, this.nextTensorId());
        this.trackTensor(t, backend);
        return t;
    }
    makeVariable(initialValue, trainable = true, name, dtype) {
        name = name || this.nextVariableId().toString();
        if (dtype != null && dtype !== initialValue.dtype) initialValue = initialValue.cast(dtype);
        const v = new (0, _tensor.Variable)(initialValue, trainable, name, this.nextTensorId());
        if (this.state.registeredVariables[v.name] != null) throw new Error(`Variable with name ${v.name} was already registered`);
        this.state.registeredVariables[v.name] = v;
        this.incRef(v, this.backend);
        return v;
    }
    trackTensor(a, backend) {
        this.state.numTensors++;
        if (a.dtype === "string") this.state.numStringTensors++;
        // Bytes for complex numbers are counted by their components. Bytes for
        // string tensors are counted when writing values.
        let bytes = 0;
        if (a.dtype !== "complex64" && a.dtype !== "string") bytes = a.size * _util.bytesPerElement(a.dtype);
        this.state.numBytes += bytes;
        if (!this.state.tensorInfo.has(a.dataId)) {
            this.state.numDataBuffers++;
            this.state.tensorInfo.set(a.dataId, {
                backend: backend || this.backend,
                dtype: a.dtype,
                shape: a.shape,
                bytes
            });
        }
        if (!(a instanceof (0, _tensor.Variable))) this.track(a);
    }
    // Track the tensor by dataId and increase the refCount for the dataId in the
    // backend.
    // TODO(pyu10055): This is currently used by makeVariable method, to increase
    // refCount on the backend for the dataId. It can potentially be replaced with
    // Identity op indead of calling backend directly.
    incRef(a, backend) {
        this.trackTensor(a, backend);
        this.backend.incRef(a.dataId);
    }
    removeDataId(dataId, backend) {
        if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend) {
            this.state.tensorInfo.delete(dataId);
            this.state.numDataBuffers--;
        }
    }
    disposeTensor(a) {
        if (!this.state.tensorInfo.has(a.dataId)) return;
        const info = this.state.tensorInfo.get(a.dataId);
        this.state.numTensors--;
        if (a.dtype === "string") {
            this.state.numStringTensors--;
            this.state.numBytes -= info.bytes;
        }
        // Don't count bytes for complex numbers as they are counted by their
        // components.
        if (a.dtype !== "complex64" && a.dtype !== "string") {
            const bytes = a.size * _util.bytesPerElement(a.dtype);
            this.state.numBytes -= bytes;
        }
        // Remove the reference to dataId if backend dispose the data successfully
        if (info.backend.disposeData(a.dataId)) this.removeDataId(a.dataId, info.backend);
    // TODO(nsthorat): Construct an error and save the stack trace for
    // debugging when in debug mode. Creating a stack trace is too expensive
    // to do unconditionally.
    }
    disposeVariables() {
        for(const varName in this.state.registeredVariables){
            const v = this.state.registeredVariables[varName];
            this.disposeVariable(v);
        }
    }
    disposeVariable(v) {
        this.disposeTensor(v);
        if (this.state.registeredVariables[v.name] != null) delete this.state.registeredVariables[v.name];
    }
    memory() {
        const info = this.backend.memory();
        info.numTensors = this.state.numTensors;
        info.numDataBuffers = this.state.numDataBuffers;
        info.numBytes = this.state.numBytes;
        if (this.state.numStringTensors > 0) {
            info.unreliable = true;
            if (info.reasons == null) info.reasons = [];
            info.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)");
        }
        return info;
    }
    async profile(query) {
        this.state.profiling = true;
        const startBytes = this.state.numBytes;
        const startNumTensors = this.state.numTensors;
        this.state.activeProfile.kernels = [];
        this.state.activeProfile.result = await query();
        this.state.profiling = false;
        this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((d)=>d.totalBytesSnapshot));
        this.state.activeProfile.newBytes = this.state.numBytes - startBytes;
        this.state.activeProfile.newTensors = this.state.numTensors - startNumTensors;
        for (const kernel of this.state.activeProfile.kernels){
            kernel.kernelTimeMs = await kernel.kernelTimeMs;
            kernel.extraInfo = await kernel.extraInfo;
        }
        return this.state.activeProfile;
    }
    isTapeOn() {
        return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
    }
    addTapeNode(kernelName, inputs, outputs, gradientsFunc, saved, attrs) {
        const tapeNode = {
            id: this.state.nextTapeNodeId++,
            kernelName,
            inputs,
            outputs,
            saved
        };
        const gradConfig = (0, _kernelRegistry.getGradient)(kernelName);
        if (gradConfig != null) gradientsFunc = gradConfig.gradFunc;
        if (gradientsFunc != null) tapeNode.gradient = (dys)=>{
            // TODO(smilkov): To optimize back-prop, pass dys that are not used in
            // the backprop graph to the user as null instead of zeros
            dys = dys.map((dy, i)=>{
                if (dy == null) {
                    const output = outputs[i];
                    const vals = _util.makeZerosTypedArray(output.size, output.dtype);
                    return this.makeTensor(vals, output.shape, output.dtype);
                }
                return dy;
            });
            // Grad functions of ops with single outputs expect a dy, while ops
            // with multiple outputs expect dys (array of dy).
            return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs);
        };
        this.state.activeTape.push(tapeNode);
    }
    keep(result) {
        result.kept = true;
        return result;
    }
    startTape() {
        if (this.state.gradientDepth === 0) this.state.activeTape = [];
        this.state.gradientDepth++;
    }
    endTape() {
        this.state.gradientDepth--;
    }
    /**
     * Start a scope. Use this with endScope() to achieve the same functionality
     * as scope() without the need for a function closure.
     */ startScope(name) {
        const scopeInfo = {
            track: [],
            name: "unnamed scope",
            id: this.state.nextScopeId++
        };
        if (name) scopeInfo.name = name;
        this.state.scopeStack.push(scopeInfo);
        this.state.activeScope = scopeInfo;
    }
    /**
     * End a scope. Use this with startScope() to achieve the same functionality
     * as scope() without the need for a function closure.
     */ endScope(result) {
        const tensorsToTrackInParent = (0, _tensorUtil.getTensorsInContainer)(result);
        const tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map((t)=>t.id));
        // Dispose the arrays tracked in this scope.
        for(let i = 0; i < this.state.activeScope.track.length; i++){
            const tensor = this.state.activeScope.track[i];
            if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) tensor.dispose();
        }
        const oldScope = this.state.scopeStack.pop();
        this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1];
        // Track the current result in the parent scope.
        tensorsToTrackInParent.forEach((tensor)=>{
            // Only track the tensor if was allocated in the inner scope and is not
            // globally kept.
            if (!tensor.kept && tensor.scopeId === oldScope.id) this.track(tensor);
        });
    }
    /**
     * Returns gradients of `f` with respect to each of the `xs`. The gradients
     * returned are of the same length as `xs`, but some might be null if `f`
     * was not a function of that `x`. It also takes optional dy to multiply the
     * gradient, which defaults to `1`.
     */ gradients(f, xs, dy, allowNoGradients = false) {
        _util.assert(xs.length > 0, ()=>"gradients() received an empty list of xs.");
        if (dy != null && dy.dtype !== "float32") throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);
        const y = this.scopedRun(()=>this.startTape(), ()=>this.endTape(), ()=>this.tidy("forward", f));
        _util.assert(y instanceof (0, _tensor.Tensor), ()=>"The result y returned by f() must be a tensor.");
        // Filter out the nodes that don't connect x => y.
        const filteredTape = (0, _tape.getFilteredNodesXToY)(this.state.activeTape, xs, y);
        if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
        return this.tidy("backward", ()=>{
            const accumulatedGradientMap = {};
            accumulatedGradientMap[y.id] = dy == null ? ones(y.shape) : dy;
            // Backprop gradients through the filtered nodes.
            (0, _tape.backpropagateGradients)(accumulatedGradientMap, filteredTape, // Pass the tidy function to avoid circular dep with `tape.ts`.
            (f)=>this.tidy(f), // Pass an add function to avoide a circular dep with `tape.ts`.
            add);
            const grads = xs.map((x)=>accumulatedGradientMap[x.id]);
            if (this.state.gradientDepth === 0) {
                // This means that we are not computing higher-order gradients
                // and can clean up the tape.
                this.state.activeTape.forEach((node)=>{
                    for (const tensor of node.saved)tensor.dispose();
                });
                this.state.activeTape = null;
            }
            return {
                value: y,
                grads
            };
        });
    }
    customGrad(f) {
        _util.assert(_util.isFunction(f), ()=>"The f passed in customGrad(f) must be a function.");
        return (...inputs)=>{
            _util.assert(inputs.every((t)=>t instanceof (0, _tensor.Tensor)), ()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");
            let res;
            const inputMap = {};
            inputs.forEach((input, i)=>{
                inputMap[i] = input;
            });
            const forwardFunc = (_, save)=>{
                res = f(...[
                    ...inputs,
                    save
                ]);
                _util.assert(res.value instanceof (0, _tensor.Tensor), ()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor");
                _util.assert(_util.isFunction(res.gradFunc), ()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.");
                return res.value;
            };
            const backwardsFunc = (dy, saved)=>{
                const gradRes = res.gradFunc(dy, saved);
                const grads = Array.isArray(gradRes) ? gradRes : [
                    gradRes
                ];
                _util.assert(grads.length === inputs.length, ()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).");
                _util.assert(grads.every((t)=>t instanceof (0, _tensor.Tensor)), ()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
                const gradMap = {};
                grads.forEach((grad, i)=>{
                    gradMap[i] = ()=>grad;
                });
                return gradMap;
            };
            return this.runKernelFunc({
                forwardFunc,
                backwardsFunc,
                inputs: inputMap
            });
        };
    }
    readSync(dataId) {
        // Route the read to the correct backend.
        const info = this.state.tensorInfo.get(dataId);
        return info.backend.readSync(dataId);
    }
    read(dataId) {
        // Route the read to the correct backend.
        const info = this.state.tensorInfo.get(dataId);
        return info.backend.read(dataId);
    }
    readToGPU(dataId, options) {
        // Route the read to the correct backend.
        const info = this.state.tensorInfo.get(dataId);
        return info.backend.readToGPU(dataId, options);
    }
    async time(query) {
        const start = (0, _util.now)();
        const timingInfo = await this.backend.time(query);
        timingInfo.wallMs = (0, _util.now)() - start;
        return timingInfo;
    }
    /**
     * Tracks a Tensor in the current scope to be automatically cleaned up
     * when the current scope ends, and returns the value.
     *
     * @param result The Tensor to track in the current scope.
     */ track(result) {
        if (this.state.activeScope != null) {
            result.scopeId = this.state.activeScope.id;
            this.state.activeScope.track.push(result);
        }
        return result;
    }
    get registeredVariables() {
        return this.state.registeredVariables;
    }
    /**
     * Resets the engine state. Removes all backends but does not remove
     * registered backend factories.
     */ reset() {
        // Make any pending promise obsolete.
        this.pendingBackendInitId++;
        this.state.dispose();
        this.ENV.reset();
        this.state = new EngineState();
        for(const backendName in this.registry){
            this.disposeRegisteredKernels(backendName);
            this.registry[backendName].dispose();
            delete this.registry[backendName];
        }
        this.backendName = null;
        this.backendInstance = null;
        this.pendingBackendInit = null;
    }
}
Engine.nextTensorId = 0;
Engine.nextVariableId = 0;
function ones(shape) {
    const values = (0, _util.makeOnesTypedArray)((0, _util.sizeFromShape)(shape), "float32");
    return ENGINE.makeTensor(values, shape, "float32");
}
function getOrMakeEngine() {
    const ns = (0, _globalUtil.getGlobalNamespace)();
    if (ns._tfengine == null) {
        const environment = new (0, _environment.Environment)(ns);
        ns._tfengine = new Engine(environment);
    }
    (0, _environment.setEnvironmentGlobal)(ns._tfengine.ENV);
    // Tell the current tensor interface that the global engine is responsible
    // for tracking.
    (0, _tensor.setTensorTracker)(()=>ns._tfengine);
    return ns._tfengine;
}
const ENGINE = getOrMakeEngine();
function add(a, b) {
    // We duplicate Add here to avoid a circular dependency with add.ts.
    const inputs = {
        a,
        b
    };
    return ENGINE.runKernel((0, _kernelNames.Add), inputs);
}

},{"./backends/backend":"dL2i3","./environment":"dXmHm","./global_util":"82SAe","./kernel_names":"hniYO","./kernel_registry":"aG7g9","./log":"5xR9j","./profiler":"aLKDS","./tape":"cUi5z","./tensor":"cJNoh","./tensor_util":"8jzJG","./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dL2i3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "EPSILON_FLOAT32", ()=>EPSILON_FLOAT32);
parcelHelpers.export(exports, "EPSILON_FLOAT16", ()=>EPSILON_FLOAT16);
/** Convenient class for storing tensor-related data. */ parcelHelpers.export(exports, "DataStorage", ()=>DataStorage);
/**
 * The interface that defines the kernels that should be implemented when
 * adding a new backend. New backends don't need to implement every one of the
 * methods, this can be done gradually (throw an error for unimplemented
 * methods).
 */ parcelHelpers.export(exports, "KernelBackend", ()=>KernelBackend);
const EPSILON_FLOAT32 = 1e-7;
const EPSILON_FLOAT16 = 1e-4;
class DataStorage {
    constructor(backend, dataMover){
        this.backend = backend;
        this.dataMover = dataMover;
        this.data = new WeakMap();
        this.dataIdsCount = 0;
    }
    get(dataId) {
        if (!this.data.has(dataId)) this.dataMover.moveData(this.backend, dataId);
        return this.data.get(dataId);
    }
    set(dataId, value) {
        this.dataIdsCount++;
        this.data.set(dataId, value);
    }
    has(dataId) {
        return this.data.has(dataId);
    }
    delete(dataId) {
        this.dataIdsCount--;
        return this.data.delete(dataId);
    }
    numDataIds() {
        return this.dataIdsCount;
    }
}
class KernelBackend {
    refCount(dataId) {
        return notYetImplemented("refCount");
    }
    incRef(dataId) {
        return notYetImplemented("incRef");
    }
    timerAvailable() {
        return true;
    }
    time(f) {
        return notYetImplemented("time");
    }
    read(dataId) {
        return notYetImplemented("read");
    }
    readSync(dataId) {
        return notYetImplemented("readSync");
    }
    readToGPU(dataId, options) {
        return notYetImplemented("readToGPU");
    }
    numDataIds() {
        return notYetImplemented("numDataIds");
    }
    disposeData(dataId, force) {
        return notYetImplemented("disposeData");
    }
    write(values, shape, dtype) {
        return notYetImplemented("write");
    }
    move(dataId, values, shape, dtype, refCount) {
        return notYetImplemented("move");
    }
    createTensorFromGPUData(values, shape, dtype) {
        return notYetImplemented("createTensorFromGPUData");
    }
    memory() {
        return notYetImplemented("memory");
    }
    /** Returns the highest precision for floats in bits (e.g. 16 or 32) */ floatPrecision() {
        return notYetImplemented("floatPrecision");
    }
    /** Returns the smallest representable number.  */ epsilon() {
        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
    }
    dispose() {
        return notYetImplemented("dispose");
    }
}
function notYetImplemented(kernelName) {
    throw new Error(`'${kernelName}' not yet implemented or not found in the registry. ` + `This kernel may not be supported by the tfjs backend you have chosen`);
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dXmHm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * The environment contains evaluated flags as well as the registered platform.
 * This is always used as a global singleton and can be retrieved with
 * `tf.env()`.
 *
 * @doc {heading: 'Environment'}
 */ parcelHelpers.export(exports, "Environment", ()=>Environment);
parcelHelpers.export(exports, "getQueryParams", ()=>getQueryParams);
/**
 * Returns the current environment (a global singleton).
 *
 * The environment object contains the evaluated feature values as well as the
 * active platform.
 *
 * @doc {heading: 'Environment'}
 */ parcelHelpers.export(exports, "env", ()=>env);
parcelHelpers.export(exports, "ENV", ()=>ENV);
parcelHelpers.export(exports, "setEnvironmentGlobal", ()=>setEnvironmentGlobal);
var _utilBase = require("./util_base");
// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.
const TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
class Environment {
    // tslint:disable-next-line: no-any
    constructor(global){
        this.global = global;
        this.flags = {};
        this.flagRegistry = {};
        this.urlFlags = {};
        // Jasmine spies on this in 'environment_test.ts'
        this.getQueryParams = getQueryParams;
        this.populateURLFlags();
    }
    setPlatform(platformName, platform) {
        if (this.platform != null) {
            if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) console.warn(`Platform ${this.platformName} has already been set. ` + `Overwriting the platform with ${platformName}.`);
        }
        this.platformName = platformName;
        this.platform = platform;
    }
    registerFlag(flagName, evaluationFn, setHook) {
        this.flagRegistry[flagName] = {
            evaluationFn,
            setHook
        };
        // Override the flag value from the URL. This has to happen here because
        // the environment is initialized before flags get registered.
        if (this.urlFlags[flagName] != null) {
            const flagValue = this.urlFlags[flagName];
            if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) console.warn(`Setting feature override from URL ${flagName}: ${flagValue}.`);
            this.set(flagName, flagValue);
        }
    }
    async getAsync(flagName) {
        if (flagName in this.flags) return this.flags[flagName];
        this.flags[flagName] = await this.evaluateFlag(flagName);
        return this.flags[flagName];
    }
    get(flagName) {
        if (flagName in this.flags) return this.flags[flagName];
        const flagValue = this.evaluateFlag(flagName);
        if ((0, _utilBase.isPromise)(flagValue)) throw new Error(`Flag ${flagName} cannot be synchronously evaluated. ` + `Please use getAsync() instead.`);
        this.flags[flagName] = flagValue;
        return this.flags[flagName];
    }
    getNumber(flagName) {
        return this.get(flagName);
    }
    getBool(flagName) {
        return this.get(flagName);
    }
    getString(flagName) {
        return this.get(flagName);
    }
    getFlags() {
        return this.flags;
    }
    // For backwards compatibility.
    get features() {
        return this.flags;
    }
    set(flagName, value) {
        if (this.flagRegistry[flagName] == null) throw new Error(`Cannot set flag ${flagName} as it has not been registered.`);
        this.flags[flagName] = value;
        if (this.flagRegistry[flagName].setHook != null) this.flagRegistry[flagName].setHook(value);
    }
    evaluateFlag(flagName) {
        if (this.flagRegistry[flagName] == null) throw new Error(`Cannot evaluate flag '${flagName}': no evaluation function found.`);
        return this.flagRegistry[flagName].evaluationFn();
    }
    setFlags(flags) {
        this.flags = Object.assign({}, flags);
    }
    reset() {
        this.flags = {};
        this.urlFlags = {};
        this.populateURLFlags();
    }
    populateURLFlags() {
        if (typeof this.global === "undefined" || typeof this.global.location === "undefined" || typeof this.global.location.search === "undefined") return;
        const urlParams = this.getQueryParams(this.global.location.search);
        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
            const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(",");
            keyValues.forEach((keyValue)=>{
                const [key, value] = keyValue.split(":");
                this.urlFlags[key] = parseValue(key, value);
            });
        }
    }
}
function getQueryParams(queryString) {
    const params = {};
    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t)=>{
        decodeParam(params, t[0], t[1]);
        return t.join("=");
    });
    return params;
}
function decodeParam(params, name, value) {
    params[decodeURIComponent(name)] = decodeURIComponent(value || "");
}
function parseValue(flagName, value) {
    const lowerCaseValue = value.toLowerCase();
    if (lowerCaseValue === "true" || lowerCaseValue === "false") return lowerCaseValue === "true";
    else if (`${+lowerCaseValue}` === lowerCaseValue) return +lowerCaseValue;
    else return value;
}
function env() {
    return ENV;
}
let ENV = null;
function setEnvironmentGlobal(environment) {
    ENV = environment;
}

},{"./util_base":"4Vo4a","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4Vo4a":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Shuffles the array in-place using Fisher-Yates algorithm.
 *
 * ```js
 * const a = [1, 2, 3, 4, 5];
 * tf.util.shuffle(a);
 * console.log(a);
 * ```
 *
 * @param array The array to shuffle in-place.
 *
 * @doc {heading: 'Util', namespace: 'util'}
 */ // tslint:disable-next-line:no-any
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "shuffle", ()=>shuffle);
/**
 * Shuffles two arrays in-place the same way using Fisher-Yates algorithm.
 *
 * ```js
 * const a = [1,2,3,4,5];
 * const b = [11,22,33,44,55];
 * tf.util.shuffleCombo(a, b);
 * console.log(a, b);
 * ```
 *
 * @param array The first array to shuffle in-place.
 * @param array2 The second array to shuffle in-place with the same permutation
 *     as the first array.
 *
 * @doc {heading: 'Util', namespace: 'util'}
 */ parcelHelpers.export(exports, "shuffleCombo", ()=>shuffleCombo);
/** Clamps a value to a specified range. */ parcelHelpers.export(exports, "clamp", ()=>clamp);
parcelHelpers.export(exports, "nearestLargerEven", ()=>nearestLargerEven);
parcelHelpers.export(exports, "swap", ()=>swap);
parcelHelpers.export(exports, "sum", ()=>sum);
/**
 * Returns a sample from a uniform [a, b) distribution.
 *
 * @param a The minimum support (inclusive).
 * @param b The maximum support (exclusive).
 * @return A pseudorandom number on the half-open interval [a,b).
 */ parcelHelpers.export(exports, "randUniform", ()=>randUniform);
/** Returns the squared Euclidean distance between two vectors. */ parcelHelpers.export(exports, "distSquared", ()=>distSquared);
/**
 * Asserts that the expression is true. Otherwise throws an error with the
 * provided message.
 *
 * ```js
 * const x = 2;
 * tf.util.assert(x === 2, 'x is not 2');
 * ```
 *
 * @param expr The expression to assert (as a boolean).
 * @param msg A function that returns the message to report when throwing an
 *     error. We use a function for performance reasons.
 *
 * @doc {heading: 'Util', namespace: 'util'}
 */ parcelHelpers.export(exports, "assert", ()=>assert);
parcelHelpers.export(exports, "assertShapesMatch", ()=>assertShapesMatch);
parcelHelpers.export(exports, "assertNonNull", ()=>assertNonNull);
/**
 * Returns the size (number of elements) of the tensor given its shape.
 *
 * ```js
 * const shape = [3, 4, 2];
 * const size = tf.util.sizeFromShape(shape);
 * console.log(size);
 * ```
 *
 * @doc {heading: 'Util', namespace: 'util'}
 */ parcelHelpers.export(exports, "sizeFromShape", ()=>sizeFromShape);
parcelHelpers.export(exports, "isScalarShape", ()=>isScalarShape);
parcelHelpers.export(exports, "arraysEqualWithNull", ()=>arraysEqualWithNull);
parcelHelpers.export(exports, "arraysEqual", ()=>arraysEqual);
parcelHelpers.export(exports, "isInt", ()=>isInt);
parcelHelpers.export(exports, "tanh", ()=>tanh);
parcelHelpers.export(exports, "sizeToSquarishShape", ()=>sizeToSquarishShape);
/**
 * Creates a new array with randomized indices to a given quantity.
 *
 * ```js
 * const randomTen = tf.util.createShuffledIndices(10);
 * console.log(randomTen);
 * ```
 *
 * @param number Quantity of how many shuffled indices to create.
 *
 * @doc {heading: 'Util', namespace: 'util'}
 */ parcelHelpers.export(exports, "createShuffledIndices", ()=>createShuffledIndices);
parcelHelpers.export(exports, "rightPad", ()=>rightPad);
parcelHelpers.export(exports, "repeatedTry", ()=>repeatedTry);
/**
 * Given the full size of the array and a shape that may contain -1 as the
 * implicit dimension, returns the inferred shape where -1 is replaced.
 * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].
 *
 * @param shape The shape, which may contain -1 in some dimension.
 * @param size The full size (number of elements) of the array.
 * @return The inferred shape where -1 is replaced with the inferred size.
 */ parcelHelpers.export(exports, "inferFromImplicitShape", ()=>inferFromImplicitShape);
parcelHelpers.export(exports, "parseAxisParam", ()=>parseAxisParam);
/** Reduces the shape by removing all dimensions of shape 1. */ parcelHelpers.export(exports, "squeezeShape", ()=>squeezeShape);
parcelHelpers.export(exports, "getTypedArrayFromDType", ()=>getTypedArrayFromDType);
parcelHelpers.export(exports, "getArrayFromDType", ()=>getArrayFromDType);
parcelHelpers.export(exports, "checkConversionForErrors", ()=>checkConversionForErrors);
/** Returns true if the dtype is valid. */ parcelHelpers.export(exports, "isValidDtype", ()=>isValidDtype);
/**
 * Returns true if the new type can't encode the old type without loss of
 * precision.
 */ parcelHelpers.export(exports, "hasEncodingLoss", ()=>hasEncodingLoss);
parcelHelpers.export(exports, "bytesPerElement", ()=>bytesPerElement);
/**
 * Returns the approximate number of bytes allocated in the string array - 2
 * bytes per character. Computing the exact bytes for a native string in JS
 * is not possible since it depends on the encoding of the html page that
 * serves the website.
 */ parcelHelpers.export(exports, "bytesFromStringArray", ()=>bytesFromStringArray);
/** Returns true if the value is a string. */ parcelHelpers.export(exports, "isString", ()=>isString);
parcelHelpers.export(exports, "isBoolean", ()=>isBoolean);
parcelHelpers.export(exports, "isNumber", ()=>isNumber);
parcelHelpers.export(exports, "inferDtype", ()=>inferDtype);
parcelHelpers.export(exports, "isFunction", ()=>isFunction);
parcelHelpers.export(exports, "nearestDivisor", ()=>nearestDivisor);
parcelHelpers.export(exports, "computeStrides", ()=>computeStrides);
// Provide a nested array of TypedArray in given shape.
parcelHelpers.export(exports, "toNestedArray", ()=>toNestedArray);
parcelHelpers.export(exports, "convertBackendValuesAndArrayBuffer", ()=>convertBackendValuesAndArrayBuffer);
parcelHelpers.export(exports, "makeOnesTypedArray", ()=>makeOnesTypedArray);
parcelHelpers.export(exports, "makeZerosTypedArray", ()=>makeZerosTypedArray);
/**
 * Make nested `TypedArray` filled with zeros.
 * @param shape The shape information for the nested array.
 * @param dtype dtype of the array element.
 */ parcelHelpers.export(exports, "makeZerosNestedTypedArray", ()=>makeZerosNestedTypedArray);
parcelHelpers.export(exports, "assertNonNegativeIntegerDimensions", ()=>assertNonNegativeIntegerDimensions);
/**
 * Computes flat index for a given location (multidimentionsal index) in a
 * Tensor/multidimensional array.
 *
 * @param locs Location in the tensor.
 * @param rank Rank of the tensor.
 * @param strides Tensor strides.
 */ parcelHelpers.export(exports, "locToIndex", ()=>locToIndex);
/**
 * Computes the location (multidimensional index) in a
 * tensor/multidimentional array for a given flat index.
 *
 * @param index Index in flat array.
 * @param rank Rank of tensor.
 * @param strides Strides of tensor.
 */ parcelHelpers.export(exports, "indexToLoc", ()=>indexToLoc);
/**
 * This method asserts whether an object is a Promise instance.
 * @param object
 */ // tslint:disable-next-line: no-any
parcelHelpers.export(exports, "isPromise", ()=>isPromise);
function shuffle(array) {
    let counter = array.length;
    let index = 0;
    // While there are elements in the array
    while(counter > 0){
        // Pick a random index
        index = Math.random() * counter | 0;
        // Decrease counter by 1
        counter--;
        // And swap the last element with it
        swap(array, counter, index);
    }
}
function shuffleCombo(// tslint:disable-next-line:no-any
array, // tslint:disable-next-line:no-any
array2) {
    if (array.length !== array2.length) throw new Error(`Array sizes must match to be shuffled together ` + `First array length was ${array.length}` + `Second array length was ${array2.length}`);
    let counter = array.length;
    let index = 0;
    // While there are elements in the array
    while(counter > 0){
        // Pick a random index
        index = Math.random() * counter | 0;
        // Decrease counter by 1
        counter--;
        // And swap the last element of each array with it
        swap(array, counter, index);
        swap(array2, counter, index);
    }
}
function clamp(min, x, max) {
    return Math.max(min, Math.min(x, max));
}
function nearestLargerEven(val) {
    return val % 2 === 0 ? val : val + 1;
}
function swap(object, left, right) {
    const temp = object[left];
    object[left] = object[right];
    object[right] = temp;
}
function sum(arr) {
    let sum = 0;
    for(let i = 0; i < arr.length; i++)sum += arr[i];
    return sum;
}
function randUniform(a, b) {
    const r = Math.random();
    return b * r + (1 - r) * a;
}
function distSquared(a, b) {
    let result = 0;
    for(let i = 0; i < a.length; i++){
        const diff = Number(a[i]) - Number(b[i]);
        result += diff * diff;
    }
    return result;
}
function assert(expr, msg) {
    if (!expr) throw new Error(typeof msg === "string" ? msg : msg());
}
function assertShapesMatch(shapeA, shapeB, errorMessagePrefix = "") {
    assert(arraysEqual(shapeA, shapeB), ()=>errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
}
function assertNonNull(a) {
    assert(a != null, ()=>`The input to the tensor constructor must be a non-null value.`);
}
function sizeFromShape(shape) {
    if (shape.length === 0) // Scalar.
    return 1;
    let size = shape[0];
    for(let i = 1; i < shape.length; i++)size *= shape[i];
    return size;
}
function isScalarShape(shape) {
    return shape.length === 0;
}
function arraysEqualWithNull(n1, n2) {
    if (n1 === n2) return true;
    if (n1 == null || n2 == null) return false;
    if (n1.length !== n2.length) return false;
    for(let i = 0; i < n1.length; i++){
        if (n1[i] !== null && n2[i] !== null && n1[i] !== n2[i]) return false;
    }
    return true;
}
function arraysEqual(n1, n2) {
    if (n1 === n2) return true;
    if (n1 == null || n2 == null) return false;
    if (n1.length !== n2.length) return false;
    for(let i = 0; i < n1.length; i++){
        if (n1[i] !== n2[i]) return false;
    }
    return true;
}
function isInt(a) {
    return a % 1 === 0;
}
function tanh(x) {
    // tslint:disable-next-line:no-any
    if (Math.tanh != null) // tslint:disable-next-line:no-any
    return Math.tanh(x);
    if (x === Infinity) return 1;
    else if (x === -Infinity) return -1;
    else {
        const e2x = Math.exp(2 * x);
        return (e2x - 1) / (e2x + 1);
    }
}
function sizeToSquarishShape(size) {
    const width = Math.ceil(Math.sqrt(size));
    return [
        width,
        Math.ceil(size / width)
    ];
}
function createShuffledIndices(n) {
    const shuffledIndices = new Uint32Array(n);
    for(let i = 0; i < n; ++i)shuffledIndices[i] = i;
    shuffle(shuffledIndices);
    return shuffledIndices;
}
function rightPad(a, size) {
    if (size <= a.length) return a;
    return a + " ".repeat(size - a.length);
}
function repeatedTry(checkFn, delayFn = (counter)=>0, maxCounter, scheduleFn) {
    return new Promise((resolve, reject)=>{
        let tryCount = 0;
        const tryFn = ()=>{
            if (checkFn()) {
                resolve();
                return;
            }
            tryCount++;
            const nextBackoff = delayFn(tryCount);
            if (maxCounter != null && tryCount >= maxCounter) {
                reject();
                return;
            }
            if (scheduleFn != null) scheduleFn(tryFn, nextBackoff);
            else // google3 does not allow assigning another variable to setTimeout.
            // Don't refactor this so scheduleFn has a default value of setTimeout.
            setTimeout(tryFn, nextBackoff);
        };
        tryFn();
    });
}
function inferFromImplicitShape(shape, size) {
    let shapeProd = 1;
    let implicitIdx = -1;
    for(let i = 0; i < shape.length; ++i){
        if (shape[i] >= 0) shapeProd *= shape[i];
        else if (shape[i] === -1) {
            if (implicitIdx !== -1) throw Error(`Shapes can only have 1 implicit size. ` + `Found -1 at dim ${implicitIdx} and dim ${i}`);
            implicitIdx = i;
        } else if (shape[i] < 0) throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);
    }
    if (implicitIdx === -1) {
        if (size > 0 && size !== shapeProd) throw Error(`Size(${size}) must match the product of shape ${shape}`);
        return shape;
    }
    if (shapeProd === 0) throw Error(`Cannot infer the missing size in [${shape}] when ` + `there are 0 elements`);
    if (size % shapeProd !== 0) throw Error(`The implicit shape can't be a fractional number. ` + `Got ${size} / ${shapeProd}`);
    const newShape = shape.slice();
    newShape[implicitIdx] = size / shapeProd;
    return newShape;
}
function parseAxisParam(axis, shape) {
    const rank = shape.length;
    // Normalize input
    axis = axis == null ? shape.map((s, i)=>i) : [].concat(axis);
    // Check for valid range
    assert(axis.every((ax)=>ax >= -rank && ax < rank), ()=>`All values in axis param must be in range [-${rank}, ${rank}) but ` + `got axis ${axis}`);
    // Check for only integers
    assert(axis.every((ax)=>isInt(ax)), ()=>`All values in axis param must be integers but ` + `got axis ${axis}`);
    // Handle negative axis.
    return axis.map((a)=>a < 0 ? rank + a : a);
}
function squeezeShape(shape, axis) {
    const newShape = [];
    const keptDims = [];
    const isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0;
    const axes = axis == null || isEmptyArray ? null : parseAxisParam(axis, shape).sort();
    let j = 0;
    for(let i = 0; i < shape.length; ++i){
        if (axes != null) {
            if (axes[j] === i && shape[i] !== 1) throw new Error(`Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);
            if ((axes[j] == null || axes[j] > i) && shape[i] === 1) {
                newShape.push(shape[i]);
                keptDims.push(i);
            }
            if (axes[j] <= i) j++;
        }
        if (shape[i] !== 1) {
            newShape.push(shape[i]);
            keptDims.push(i);
        }
    }
    return {
        newShape,
        keptDims
    };
}
function getTypedArrayFromDType(dtype, size) {
    return getArrayFromDType(dtype, size);
}
function getArrayFromDType(dtype, size) {
    let values = null;
    if (dtype == null || dtype === "float32") values = new Float32Array(size);
    else if (dtype === "int32") values = new Int32Array(size);
    else if (dtype === "bool") values = new Uint8Array(size);
    else if (dtype === "string") values = new Array(size);
    else throw new Error(`Unknown data type ${dtype}`);
    return values;
}
function checkConversionForErrors(vals, dtype) {
    for(let i = 0; i < vals.length; i++){
        const num = vals[i];
        if (isNaN(num) || !isFinite(num)) throw Error(`A tensor of type ${dtype} being uploaded contains ${num}.`);
    }
}
function isValidDtype(dtype) {
    return dtype === "bool" || dtype === "complex64" || dtype === "float32" || dtype === "int32" || dtype === "string";
}
function hasEncodingLoss(oldType, newType) {
    if (newType === "complex64") return false;
    if (newType === "float32" && oldType !== "complex64") return false;
    if (newType === "int32" && oldType !== "float32" && oldType !== "complex64") return false;
    if (newType === "bool" && oldType === "bool") return false;
    return true;
}
function bytesPerElement(dtype) {
    if (dtype === "float32" || dtype === "int32") return 4;
    else if (dtype === "complex64") return 8;
    else if (dtype === "bool") return 1;
    else throw new Error(`Unknown dtype ${dtype}`);
}
function bytesFromStringArray(arr) {
    if (arr == null) return 0;
    let bytes = 0;
    arr.forEach((x)=>bytes += x.length);
    return bytes;
}
function isString(value) {
    return typeof value === "string" || value instanceof String;
}
function isBoolean(value) {
    return typeof value === "boolean";
}
function isNumber(value) {
    return typeof value === "number";
}
function inferDtype(values) {
    if (Array.isArray(values)) return inferDtype(values[0]);
    if (values instanceof Float32Array) return "float32";
    else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) return "int32";
    else if (isNumber(values)) return "float32";
    else if (isString(values)) return "string";
    else if (isBoolean(values)) return "bool";
    return "float32";
}
function isFunction(f) {
    return !!(f && f.constructor && f.call && f.apply);
}
function nearestDivisor(size, start) {
    for(let i = start; i < size; ++i){
        if (size % i === 0) return i;
    }
    return size;
}
function computeStrides(shape) {
    const rank = shape.length;
    if (rank < 2) return [];
    // Last dimension has implicit stride of 1, thus having D-1 (instead of D)
    // strides.
    const strides = new Array(rank - 1);
    strides[rank - 2] = shape[rank - 1];
    for(let i = rank - 3; i >= 0; --i)strides[i] = strides[i + 1] * shape[i + 1];
    return strides;
}
function createNestedArray(offset, shape, a, isComplex = false) {
    const ret = new Array();
    if (shape.length === 1) {
        const d = shape[0] * (isComplex ? 2 : 1);
        for(let i = 0; i < d; i++)ret[i] = a[offset + i];
    } else {
        const d = shape[0];
        const rest = shape.slice(1);
        const len = rest.reduce((acc, c)=>acc * c) * (isComplex ? 2 : 1);
        for(let i = 0; i < d; i++)ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);
    }
    return ret;
}
function toNestedArray(shape, a, isComplex = false) {
    if (shape.length === 0) // Scalar type should return a single number.
    return a[0];
    const size = shape.reduce((acc, c)=>acc * c) * (isComplex ? 2 : 1);
    if (size === 0) // A tensor with shape zero should be turned into empty list.
    return [];
    if (size !== a.length) throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? " for a complex tensor" : ""}.`);
    return createNestedArray(0, shape, a, isComplex);
}
function convertBackendValuesAndArrayBuffer(data, dtype) {
    // If is type Uint8Array[], return it directly.
    if (Array.isArray(data)) return data;
    if (dtype === "float32") return data instanceof Float32Array ? data : new Float32Array(data);
    else if (dtype === "int32") return data instanceof Int32Array ? data : new Int32Array(data);
    else if (dtype === "bool" || dtype === "string") return Uint8Array.from(new Int32Array(data));
    else throw new Error(`Unknown dtype ${dtype}`);
}
function makeOnesTypedArray(size, dtype) {
    const array = makeZerosTypedArray(size, dtype);
    for(let i = 0; i < array.length; i++)array[i] = 1;
    return array;
}
function makeZerosTypedArray(size, dtype) {
    if (dtype == null || dtype === "float32" || dtype === "complex64") return new Float32Array(size);
    else if (dtype === "int32") return new Int32Array(size);
    else if (dtype === "bool") return new Uint8Array(size);
    else throw new Error(`Unknown data type ${dtype}`);
}
function makeZerosNestedTypedArray(shape, dtype) {
    const size = shape.reduce((prev, curr)=>prev * curr, 1);
    if (dtype == null || dtype === "float32") return toNestedArray(shape, new Float32Array(size));
    else if (dtype === "int32") return toNestedArray(shape, new Int32Array(size));
    else if (dtype === "bool") return toNestedArray(shape, new Uint8Array(size));
    else throw new Error(`Unknown data type ${dtype}`);
}
function assertNonNegativeIntegerDimensions(shape) {
    shape.forEach((dimSize)=>{
        assert(Number.isInteger(dimSize) && dimSize >= 0, ()=>`Tensor must have a shape comprised of positive integers but got ` + `shape [${shape}].`);
    });
}
function locToIndex(locs, rank, strides) {
    if (rank === 0) return 0;
    else if (rank === 1) return locs[0];
    let index = locs[locs.length - 1];
    for(let i = 0; i < locs.length - 1; ++i)index += strides[i] * locs[i];
    return index;
}
function indexToLoc(index, rank, strides) {
    if (rank === 0) return [];
    else if (rank === 1) return [
        index
    ];
    const locs = new Array(rank);
    for(let i = 0; i < locs.length - 1; ++i){
        locs[i] = Math.floor(index / strides[i]);
        index -= locs[i] * strides[i];
    }
    locs[locs.length - 1] = index;
    return locs;
}
function isPromise(object) {
    //  We chose to not use 'obj instanceOf Promise' for two reasons:
    //  1. It only reliably works for es6 Promise, not other Promise
    //  implementations.
    //  2. It doesn't work with framework that uses zone.js. zone.js monkey
    //  patch the async calls, so it is possible the obj (patched) is
    //  comparing to a pre-patched Promise.
    return object && object.then && typeof object.then === "function";
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"82SAe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Note that the identifier globalNameSpace is scoped to this module, but will
// always resolve to the same global object regardless of how the module is
// resolved.
// tslint:disable-next-line:no-any
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
// tslint:disable-next-line:no-any
parcelHelpers.export(exports, "getGlobalNamespace", ()=>getGlobalNamespace);
/**
 * Returns a globally accessible 'singleton' object.
 *
 * @param key the name of the object
 * @param init a function to initialize to initialize this object
 *             the first time it is fetched.
 */ parcelHelpers.export(exports, "getGlobal", ()=>getGlobal);
var global = arguments[3];
var process = require("585141946453e563");
let globalNameSpace;
function getGlobalNamespace() {
    if (globalNameSpace == null) {
        // tslint:disable-next-line:no-any
        let ns;
        if (typeof window !== "undefined") ns = window;
        else if (typeof global !== "undefined") ns = global;
        else if (typeof process !== "undefined") ns = process;
        else if (typeof self !== "undefined") ns = self;
        else throw new Error("Could not find a global object");
        globalNameSpace = ns;
    }
    return globalNameSpace;
}
// tslint:disable-next-line:no-any
function getGlobalMap() {
    const ns = getGlobalNamespace();
    if (ns._tfGlobals == null) ns._tfGlobals = new Map();
    return ns._tfGlobals;
}
function getGlobal(key, init) {
    const globalMap = getGlobalMap();
    if (globalMap.has(key)) return globalMap.get(key);
    else {
        const singleton = init();
        globalMap.set(key, singleton);
        return globalMap.get(key);
    }
}

},{"585141946453e563":"h0aBX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"h0aBX":[function(require,module,exports) {
// shim for using process in browser
var process = module.exports = {};
// cached from whatever global is present so that test runners that stub it
// don't break things.  But we need to wrap it in a try catch in case it is
// wrapped in strict mode code which doesn't define any globals.  It's inside a
// function because try/catches deoptimize in certain engines.
var cachedSetTimeout;
var cachedClearTimeout;
function defaultSetTimout() {
    throw new Error("setTimeout has not been defined");
}
function defaultClearTimeout() {
    throw new Error("clearTimeout has not been defined");
}
(function() {
    try {
        if (typeof setTimeout === "function") cachedSetTimeout = setTimeout;
        else cachedSetTimeout = defaultSetTimout;
    } catch (e) {
        cachedSetTimeout = defaultSetTimout;
    }
    try {
        if (typeof clearTimeout === "function") cachedClearTimeout = clearTimeout;
        else cachedClearTimeout = defaultClearTimeout;
    } catch (e) {
        cachedClearTimeout = defaultClearTimeout;
    }
})();
function runTimeout(fun) {
    if (cachedSetTimeout === setTimeout) //normal enviroments in sane situations
    return setTimeout(fun, 0);
    // if setTimeout wasn't available but was latter defined
    if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) {
        cachedSetTimeout = setTimeout;
        return setTimeout(fun, 0);
    }
    try {
        // when when somebody has screwed with setTimeout but no I.E. maddness
        return cachedSetTimeout(fun, 0);
    } catch (e) {
        try {
            // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally
            return cachedSetTimeout.call(null, fun, 0);
        } catch (e) {
            // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error
            return cachedSetTimeout.call(this, fun, 0);
        }
    }
}
function runClearTimeout(marker) {
    if (cachedClearTimeout === clearTimeout) //normal enviroments in sane situations
    return clearTimeout(marker);
    // if clearTimeout wasn't available but was latter defined
    if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) {
        cachedClearTimeout = clearTimeout;
        return clearTimeout(marker);
    }
    try {
        // when when somebody has screwed with setTimeout but no I.E. maddness
        return cachedClearTimeout(marker);
    } catch (e) {
        try {
            // When we are in I.E. but the script has been evaled so I.E. doesn't  trust the global object when called normally
            return cachedClearTimeout.call(null, marker);
        } catch (e) {
            // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error.
            // Some versions of I.E. have different rules for clearTimeout vs setTimeout
            return cachedClearTimeout.call(this, marker);
        }
    }
}
var queue = [];
var draining = false;
var currentQueue;
var queueIndex = -1;
function cleanUpNextTick() {
    if (!draining || !currentQueue) return;
    draining = false;
    if (currentQueue.length) queue = currentQueue.concat(queue);
    else queueIndex = -1;
    if (queue.length) drainQueue();
}
function drainQueue() {
    if (draining) return;
    var timeout = runTimeout(cleanUpNextTick);
    draining = true;
    var len = queue.length;
    while(len){
        currentQueue = queue;
        queue = [];
        while(++queueIndex < len)if (currentQueue) currentQueue[queueIndex].run();
        queueIndex = -1;
        len = queue.length;
    }
    currentQueue = null;
    draining = false;
    runClearTimeout(timeout);
}
process.nextTick = function(fun) {
    var args = new Array(arguments.length - 1);
    if (arguments.length > 1) for(var i = 1; i < arguments.length; i++)args[i - 1] = arguments[i];
    queue.push(new Item(fun, args));
    if (queue.length === 1 && !draining) runTimeout(drainQueue);
};
// v8 likes predictible objects
function Item(fun, array) {
    this.fun = fun;
    this.array = array;
}
Item.prototype.run = function() {
    this.fun.apply(null, this.array);
};
process.title = "browser";
process.browser = true;
process.env = {};
process.argv = [];
process.version = ""; // empty string to avoid regexp issues
process.versions = {};
function noop() {}
process.on = noop;
process.addListener = noop;
process.once = noop;
process.off = noop;
process.removeListener = noop;
process.removeAllListeners = noop;
process.emit = noop;
process.prependListener = noop;
process.prependOnceListener = noop;
process.listeners = function(name) {
    return [];
};
process.binding = function(name) {
    throw new Error("process.binding is not supported");
};
process.cwd = function() {
    return "/";
};
process.chdir = function(dir) {
    throw new Error("process.chdir is not supported");
};
process.umask = function() {
    return 0;
};

},{}],"hniYO":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Abs", ()=>Abs);
parcelHelpers.export(exports, "Acos", ()=>Acos);
parcelHelpers.export(exports, "Acosh", ()=>Acosh);
parcelHelpers.export(exports, "Add", ()=>Add);
parcelHelpers.export(exports, "AddN", ()=>AddN);
parcelHelpers.export(exports, "All", ()=>All);
parcelHelpers.export(exports, "Any", ()=>Any);
parcelHelpers.export(exports, "ArgMax", ()=>ArgMax);
parcelHelpers.export(exports, "ArgMin", ()=>ArgMin);
parcelHelpers.export(exports, "Asin", ()=>Asin);
parcelHelpers.export(exports, "Asinh", ()=>Asinh);
parcelHelpers.export(exports, "Atan", ()=>Atan);
parcelHelpers.export(exports, "Atanh", ()=>Atanh);
parcelHelpers.export(exports, "Atan2", ()=>Atan2);
parcelHelpers.export(exports, "AvgPool", ()=>AvgPool);
parcelHelpers.export(exports, "AvgPoolGrad", ()=>AvgPoolGrad);
parcelHelpers.export(exports, "AvgPool3D", ()=>AvgPool3D);
parcelHelpers.export(exports, "AvgPool3DGrad", ()=>AvgPool3DGrad);
parcelHelpers.export(exports, "BatchMatMul", ()=>BatchMatMul);
parcelHelpers.export(exports, "BatchToSpaceND", ()=>BatchToSpaceND);
parcelHelpers.export(exports, "Bincount", ()=>Bincount);
parcelHelpers.export(exports, "BitwiseAnd", ()=>BitwiseAnd);
parcelHelpers.export(exports, "BroadcastTo", ()=>BroadcastTo);
parcelHelpers.export(exports, "BroadcastArgs", ()=>BroadcastArgs);
parcelHelpers.export(exports, "Cast", ()=>Cast);
parcelHelpers.export(exports, "Ceil", ()=>Ceil);
parcelHelpers.export(exports, "ClipByValue", ()=>ClipByValue);
parcelHelpers.export(exports, "Complex", ()=>Complex);
parcelHelpers.export(exports, "ComplexAbs", ()=>ComplexAbs);
parcelHelpers.export(exports, "Concat", ()=>Concat);
parcelHelpers.export(exports, "Conv2D", ()=>Conv2D);
parcelHelpers.export(exports, "Conv2DBackpropFilter", ()=>Conv2DBackpropFilter);
parcelHelpers.export(exports, "Conv2DBackpropInput", ()=>Conv2DBackpropInput);
parcelHelpers.export(exports, "Conv3D", ()=>Conv3D);
parcelHelpers.export(exports, "Conv3DBackpropFilterV2", ()=>Conv3DBackpropFilterV2);
parcelHelpers.export(exports, "Conv3DBackpropInputV2", ()=>Conv3DBackpropInputV2);
parcelHelpers.export(exports, "Cos", ()=>Cos);
parcelHelpers.export(exports, "Cosh", ()=>Cosh);
parcelHelpers.export(exports, "Cumprod", ()=>Cumprod);
parcelHelpers.export(exports, "Cumsum", ()=>Cumsum);
parcelHelpers.export(exports, "CropAndResize", ()=>CropAndResize);
parcelHelpers.export(exports, "DenseBincount", ()=>DenseBincount);
parcelHelpers.export(exports, "DepthToSpace", ()=>DepthToSpace);
parcelHelpers.export(exports, "DepthwiseConv2dNative", ()=>DepthwiseConv2dNative);
parcelHelpers.export(exports, "DepthwiseConv2dNativeBackpropFilter", ()=>DepthwiseConv2dNativeBackpropFilter);
parcelHelpers.export(exports, "DepthwiseConv2dNativeBackpropInput", ()=>DepthwiseConv2dNativeBackpropInput);
parcelHelpers.export(exports, "Diag", ()=>Diag);
parcelHelpers.export(exports, "Dilation2D", ()=>Dilation2D);
parcelHelpers.export(exports, "Dilation2DBackpropInput", ()=>Dilation2DBackpropInput);
parcelHelpers.export(exports, "Dilation2DBackpropFilter", ()=>Dilation2DBackpropFilter);
parcelHelpers.export(exports, "Draw", ()=>Draw);
parcelHelpers.export(exports, "RealDiv", ()=>RealDiv);
parcelHelpers.export(exports, "Einsum", ()=>Einsum);
parcelHelpers.export(exports, "Elu", ()=>Elu);
parcelHelpers.export(exports, "EluGrad", ()=>EluGrad);
parcelHelpers.export(exports, "Erf", ()=>Erf);
parcelHelpers.export(exports, "Equal", ()=>Equal);
parcelHelpers.export(exports, "Exp", ()=>Exp);
parcelHelpers.export(exports, "ExpandDims", ()=>ExpandDims);
parcelHelpers.export(exports, "Expm1", ()=>Expm1);
parcelHelpers.export(exports, "FFT", ()=>FFT);
parcelHelpers.export(exports, "Fill", ()=>Fill);
parcelHelpers.export(exports, "FlipLeftRight", ()=>FlipLeftRight);
parcelHelpers.export(exports, "Floor", ()=>Floor);
parcelHelpers.export(exports, "FloorDiv", ()=>FloorDiv);
parcelHelpers.export(exports, "FusedBatchNorm", ()=>FusedBatchNorm);
parcelHelpers.export(exports, "GatherV2", ()=>GatherV2);
parcelHelpers.export(exports, "GatherNd", ()=>GatherNd);
parcelHelpers.export(exports, "Greater", ()=>Greater);
parcelHelpers.export(exports, "GreaterEqual", ()=>GreaterEqual);
parcelHelpers.export(exports, "Identity", ()=>Identity);
parcelHelpers.export(exports, "IFFT", ()=>IFFT);
parcelHelpers.export(exports, "Imag", ()=>Imag);
parcelHelpers.export(exports, "IsFinite", ()=>IsFinite);
parcelHelpers.export(exports, "IsInf", ()=>IsInf);
parcelHelpers.export(exports, "IsNan", ()=>IsNan);
parcelHelpers.export(exports, "LeakyRelu", ()=>LeakyRelu);
parcelHelpers.export(exports, "Less", ()=>Less);
parcelHelpers.export(exports, "LessEqual", ()=>LessEqual);
parcelHelpers.export(exports, "LinSpace", ()=>LinSpace);
parcelHelpers.export(exports, "Log", ()=>Log);
parcelHelpers.export(exports, "Log1p", ()=>Log1p);
parcelHelpers.export(exports, "LogicalAnd", ()=>LogicalAnd);
parcelHelpers.export(exports, "LogicalNot", ()=>LogicalNot);
parcelHelpers.export(exports, "LogicalOr", ()=>LogicalOr);
parcelHelpers.export(exports, "LogicalXor", ()=>LogicalXor);
parcelHelpers.export(exports, "LogSoftmax", ()=>LogSoftmax);
parcelHelpers.export(exports, "LowerBound", ()=>LowerBound);
parcelHelpers.export(exports, "LRN", ()=>LRN);
parcelHelpers.export(exports, "LRNGrad", ()=>LRNGrad);
parcelHelpers.export(exports, "MatrixBandPart", ()=>MatrixBandPart);
parcelHelpers.export(exports, "Max", ()=>Max);
parcelHelpers.export(exports, "Maximum", ()=>Maximum);
parcelHelpers.export(exports, "MaxPool", ()=>MaxPool);
parcelHelpers.export(exports, "MaxPoolGrad", ()=>MaxPoolGrad);
parcelHelpers.export(exports, "MaxPool3D", ()=>MaxPool3D);
parcelHelpers.export(exports, "MaxPool3DGrad", ()=>MaxPool3DGrad);
parcelHelpers.export(exports, "MaxPoolWithArgmax", ()=>MaxPoolWithArgmax);
parcelHelpers.export(exports, "Mean", ()=>Mean);
parcelHelpers.export(exports, "Min", ()=>Min);
parcelHelpers.export(exports, "Minimum", ()=>Minimum);
parcelHelpers.export(exports, "MirrorPad", ()=>MirrorPad);
parcelHelpers.export(exports, "Mod", ()=>Mod);
parcelHelpers.export(exports, "Multinomial", ()=>Multinomial);
parcelHelpers.export(exports, "Multiply", ()=>Multiply);
parcelHelpers.export(exports, "Neg", ()=>Neg);
parcelHelpers.export(exports, "NotEqual", ()=>NotEqual);
parcelHelpers.export(exports, "NonMaxSuppressionV3", ()=>NonMaxSuppressionV3);
parcelHelpers.export(exports, "NonMaxSuppressionV4", ()=>NonMaxSuppressionV4);
parcelHelpers.export(exports, "NonMaxSuppressionV5", ()=>NonMaxSuppressionV5);
parcelHelpers.export(exports, "OnesLike", ()=>OnesLike);
parcelHelpers.export(exports, "OneHot", ()=>OneHot);
parcelHelpers.export(exports, "Pack", ()=>Pack);
parcelHelpers.export(exports, "PadV2", ()=>PadV2);
parcelHelpers.export(exports, "Pool", ()=>Pool);
parcelHelpers.export(exports, "Pow", ()=>Pow);
parcelHelpers.export(exports, "Prelu", ()=>Prelu);
parcelHelpers.export(exports, "Prod", ()=>Prod);
parcelHelpers.export(exports, "RaggedGather", ()=>RaggedGather);
parcelHelpers.export(exports, "RaggedRange", ()=>RaggedRange);
parcelHelpers.export(exports, "RaggedTensorToTensor", ()=>RaggedTensorToTensor);
parcelHelpers.export(exports, "Range", ()=>Range);
parcelHelpers.export(exports, "Real", ()=>Real);
parcelHelpers.export(exports, "Reciprocal", ()=>Reciprocal);
parcelHelpers.export(exports, "Relu", ()=>Relu);
parcelHelpers.export(exports, "Reshape", ()=>Reshape);
parcelHelpers.export(exports, "ResizeNearestNeighbor", ()=>ResizeNearestNeighbor);
parcelHelpers.export(exports, "ResizeNearestNeighborGrad", ()=>ResizeNearestNeighborGrad);
parcelHelpers.export(exports, "ResizeBilinear", ()=>ResizeBilinear);
parcelHelpers.export(exports, "ResizeBilinearGrad", ()=>ResizeBilinearGrad);
parcelHelpers.export(exports, "Relu6", ()=>Relu6);
parcelHelpers.export(exports, "Reverse", ()=>Reverse);
parcelHelpers.export(exports, "Round", ()=>Round);
parcelHelpers.export(exports, "Rsqrt", ()=>Rsqrt);
parcelHelpers.export(exports, "ScatterNd", ()=>ScatterNd);
parcelHelpers.export(exports, "TensorScatterUpdate", ()=>TensorScatterUpdate);
parcelHelpers.export(exports, "SearchSorted", ()=>SearchSorted);
parcelHelpers.export(exports, "Select", ()=>Select);
parcelHelpers.export(exports, "Selu", ()=>Selu);
parcelHelpers.export(exports, "Slice", ()=>Slice);
parcelHelpers.export(exports, "Sin", ()=>Sin);
parcelHelpers.export(exports, "Sinh", ()=>Sinh);
parcelHelpers.export(exports, "Sign", ()=>Sign);
parcelHelpers.export(exports, "Sigmoid", ()=>Sigmoid);
parcelHelpers.export(exports, "Softplus", ()=>Softplus);
parcelHelpers.export(exports, "Sqrt", ()=>Sqrt);
parcelHelpers.export(exports, "Sum", ()=>Sum);
parcelHelpers.export(exports, "SpaceToBatchND", ()=>SpaceToBatchND);
parcelHelpers.export(exports, "SplitV", ()=>SplitV);
parcelHelpers.export(exports, "Softmax", ()=>Softmax);
parcelHelpers.export(exports, "SparseFillEmptyRows", ()=>SparseFillEmptyRows);
parcelHelpers.export(exports, "SparseReshape", ()=>SparseReshape);
parcelHelpers.export(exports, "SparseSegmentMean", ()=>SparseSegmentMean);
parcelHelpers.export(exports, "SparseSegmentSum", ()=>SparseSegmentSum);
parcelHelpers.export(exports, "SparseToDense", ()=>SparseToDense);
parcelHelpers.export(exports, "SquaredDifference", ()=>SquaredDifference);
parcelHelpers.export(exports, "Square", ()=>Square);
parcelHelpers.export(exports, "StaticRegexReplace", ()=>StaticRegexReplace);
parcelHelpers.export(exports, "StridedSlice", ()=>StridedSlice);
parcelHelpers.export(exports, "StringNGrams", ()=>StringNGrams);
parcelHelpers.export(exports, "StringSplit", ()=>StringSplit);
parcelHelpers.export(exports, "StringToHashBucketFast", ()=>StringToHashBucketFast);
parcelHelpers.export(exports, "Sub", ()=>Sub);
parcelHelpers.export(exports, "Tan", ()=>Tan);
parcelHelpers.export(exports, "Tanh", ()=>Tanh);
parcelHelpers.export(exports, "Tile", ()=>Tile);
parcelHelpers.export(exports, "TopK", ()=>TopK);
parcelHelpers.export(exports, "Transform", ()=>Transform);
parcelHelpers.export(exports, "Transpose", ()=>Transpose);
parcelHelpers.export(exports, "Unique", ()=>Unique);
parcelHelpers.export(exports, "Unpack", ()=>Unpack);
parcelHelpers.export(exports, "UnsortedSegmentSum", ()=>UnsortedSegmentSum);
parcelHelpers.export(exports, "UpperBound", ()=>UpperBound);
parcelHelpers.export(exports, "ZerosLike", ()=>ZerosLike);
parcelHelpers.export(exports, "Step", ()=>Step);
parcelHelpers.export(exports, "FromPixels", ()=>FromPixels);
parcelHelpers.export(exports, "RotateWithOffset", ()=>RotateWithOffset);
parcelHelpers.export(exports, "_FusedMatMul", ()=>_FusedMatMul);
parcelHelpers.export(exports, "FusedConv2D", ()=>FusedConv2D);
parcelHelpers.export(exports, "FusedDepthwiseConv2D", ()=>FusedDepthwiseConv2D);
const Abs = "Abs";
const Acos = "Acos";
const Acosh = "Acosh";
const Add = "Add";
const AddN = "AddN";
const All = "All";
const Any = "Any";
const ArgMax = "ArgMax";
const ArgMin = "ArgMin";
const Asin = "Asin";
const Asinh = "Asinh";
const Atan = "Atan";
const Atanh = "Atanh";
const Atan2 = "Atan2";
const AvgPool = "AvgPool";
const AvgPoolGrad = "AvgPoolGrad";
const AvgPool3D = "AvgPool3D";
const AvgPool3DGrad = "AvgPool3DGrad";
const BatchMatMul = "BatchMatMul";
const BatchToSpaceND = "BatchToSpaceND";
const Bincount = "Bincount";
const BitwiseAnd = "BitwiseAnd";
const BroadcastTo = "BroadcastTo";
const BroadcastArgs = "BroadcastArgs";
const Cast = "Cast";
const Ceil = "Ceil";
const ClipByValue = "ClipByValue";
const Complex = "Complex";
const ComplexAbs = "ComplexAbs";
const Concat = "Concat";
const Conv2D = "Conv2D";
const Conv2DBackpropFilter = "Conv2DBackpropFilter";
const Conv2DBackpropInput = "Conv2DBackpropInput";
const Conv3D = "Conv3D";
const Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
const Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
const Cos = "Cos";
const Cosh = "Cosh";
const Cumprod = "Cumprod";
const Cumsum = "Cumsum";
const CropAndResize = "CropAndResize";
const DenseBincount = "DenseBincount";
const DepthToSpace = "DepthToSpace";
const DepthwiseConv2dNative = "DepthwiseConv2dNative";
const DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
const DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
const Diag = "Diag";
const Dilation2D = "Dilation2D";
const Dilation2DBackpropInput = "Dilation2DBackpropInput";
const Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
const Draw = "Draw";
const RealDiv = "RealDiv";
const Einsum = "Einsum";
const Elu = "Elu";
const EluGrad = "EluGrad";
const Erf = "Erf";
const Equal = "Equal";
const Exp = "Exp";
const ExpandDims = "ExpandDims";
const Expm1 = "Expm1";
const FFT = "FFT";
const Fill = "Fill";
const FlipLeftRight = "FlipLeftRight";
const Floor = "Floor";
const FloorDiv = "FloorDiv";
const FusedBatchNorm = "FusedBatchNorm";
const GatherV2 = "GatherV2";
const GatherNd = "GatherNd";
const Greater = "Greater";
const GreaterEqual = "GreaterEqual";
const Identity = "Identity";
const IFFT = "IFFT";
const Imag = "Imag";
const IsFinite = "IsFinite";
const IsInf = "IsInf";
const IsNan = "IsNan";
const LeakyRelu = "LeakyRelu";
const Less = "Less";
const LessEqual = "LessEqual";
const LinSpace = "LinSpace";
const Log = "Log";
const Log1p = "Log1p";
const LogicalAnd = "LogicalAnd";
const LogicalNot = "LogicalNot";
const LogicalOr = "LogicalOr";
const LogicalXor = "LogicalXor";
const LogSoftmax = "LogSoftmax";
const LowerBound = "LowerBound";
const LRN = "LRN";
const LRNGrad = "LRNGrad";
const MatrixBandPart = "MatrixBandPart";
const Max = "Max";
const Maximum = "Maximum";
const MaxPool = "MaxPool";
const MaxPoolGrad = "MaxPoolGrad";
const MaxPool3D = "MaxPool3D";
const MaxPool3DGrad = "MaxPool3DGrad";
const MaxPoolWithArgmax = "MaxPoolWithArgmax";
const Mean = "Mean";
const Min = "Min";
const Minimum = "Minimum";
const MirrorPad = "MirrorPad";
const Mod = "Mod";
const Multinomial = "Multinomial";
const Multiply = "Multiply";
const Neg = "Neg";
const NotEqual = "NotEqual";
const NonMaxSuppressionV3 = "NonMaxSuppressionV3";
const NonMaxSuppressionV4 = "NonMaxSuppressionV4";
const NonMaxSuppressionV5 = "NonMaxSuppressionV5";
const OnesLike = "OnesLike";
const OneHot = "OneHot";
const Pack = "Pack";
const PadV2 = "PadV2";
const Pool = "Pool";
const Pow = "Pow";
const Prelu = "Prelu";
const Prod = "Prod";
const RaggedGather = "RaggedGather";
const RaggedRange = "RaggedRange";
const RaggedTensorToTensor = "RaggedTensorToTensor";
const Range = "Range";
const Real = "Real";
const Reciprocal = "Reciprocal";
const Relu = "Relu";
const Reshape = "Reshape";
const ResizeNearestNeighbor = "ResizeNearestNeighbor";
const ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
const ResizeBilinear = "ResizeBilinear";
const ResizeBilinearGrad = "ResizeBilinearGrad";
const Relu6 = "Relu6";
const Reverse = "Reverse";
const Round = "Round";
const Rsqrt = "Rsqrt";
const ScatterNd = "ScatterNd";
const TensorScatterUpdate = "TensorScatterUpdate";
const SearchSorted = "SearchSorted";
const Select = "Select";
const Selu = "Selu";
const Slice = "Slice";
const Sin = "Sin";
const Sinh = "Sinh";
const Sign = "Sign";
const Sigmoid = "Sigmoid";
const Softplus = "Softplus";
const Sqrt = "Sqrt";
const Sum = "Sum";
const SpaceToBatchND = "SpaceToBatchND";
const SplitV = "SplitV";
const Softmax = "Softmax";
const SparseFillEmptyRows = "SparseFillEmptyRows";
const SparseReshape = "SparseReshape";
const SparseSegmentMean = "SparseSegmentMean";
const SparseSegmentSum = "SparseSegmentSum";
const SparseToDense = "SparseToDense";
const SquaredDifference = "SquaredDifference";
const Square = "Square";
const StaticRegexReplace = "StaticRegexReplace";
const StridedSlice = "StridedSlice";
const StringNGrams = "StringNGrams";
const StringSplit = "StringSplit";
const StringToHashBucketFast = "StringToHashBucketFast";
const Sub = "Sub";
const Tan = "Tan";
const Tanh = "Tanh";
const Tile = "Tile";
const TopK = "TopK";
const Transform = "Transform";
const Transpose = "Transpose";
const Unique = "Unique";
const Unpack = "Unpack";
const UnsortedSegmentSum = "UnsortedSegmentSum";
const UpperBound = "UpperBound";
const ZerosLike = "ZerosLike";
const Step = "Step";
const FromPixels = "FromPixels";
const RotateWithOffset = "RotateWithOffset";
const _FusedMatMul = "_FusedMatMul";
const FusedConv2D = "FusedConv2D";
const FusedDepthwiseConv2D = "FusedDepthwiseConv2D";

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aG7g9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Returns the kernel function (code) associated with the provided names.
 *
 * @param kernelName The official name of the kernel.
 * @param backendName The official name of the backend.
 */ parcelHelpers.export(exports, "getKernel", ()=>getKernel);
/**
 * Returns the registered gradient info associated with the provided kernel.
 * @param kernelName The official TF kernel name.
 */ parcelHelpers.export(exports, "getGradient", ()=>getGradient);
parcelHelpers.export(exports, "getKernelsForBackend", ()=>getKernelsForBackend);
/**
 * Registers the function (forward pass) for the kernel in a global registry.
 *
 * @param config A config object with the following properties:
 * - `kernelName` The official name of the kernel.
 * - `backendName` The official name of the backend.
 * - `kernelFunc` The function to run during the forward pass of the kernel.
 * - `setupFunc` Optional. Gets called once, after the backend initializes.
 * - `disposeFunc` Optional. Gets called once, right before the backend is
 * disposed.
 */ parcelHelpers.export(exports, "registerKernel", ()=>registerKernel);
/**
 * Registers a gradient function for a given kernel in the global registry,
 * to be used during the back-propagation of that kernel.
 *
 * @param config An object with the following properties:
 * - `kernelName` The name of the kernel that the gradient function is for.
 * - `gradFunc` The function to run during back-propagation.
 */ parcelHelpers.export(exports, "registerGradient", ()=>registerGradient);
/**
 * Removes the kernel function from the registry.
 *
 * @param kernelName The official name of the kernel.
 * @param backendName The official name of the backend.
 *
 */ parcelHelpers.export(exports, "unregisterKernel", ()=>unregisterKernel);
/** Removes the registered gradient from the global registry. */ parcelHelpers.export(exports, "unregisterGradient", ()=>unregisterGradient);
/**
 * Finds kernels that have already been registered to a backend and re-registers
 * them for a new backend. Useful for registering custom backends.
 * @param registeredBackendName Already registered backend.
 * @param newBackendName New backend.
 */ parcelHelpers.export(exports, "copyRegisteredKernels", ()=>copyRegisteredKernels);
var _environment = require("./environment");
var _globalUtil = require("./global_util");
var _log = require("./log");
const kernelRegistry = (0, _globalUtil.getGlobal)("kernelRegistry", ()=>new Map());
const gradRegistry = (0, _globalUtil.getGlobal)("gradRegistry", ()=>new Map());
function getKernel(kernelName, backendName) {
    const key = makeKey(kernelName, backendName);
    return kernelRegistry.get(key);
}
function getGradient(kernelName) {
    return gradRegistry.get(kernelName);
}
function getKernelsForBackend(backendName) {
    const it = kernelRegistry.entries();
    const result = [];
    while(true){
        const { done, value } = it.next();
        if (done) break;
        const [key, config] = value;
        const [backend] = key.split("_");
        if (backend === backendName) result.push(config);
    }
    return result;
}
function registerKernel(config) {
    const { kernelName, backendName } = config;
    const key = makeKey(kernelName, backendName);
    if (kernelRegistry.has(key)) _log.warn(`The kernel '${kernelName}' for backend ` + `'${backendName}' is already registered`);
    kernelRegistry.set(key, config);
}
function registerGradient(config) {
    const { kernelName } = config;
    if (gradRegistry.has(kernelName)) // TODO (yassogba) after 3.0 assess whether we need to keep this gated
    // to debug mode.
    {
        if ((0, _environment.env)().getBool("DEBUG")) _log.warn(`Overriding the gradient for '${kernelName}'`);
    }
    gradRegistry.set(kernelName, config);
}
function unregisterKernel(kernelName, backendName) {
    const key = makeKey(kernelName, backendName);
    if (!kernelRegistry.has(key)) throw new Error(`The kernel '${kernelName}' for backend ` + `'${backendName}' is not registered`);
    kernelRegistry.delete(key);
}
function unregisterGradient(kernelName) {
    if (!gradRegistry.has(kernelName)) throw new Error(`The gradient '${kernelName}' for backend is not registered`);
    gradRegistry.delete(kernelName);
}
function copyRegisteredKernels(registeredBackendName, newBackendName) {
    const kernels = getKernelsForBackend(registeredBackendName);
    kernels.forEach((kernelConfig)=>{
        const newKernelConfig = Object.assign({}, kernelConfig, {
            backendName: newBackendName
        });
        registerKernel(newKernelConfig);
    });
}
function makeKey(kernelName, backendName) {
    return `${backendName}_${kernelName}`;
}

},{"./environment":"dXmHm","./global_util":"82SAe","./log":"5xR9j","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5xR9j":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "warn", ()=>warn);
parcelHelpers.export(exports, "log", ()=>log);
var _environment = require("./environment");
function warn(...msg) {
    if (!((0, _environment.env)().getBool("IS_TEST") || (0, _environment.env)().getBool("PROD"))) console.warn(...msg);
}
function log(...msg) {
    if (!((0, _environment.env)().getBool("IS_TEST") || (0, _environment.env)().getBool("PROD"))) console.log(...msg);
}

},{"./environment":"dXmHm","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aLKDS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Profiler", ()=>Profiler);
parcelHelpers.export(exports, "checkComputationForErrors", ()=>checkComputationForErrors);
parcelHelpers.export(exports, "Logger", ()=>Logger);
var _environment = require("./environment");
var _util = require("./util");
class Profiler {
    constructor(backendTimer, logger){
        this.backendTimer = backendTimer;
        this.logger = logger;
        if (logger == null) this.logger = new Logger();
    }
    profileKernel(kernelName, inputs, f) {
        let outputs;
        const holdResultWrapperFn = ()=>{
            outputs = f();
        };
        let timer;
        const start = _util.now();
        if (this.backendTimer.timerAvailable()) timer = this.backendTimer.time(holdResultWrapperFn);
        else {
            holdResultWrapperFn();
            for (const output of outputs)output.dataSync();
            timer = Promise.resolve({
                kernelMs: _util.now() - start
            });
        }
        if ((0, _environment.env)().getBool("CHECK_COMPUTATION_FOR_ERRORS")) for(let i = 0; i < outputs.length; i++){
            const output = outputs[i];
            // Dangling promise here because we don't want to propagate up
            // asynchronicity.
            output.data().then((tensorVals)=>{
                checkComputationForErrors(tensorVals, output.dtype, kernelName);
            });
        }
        const kernelProfile = {
            kernelName,
            outputs,
            inputs,
            timeMs: timer.then((timing)=>timing.kernelMs),
            extraInfo: timer.then((timing)=>timing.getExtraProfileInfo != null ? timing.getExtraProfileInfo() : "")
        };
        return kernelProfile;
    }
    logKernelProfile(kernelProfile) {
        const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;
        outputs.forEach((result)=>{
            Promise.all([
                result.data(),
                timeMs,
                extraInfo
            ]).then((valueContainer)=>{
                this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
            });
        });
    }
}
function checkComputationForErrors(vals, dtype, kernelName) {
    if (dtype !== "float32") // Only floating point computations will generate NaN values
    return false;
    for(let i = 0; i < vals.length; i++){
        const num = vals[i];
        if (isNaN(num) || !isFinite(num)) {
            // Throwing custom exception so behavior is testable.
            console.warn(`Found ${num} in the result of '${kernelName}'`);
            return true;
        }
    }
    return false;
}
class Logger {
    logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
        const time = typeof timeMs === "number" ? _util.rightPad(`${timeMs}ms`, 9) : timeMs["error"];
        const paddedName = _util.rightPad(name, 25);
        const rank = result.rank;
        const size = result.size;
        const shape = _util.rightPad(result.shape.toString(), 14);
        let inputShapesDescription = "";
        for(const name in inputs){
            const input = inputs[name];
            if (input != null) {
                // The input might be a non-tensor (e.g HTMLImageElement), in which case
                // we claim the output shape as input shape.
                const inputShape = input.shape || result.shape;
                const inputRank = inputShape.length;
                inputShapesDescription += `${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ""} `;
            }
        }
        console.log(`%c${paddedName}\t%c${time}\t%c${rank}D ${shape}\t%c${size}\t%c${inputShapesDescription}\t%c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
    }
}

},{"./environment":"dXmHm","./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ctavo":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Create typed array for scalar value. Used for storing in `DataStorage`.
 */ parcelHelpers.export(exports, "createScalarValue", ()=>createScalarValue);
parcelHelpers.export(exports, "toTypedArray", ()=>toTypedArray);
/**
 * Returns the current high-resolution time in milliseconds relative to an
 * arbitrary time in the past. It works across different platforms (node.js,
 * browsers).
 *
 * ```js
 * console.log(tf.util.now());
 * ```
 *
 * @doc {heading: 'Util', namespace: 'util'}
 */ parcelHelpers.export(exports, "now", ()=>now);
/**
 * Returns a platform-specific implementation of
 * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).
 *
 * If `fetch` is defined on the global object (`window`, `process`, etc.),
 * `tf.util.fetch` returns that function.
 *
 * If not, `tf.util.fetch` returns a platform-specific solution.
 *
 * ```js
 * const resource = await tf.util.fetch('https://cdn.jsdelivr.net/npm/@tensorflow/tfjs');
 * // handle response
 * ```
 *
 * @doc {heading: 'Util'}
 */ parcelHelpers.export(exports, "fetch", ()=>fetch);
/**
 * Encodes the provided string into bytes using the provided encoding scheme.
 *
 * @param s The string to encode.
 * @param encoding The encoding scheme. Defaults to utf-8.
 *
 * @doc {heading: 'Util'}
 */ parcelHelpers.export(exports, "encodeString", ()=>encodeString);
/**
 * Decodes the provided bytes into a string using the provided encoding scheme.
 * @param bytes The bytes to decode.
 *
 * @param encoding The encoding scheme. Defaults to utf-8.
 *
 * @doc {heading: 'Util'}
 */ parcelHelpers.export(exports, "decodeString", ()=>decodeString);
parcelHelpers.export(exports, "isTypedArray", ()=>isTypedArray);
// NOTE: We explicitly type out what T extends instead of any so that
// util.flatten on a nested array of number doesn't try to infer T as a
// number[][], causing us to explicitly type util.flatten<number>().
/**
 *  Flattens an arbitrarily nested array.
 *
 * ```js
 * const a = [[1, 2], [3, 4], [5, [6, [7]]]];
 * const flat = tf.util.flatten(a);
 * console.log(flat);
 * ```
 *
 *  @param arr The nested array to flatten.
 *  @param result The destination array which holds the elements.
 *  @param skipTypedArray If true, avoids flattening the typed arrays. Defaults
 *      to false.
 *
 * @doc {heading: 'Util', namespace: 'util'}
 */ parcelHelpers.export(exports, "flatten", ()=>flatten);
var _environment = require("./environment");
var _isTypedArrayBrowser = require("./platforms/is_typed_array_browser");
var _utilBase = require("./util_base");
parcelHelpers.exportAll(_utilBase, exports);
var _hashUtil = require("./hash_util");
parcelHelpers.exportAll(_hashUtil, exports);
function createScalarValue(value, dtype) {
    if (dtype === "string") return encodeString(value);
    return toTypedArray([
        value
    ], dtype);
}
function noConversionNeeded(a, dtype) {
    return a instanceof Float32Array && dtype === "float32" || a instanceof Int32Array && dtype === "int32" || a instanceof Uint8Array && dtype === "bool";
}
function toTypedArray(a, dtype) {
    if (dtype === "string") throw new Error("Cannot convert a string[] to a TypedArray");
    if (Array.isArray(a)) a = flatten(a);
    if ((0, _environment.env)().getBool("DEBUG")) _utilBase.checkConversionForErrors(a, dtype);
    if (noConversionNeeded(a, dtype)) return a;
    if (dtype == null || dtype === "float32" || dtype === "complex64") return new Float32Array(a);
    else if (dtype === "int32") return new Int32Array(a);
    else if (dtype === "bool") {
        const bool = new Uint8Array(a.length);
        for(let i = 0; i < bool.length; ++i)if (Math.round(a[i]) !== 0) bool[i] = 1;
        return bool;
    } else throw new Error(`Unknown data type ${dtype}`);
}
function now() {
    return (0, _environment.env)().platform.now();
}
function fetch(path, requestInits) {
    return (0, _environment.env)().platform.fetch(path, requestInits);
}
function encodeString(s, encoding = "utf-8") {
    encoding = encoding || "utf-8";
    return (0, _environment.env)().platform.encode(s, encoding);
}
function decodeString(bytes, encoding = "utf-8") {
    encoding = encoding || "utf-8";
    return (0, _environment.env)().platform.decode(bytes, encoding);
}
function isTypedArray(a) {
    // TODO(mattsoulanille): Remove this fallback in 5.0.0
    if ((0, _environment.env)().platform.isTypedArray != null) return (0, _environment.env)().platform.isTypedArray(a);
    else return (0, _isTypedArrayBrowser.isTypedArrayBrowser)(a);
}
function flatten(arr, result = [], skipTypedArray = false) {
    if (result == null) result = [];
    if (typeof arr === "boolean" || typeof arr === "number" || typeof arr === "string" || _utilBase.isPromise(arr) || arr == null || isTypedArray(arr) && skipTypedArray) result.push(arr);
    else if (Array.isArray(arr) || isTypedArray(arr)) for(let i = 0; i < arr.length; ++i)flatten(arr[i], result, skipTypedArray);
    else {
        let maxIndex = -1;
        for (const key of Object.keys(arr))// 0 or positive integer.
        if (/^([1-9]+[0-9]*|0)$/.test(key)) maxIndex = Math.max(maxIndex, Number(key));
        for(let i = 0; i <= maxIndex; i++)// tslint:disable-next-line: no-unnecessary-type-assertion
        flatten(arr[i], result, skipTypedArray);
    }
    return result;
}

},{"./environment":"dXmHm","./platforms/is_typed_array_browser":"cjcvP","./util_base":"4Vo4a","./hash_util":"jaKDR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cjcvP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isTypedArrayBrowser", ()=>isTypedArrayBrowser);
function isTypedArrayBrowser(a) {
    return a instanceof Float32Array || a instanceof Int32Array || a instanceof Uint8Array || a instanceof Uint8ClampedArray;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jaKDR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Workaround for allowing cjs module to be included in bundle created by
// rollup.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "hexToLong", ()=>hexToLong);
parcelHelpers.export(exports, "fingerPrint64", ()=>fingerPrint64);
var _long = require("long");
// tslint:disable-next-line
const Long = // tslint:disable-next-line
_long.default || _long;
function hexToLong(hex) {
    return Long.fromString(hex, true, 16);
}
// Some primes between 2^63 and 2^64 for various uses.
// Hex 0xc3a5c85c97cb3127
const k0 = hexToLong("c3a5c85c97cb3127");
// Hex 0xb492b66fbe98f273
const k1 = hexToLong("b492b66fbe98f273");
// Hex 0x9ae16a3b2f90404f
const k2 = hexToLong("9ae16a3b2f90404f");
function shiftMix(val) {
    return val.xor(val.shru(47));
}
function fetch(s, offset, numBytes) {
    const bytes = s.slice(offset, offset + numBytes);
    return Long.fromBytes(Array.from(bytes), true, true);
}
function fetch64(s, offset) {
    return fetch(s, offset, 8);
}
function fetch32(s, offset) {
    return fetch(s, offset, 4);
}
function rotate64(val, shift) {
    // Avoid shifting by 64: doing so yields an undefined result.
    return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
}
function hashLen16(u, v, mul = hexToLong("9ddfea08eb382d69")) {
    // Murmur-inspired hashing.
    let a = u.xor(v).mul(mul);
    a = a.xor(a.shru(47));
    let b = v.xor(a).mul(mul);
    b = b.xor(b.shru(47));
    b = b.mul(mul);
    return b;
}
// Return a 16-byte hash for 48 bytes.  Quick and dirty.
// Callers do best to use "random-looking" values for a and b.
function weakHashLen32WithSeeds(w, x, y, z, a, b) {
    a = a.add(w);
    b = rotate64(b.add(a).add(z), 21);
    const c = a;
    a = a.add(x);
    a = a.add(y);
    b = b.add(rotate64(a, 44));
    return [
        a.add(z),
        b.add(c)
    ];
}
function weakHashLen32WithSeedsStr(s, offset, a, b) {
    return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);
}
function hashLen0to16(s, len = s.length) {
    if (len >= 8) {
        const mul = k2.add(len * 2);
        const a = fetch64(s, 0).add(k2);
        const b = fetch64(s, len - 8);
        const c = rotate64(b, 37).mul(mul).add(a);
        const d = rotate64(a, 25).add(b).mul(mul);
        return hashLen16(c, d, mul);
    }
    if (len >= 4) {
        const mul = k2.add(len * 2);
        const a = fetch32(s, 0);
        return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul);
    }
    if (len > 0) {
        const a = s[0];
        const b = s[len >> 1];
        const c = s[len - 1];
        const y = a + (b << 8);
        const z = len + (c << 2);
        return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
    }
    return k2;
}
function hashLen17to32(s, len = s.length) {
    const mul = k2.add(len * 2);
    const a = fetch64(s, 0).mul(k1);
    const b = fetch64(s, 8);
    const c = fetch64(s, len - 8).mul(mul);
    const d = fetch64(s, len - 16).mul(k2);
    return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul);
}
function hashLen33to64(s, len = s.length) {
    const mul = k2.add(len * 2);
    const a = fetch64(s, 0).mul(k2);
    const b = fetch64(s, 8);
    const c = fetch64(s, len - 8).mul(mul);
    const d = fetch64(s, len - 16).mul(k2);
    const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);
    const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul);
    const e = fetch64(s, 16).mul(mul);
    const f = fetch64(s, 24);
    const g = y.add(fetch64(s, len - 32)).mul(mul);
    const h = z.add(fetch64(s, len - 24)).mul(mul);
    return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul);
}
function fingerPrint64(s, len = s.length) {
    const seed = Long.fromNumber(81, true);
    if (len <= 32) {
        if (len <= 16) return hashLen0to16(s, len);
        else return hashLen17to32(s, len);
    } else if (len <= 64) return hashLen33to64(s, len);
    // For strings over 64 bytes we loop.  Internal state consists of
    // 56 bytes: v, w, x, y, and z.
    let x = seed;
    let y = seed.mul(k1).add(113);
    let z = shiftMix(y.mul(k2).add(113)).mul(k2);
    let v = [
        Long.UZERO,
        Long.UZERO
    ];
    let w = [
        Long.UZERO,
        Long.UZERO
    ];
    x = x.mul(k2).add(fetch64(s, 0));
    let offset = 0;
    // Set end so that after the loop we have 1 to 64 bytes left to process.
    const end = (len - 1 >> 6) * 64;
    const last64 = end + (len - 1 & 63) - 63;
    do {
        x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);
        y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);
        x = x.xor(w[1]);
        y = y.add(v[0]).add(fetch64(s, offset + 40));
        z = rotate64(z.add(w[0]), 33).mul(k1);
        v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));
        w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
        [z, x] = [
            x,
            z
        ];
        offset += 64;
    }while (offset !== end);
    const mul = k1.add(z.and(0xff).shl(1));
    // Point to the last 64 bytes of input.
    offset = last64;
    w[0] = w[0].add(len - 1 & 63);
    v[0] = v[0].add(w[0]);
    w[0] = w[0].add(v[0]);
    x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul);
    y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul);
    x = x.xor(w[1].mul(9));
    y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));
    z = rotate64(z.add(w[0]), 33).mul(mul);
    v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul), x.add(w[0]));
    w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
    [z, x] = [
        x,
        z
    ];
    return hashLen16(hashLen16(v[0], w[0], mul).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul).add(x), mul);
}

},{"long":"2goS4","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2goS4":[function(require,module,exports) {
module.exports = Long;
/**
 * wasm optimizations, to do native i64 multiplication and divide
 */ var wasm = null;
try {
    wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([
        0,
        97,
        115,
        109,
        1,
        0,
        0,
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        127,
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    ])), {}).exports;
} catch (e) {
// no wasm support :(
}
/**
 * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers.
 *  See the from* functions below for more convenient ways of constructing Longs.
 * @exports Long
 * @class A Long class for representing a 64 bit two's-complement integer value.
 * @param {number} low The low (signed) 32 bits of the long
 * @param {number} high The high (signed) 32 bits of the long
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @constructor
 */ function Long(low, high, unsigned) {
    /**
     * The low 32 bits as a signed value.
     * @type {number}
     */ this.low = low | 0;
    /**
     * The high 32 bits as a signed value.
     * @type {number}
     */ this.high = high | 0;
    /**
     * Whether unsigned or not.
     * @type {boolean}
     */ this.unsigned = !!unsigned;
}
// The internal representation of a long is the two given signed, 32-bit values.
// We use 32-bit pieces because these are the size of integers on which
// Javascript performs bit-operations.  For operations like addition and
// multiplication, we split each number into 16 bit pieces, which can easily be
// multiplied within Javascript's floating-point representation without overflow
// or change in sign.
//
// In the algorithms below, we frequently reduce the negative case to the
// positive case by negating the input(s) and then post-processing the result.
// Note that we must ALWAYS check specially whether those values are MIN_VALUE
// (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as
// a positive number, it overflows back into a negative).  Not handling this
// case would often result in infinite recursion.
//
// Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from*
// methods on which they depend.
/**
 * An indicator used to reliably determine if an object is a Long or not.
 * @type {boolean}
 * @const
 * @private
 */ Long.prototype.__isLong__;
Object.defineProperty(Long.prototype, "__isLong__", {
    value: true
});
/**
 * @function
 * @param {*} obj Object
 * @returns {boolean}
 * @inner
 */ function isLong(obj) {
    return (obj && obj["__isLong__"]) === true;
}
/**
 * Tests if the specified object is a Long.
 * @function
 * @param {*} obj Object
 * @returns {boolean}
 */ Long.isLong = isLong;
/**
 * A cache of the Long representations of small integer values.
 * @type {!Object}
 * @inner
 */ var INT_CACHE = {};
/**
 * A cache of the Long representations of small unsigned integer values.
 * @type {!Object}
 * @inner
 */ var UINT_CACHE = {};
/**
 * @param {number} value
 * @param {boolean=} unsigned
 * @returns {!Long}
 * @inner
 */ function fromInt(value, unsigned) {
    var obj, cachedObj, cache;
    if (unsigned) {
        value >>>= 0;
        if (cache = 0 <= value && value < 256) {
            cachedObj = UINT_CACHE[value];
            if (cachedObj) return cachedObj;
        }
        obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);
        if (cache) UINT_CACHE[value] = obj;
        return obj;
    } else {
        value |= 0;
        if (cache = -128 <= value && value < 128) {
            cachedObj = INT_CACHE[value];
            if (cachedObj) return cachedObj;
        }
        obj = fromBits(value, value < 0 ? -1 : 0, false);
        if (cache) INT_CACHE[value] = obj;
        return obj;
    }
}
/**
 * Returns a Long representing the given 32 bit integer value.
 * @function
 * @param {number} value The 32 bit integer in question
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @returns {!Long} The corresponding Long value
 */ Long.fromInt = fromInt;
/**
 * @param {number} value
 * @param {boolean=} unsigned
 * @returns {!Long}
 * @inner
 */ function fromNumber(value, unsigned) {
    if (isNaN(value)) return unsigned ? UZERO : ZERO;
    if (unsigned) {
        if (value < 0) return UZERO;
        if (value >= TWO_PWR_64_DBL) return MAX_UNSIGNED_VALUE;
    } else {
        if (value <= -TWO_PWR_63_DBL) return MIN_VALUE;
        if (value + 1 >= TWO_PWR_63_DBL) return MAX_VALUE;
    }
    if (value < 0) return fromNumber(-value, unsigned).neg();
    return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);
}
/**
 * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned.
 * @function
 * @param {number} value The number in question
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @returns {!Long} The corresponding Long value
 */ Long.fromNumber = fromNumber;
/**
 * @param {number} lowBits
 * @param {number} highBits
 * @param {boolean=} unsigned
 * @returns {!Long}
 * @inner
 */ function fromBits(lowBits, highBits, unsigned) {
    return new Long(lowBits, highBits, unsigned);
}
/**
 * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is
 *  assumed to use 32 bits.
 * @function
 * @param {number} lowBits The low 32 bits
 * @param {number} highBits The high 32 bits
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @returns {!Long} The corresponding Long value
 */ Long.fromBits = fromBits;
/**
 * @function
 * @param {number} base
 * @param {number} exponent
 * @returns {number}
 * @inner
 */ var pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4)
/**
 * @param {string} str
 * @param {(boolean|number)=} unsigned
 * @param {number=} radix
 * @returns {!Long}
 * @inner
 */ function fromString(str, unsigned, radix) {
    if (str.length === 0) throw Error("empty string");
    if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity") return ZERO;
    if (typeof unsigned === "number") // For goog.math.long compatibility
    radix = unsigned, unsigned = false;
    else unsigned = !!unsigned;
    radix = radix || 10;
    if (radix < 2 || 36 < radix) throw RangeError("radix");
    var p;
    if ((p = str.indexOf("-")) > 0) throw Error("interior hyphen");
    else if (p === 0) return fromString(str.substring(1), unsigned, radix).neg();
    // Do several (8) digits each time through the loop, so as to
    // minimize the calls to the very expensive emulated div.
    var radixToPower = fromNumber(pow_dbl(radix, 8));
    var result = ZERO;
    for(var i = 0; i < str.length; i += 8){
        var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix);
        if (size < 8) {
            var power = fromNumber(pow_dbl(radix, size));
            result = result.mul(power).add(fromNumber(value));
        } else {
            result = result.mul(radixToPower);
            result = result.add(fromNumber(value));
        }
    }
    result.unsigned = unsigned;
    return result;
}
/**
 * Returns a Long representation of the given string, written using the specified radix.
 * @function
 * @param {string} str The textual representation of the Long
 * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed
 * @param {number=} radix The radix in which the text is written (2-36), defaults to 10
 * @returns {!Long} The corresponding Long value
 */ Long.fromString = fromString;
/**
 * @function
 * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val
 * @param {boolean=} unsigned
 * @returns {!Long}
 * @inner
 */ function fromValue(val, unsigned) {
    if (typeof val === "number") return fromNumber(val, unsigned);
    if (typeof val === "string") return fromString(val, unsigned);
    // Throws for non-objects, converts non-instanceof Long:
    return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned);
}
/**
 * Converts the specified value to a Long using the appropriate from* function for its type.
 * @function
 * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @returns {!Long}
 */ Long.fromValue = fromValue;
// NOTE: the compiler should inline these constant values below and then remove these variables, so there should be
// no runtime penalty for these.
/**
 * @type {number}
 * @const
 * @inner
 */ var TWO_PWR_16_DBL = 65536;
/**
 * @type {number}
 * @const
 * @inner
 */ var TWO_PWR_24_DBL = 16777216;
/**
 * @type {number}
 * @const
 * @inner
 */ var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;
/**
 * @type {number}
 * @const
 * @inner
 */ var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;
/**
 * @type {number}
 * @const
 * @inner
 */ var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;
/**
 * @type {!Long}
 * @const
 * @inner
 */ var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);
/**
 * @type {!Long}
 * @inner
 */ var ZERO = fromInt(0);
/**
 * Signed zero.
 * @type {!Long}
 */ Long.ZERO = ZERO;
/**
 * @type {!Long}
 * @inner
 */ var UZERO = fromInt(0, true);
/**
 * Unsigned zero.
 * @type {!Long}
 */ Long.UZERO = UZERO;
/**
 * @type {!Long}
 * @inner
 */ var ONE = fromInt(1);
/**
 * Signed one.
 * @type {!Long}
 */ Long.ONE = ONE;
/**
 * @type {!Long}
 * @inner
 */ var UONE = fromInt(1, true);
/**
 * Unsigned one.
 * @type {!Long}
 */ Long.UONE = UONE;
/**
 * @type {!Long}
 * @inner
 */ var NEG_ONE = fromInt(-1);
/**
 * Signed negative one.
 * @type {!Long}
 */ Long.NEG_ONE = NEG_ONE;
/**
 * @type {!Long}
 * @inner
 */ var MAX_VALUE = fromBits(-1, 2147483647, false);
/**
 * Maximum signed value.
 * @type {!Long}
 */ Long.MAX_VALUE = MAX_VALUE;
/**
 * @type {!Long}
 * @inner
 */ var MAX_UNSIGNED_VALUE = fromBits(-1, -1, true);
/**
 * Maximum unsigned value.
 * @type {!Long}
 */ Long.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;
/**
 * @type {!Long}
 * @inner
 */ var MIN_VALUE = fromBits(0, -2147483648, false);
/**
 * Minimum signed value.
 * @type {!Long}
 */ Long.MIN_VALUE = MIN_VALUE;
/**
 * @alias Long.prototype
 * @inner
 */ var LongPrototype = Long.prototype;
/**
 * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer.
 * @returns {number}
 */ LongPrototype.toInt = function toInt() {
    return this.unsigned ? this.low >>> 0 : this.low;
};
/**
 * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa).
 * @returns {number}
 */ LongPrototype.toNumber = function toNumber() {
    if (this.unsigned) return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);
    return this.high * TWO_PWR_32_DBL + (this.low >>> 0);
};
/**
 * Converts the Long to a string written in the specified radix.
 * @param {number=} radix Radix (2-36), defaults to 10
 * @returns {string}
 * @override
 * @throws {RangeError} If `radix` is out of range
 */ LongPrototype.toString = function toString(radix) {
    radix = radix || 10;
    if (radix < 2 || 36 < radix) throw RangeError("radix");
    if (this.isZero()) return "0";
    if (this.isNegative()) {
        if (this.eq(MIN_VALUE)) {
            // We need to change the Long value before it can be negated, so we remove
            // the bottom-most digit in this base and then recurse to do the rest.
            var radixLong = fromNumber(radix), div = this.div(radixLong), rem1 = div.mul(radixLong).sub(this);
            return div.toString(radix) + rem1.toInt().toString(radix);
        } else return "-" + this.neg().toString(radix);
    }
    // Do several (6) digits each time through the loop, so as to
    // minimize the calls to the very expensive emulated div.
    var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;
    var result = "";
    while(true){
        var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);
        rem = remDiv;
        if (rem.isZero()) return digits + result;
        else {
            while(digits.length < 6)digits = "0" + digits;
            result = "" + digits + result;
        }
    }
};
/**
 * Gets the high 32 bits as a signed integer.
 * @returns {number} Signed high bits
 */ LongPrototype.getHighBits = function getHighBits() {
    return this.high;
};
/**
 * Gets the high 32 bits as an unsigned integer.
 * @returns {number} Unsigned high bits
 */ LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {
    return this.high >>> 0;
};
/**
 * Gets the low 32 bits as a signed integer.
 * @returns {number} Signed low bits
 */ LongPrototype.getLowBits = function getLowBits() {
    return this.low;
};
/**
 * Gets the low 32 bits as an unsigned integer.
 * @returns {number} Unsigned low bits
 */ LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {
    return this.low >>> 0;
};
/**
 * Gets the number of bits needed to represent the absolute value of this Long.
 * @returns {number}
 */ LongPrototype.getNumBitsAbs = function getNumBitsAbs() {
    if (this.isNegative()) return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();
    var val = this.high != 0 ? this.high : this.low;
    for(var bit = 31; bit > 0; bit--)if ((val & 1 << bit) != 0) break;
    return this.high != 0 ? bit + 33 : bit + 1;
};
/**
 * Tests if this Long's value equals zero.
 * @returns {boolean}
 */ LongPrototype.isZero = function isZero() {
    return this.high === 0 && this.low === 0;
};
/**
 * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}.
 * @returns {boolean}
 */ LongPrototype.eqz = LongPrototype.isZero;
/**
 * Tests if this Long's value is negative.
 * @returns {boolean}
 */ LongPrototype.isNegative = function isNegative() {
    return !this.unsigned && this.high < 0;
};
/**
 * Tests if this Long's value is positive.
 * @returns {boolean}
 */ LongPrototype.isPositive = function isPositive() {
    return this.unsigned || this.high >= 0;
};
/**
 * Tests if this Long's value is odd.
 * @returns {boolean}
 */ LongPrototype.isOdd = function isOdd() {
    return (this.low & 1) === 1;
};
/**
 * Tests if this Long's value is even.
 * @returns {boolean}
 */ LongPrototype.isEven = function isEven() {
    return (this.low & 1) === 0;
};
/**
 * Tests if this Long's value equals the specified's.
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.equals = function equals(other) {
    if (!isLong(other)) other = fromValue(other);
    if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1) return false;
    return this.high === other.high && this.low === other.low;
};
/**
 * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.eq = LongPrototype.equals;
/**
 * Tests if this Long's value differs from the specified's.
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.notEquals = function notEquals(other) {
    return !this.eq(/* validates */ other);
};
/**
 * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.neq = LongPrototype.notEquals;
/**
 * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.ne = LongPrototype.notEquals;
/**
 * Tests if this Long's value is less than the specified's.
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.lessThan = function lessThan(other) {
    return this.comp(/* validates */ other) < 0;
};
/**
 * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.lt = LongPrototype.lessThan;
/**
 * Tests if this Long's value is less than or equal the specified's.
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {
    return this.comp(/* validates */ other) <= 0;
};
/**
 * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.lte = LongPrototype.lessThanOrEqual;
/**
 * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.le = LongPrototype.lessThanOrEqual;
/**
 * Tests if this Long's value is greater than the specified's.
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.greaterThan = function greaterThan(other) {
    return this.comp(/* validates */ other) > 0;
};
/**
 * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.gt = LongPrototype.greaterThan;
/**
 * Tests if this Long's value is greater than or equal the specified's.
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {
    return this.comp(/* validates */ other) >= 0;
};
/**
 * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.gte = LongPrototype.greaterThanOrEqual;
/**
 * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {boolean}
 */ LongPrototype.ge = LongPrototype.greaterThanOrEqual;
/**
 * Compares this Long's value with the specified's.
 * @param {!Long|number|string} other Other value
 * @returns {number} 0 if they are the same, 1 if the this is greater and -1
 *  if the given one is greater
 */ LongPrototype.compare = function compare(other) {
    if (!isLong(other)) other = fromValue(other);
    if (this.eq(other)) return 0;
    var thisNeg = this.isNegative(), otherNeg = other.isNegative();
    if (thisNeg && !otherNeg) return -1;
    if (!thisNeg && otherNeg) return 1;
    // At this point the sign bits are the same
    if (!this.unsigned) return this.sub(other).isNegative() ? -1 : 1;
    // Both are positive if at least one is unsigned
    return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;
};
/**
 * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}.
 * @function
 * @param {!Long|number|string} other Other value
 * @returns {number} 0 if they are the same, 1 if the this is greater and -1
 *  if the given one is greater
 */ LongPrototype.comp = LongPrototype.compare;
/**
 * Negates this Long's value.
 * @returns {!Long} Negated Long
 */ LongPrototype.negate = function negate() {
    if (!this.unsigned && this.eq(MIN_VALUE)) return MIN_VALUE;
    return this.not().add(ONE);
};
/**
 * Negates this Long's value. This is an alias of {@link Long#negate}.
 * @function
 * @returns {!Long} Negated Long
 */ LongPrototype.neg = LongPrototype.negate;
/**
 * Returns the sum of this and the specified Long.
 * @param {!Long|number|string} addend Addend
 * @returns {!Long} Sum
 */ LongPrototype.add = function add(addend) {
    if (!isLong(addend)) addend = fromValue(addend);
    // Divide each number into 4 chunks of 16 bits, and then sum the chunks.
    var a48 = this.high >>> 16;
    var a32 = this.high & 0xFFFF;
    var a16 = this.low >>> 16;
    var a00 = this.low & 0xFFFF;
    var b48 = addend.high >>> 16;
    var b32 = addend.high & 0xFFFF;
    var b16 = addend.low >>> 16;
    var b00 = addend.low & 0xFFFF;
    var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
    c00 += a00 + b00;
    c16 += c00 >>> 16;
    c00 &= 0xFFFF;
    c16 += a16 + b16;
    c32 += c16 >>> 16;
    c16 &= 0xFFFF;
    c32 += a32 + b32;
    c48 += c32 >>> 16;
    c32 &= 0xFFFF;
    c48 += a48 + b48;
    c48 &= 0xFFFF;
    return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
};
/**
 * Returns the difference of this and the specified Long.
 * @param {!Long|number|string} subtrahend Subtrahend
 * @returns {!Long} Difference
 */ LongPrototype.subtract = function subtract(subtrahend) {
    if (!isLong(subtrahend)) subtrahend = fromValue(subtrahend);
    return this.add(subtrahend.neg());
};
/**
 * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}.
 * @function
 * @param {!Long|number|string} subtrahend Subtrahend
 * @returns {!Long} Difference
 */ LongPrototype.sub = LongPrototype.subtract;
/**
 * Returns the product of this and the specified Long.
 * @param {!Long|number|string} multiplier Multiplier
 * @returns {!Long} Product
 */ LongPrototype.multiply = function multiply(multiplier) {
    if (this.isZero()) return ZERO;
    if (!isLong(multiplier)) multiplier = fromValue(multiplier);
    // use wasm support if present
    if (wasm) {
        var low = wasm.mul(this.low, this.high, multiplier.low, multiplier.high);
        return fromBits(low, wasm.get_high(), this.unsigned);
    }
    if (multiplier.isZero()) return ZERO;
    if (this.eq(MIN_VALUE)) return multiplier.isOdd() ? MIN_VALUE : ZERO;
    if (multiplier.eq(MIN_VALUE)) return this.isOdd() ? MIN_VALUE : ZERO;
    if (this.isNegative()) {
        if (multiplier.isNegative()) return this.neg().mul(multiplier.neg());
        else return this.neg().mul(multiplier).neg();
    } else if (multiplier.isNegative()) return this.mul(multiplier.neg()).neg();
    // If both longs are small, use float multiplication
    if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);
    // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products.
    // We can skip products that would overflow.
    var a48 = this.high >>> 16;
    var a32 = this.high & 0xFFFF;
    var a16 = this.low >>> 16;
    var a00 = this.low & 0xFFFF;
    var b48 = multiplier.high >>> 16;
    var b32 = multiplier.high & 0xFFFF;
    var b16 = multiplier.low >>> 16;
    var b00 = multiplier.low & 0xFFFF;
    var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
    c00 += a00 * b00;
    c16 += c00 >>> 16;
    c00 &= 0xFFFF;
    c16 += a16 * b00;
    c32 += c16 >>> 16;
    c16 &= 0xFFFF;
    c16 += a00 * b16;
    c32 += c16 >>> 16;
    c16 &= 0xFFFF;
    c32 += a32 * b00;
    c48 += c32 >>> 16;
    c32 &= 0xFFFF;
    c32 += a16 * b16;
    c48 += c32 >>> 16;
    c32 &= 0xFFFF;
    c32 += a00 * b32;
    c48 += c32 >>> 16;
    c32 &= 0xFFFF;
    c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;
    c48 &= 0xFFFF;
    return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
};
/**
 * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}.
 * @function
 * @param {!Long|number|string} multiplier Multiplier
 * @returns {!Long} Product
 */ LongPrototype.mul = LongPrototype.multiply;
/**
 * Returns this Long divided by the specified. The result is signed if this Long is signed or
 *  unsigned if this Long is unsigned.
 * @param {!Long|number|string} divisor Divisor
 * @returns {!Long} Quotient
 */ LongPrototype.divide = function divide(divisor) {
    if (!isLong(divisor)) divisor = fromValue(divisor);
    if (divisor.isZero()) throw Error("division by zero");
    // use wasm support if present
    if (wasm) {
        // guard against signed division overflow: the largest
        // negative number / -1 would be 1 larger than the largest
        // positive number, due to two's complement.
        if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) // be consistent with non-wasm code path
        return this;
        var low = (this.unsigned ? wasm.div_u : wasm.div_s)(this.low, this.high, divisor.low, divisor.high);
        return fromBits(low, wasm.get_high(), this.unsigned);
    }
    if (this.isZero()) return this.unsigned ? UZERO : ZERO;
    var approx, rem, res;
    if (!this.unsigned) {
        // This section is only relevant for signed longs and is derived from the
        // closure library as a whole.
        if (this.eq(MIN_VALUE)) {
            if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) return MIN_VALUE; // recall that -MIN_VALUE == MIN_VALUE
            else if (divisor.eq(MIN_VALUE)) return ONE;
            else {
                // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|.
                var halfThis = this.shr(1);
                approx = halfThis.div(divisor).shl(1);
                if (approx.eq(ZERO)) return divisor.isNegative() ? ONE : NEG_ONE;
                else {
                    rem = this.sub(divisor.mul(approx));
                    res = approx.add(rem.div(divisor));
                    return res;
                }
            }
        } else if (divisor.eq(MIN_VALUE)) return this.unsigned ? UZERO : ZERO;
        if (this.isNegative()) {
            if (divisor.isNegative()) return this.neg().div(divisor.neg());
            return this.neg().div(divisor).neg();
        } else if (divisor.isNegative()) return this.div(divisor.neg()).neg();
        res = ZERO;
    } else {
        // The algorithm below has not been made for unsigned longs. It's therefore
        // required to take special care of the MSB prior to running it.
        if (!divisor.unsigned) divisor = divisor.toUnsigned();
        if (divisor.gt(this)) return UZERO;
        if (divisor.gt(this.shru(1))) return UONE;
        res = UZERO;
    }
    // Repeat the following until the remainder is less than other:  find a
    // floating-point that approximates remainder / other *from below*, add this
    // into the result, and subtract it from the remainder.  It is critical that
    // the approximate value is less than or equal to the real value so that the
    // remainder never becomes negative.
    rem = this;
    while(rem.gte(divisor)){
        // Approximate the result of division. This may be a little greater or
        // smaller than the actual value.
        approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));
        // We will tweak the approximate result by changing it in the 48-th digit or
        // the smallest non-fractional digit, whichever is larger.
        var log2 = Math.ceil(Math.log(approx) / Math.LN2), delta = log2 <= 48 ? 1 : pow_dbl(2, log2 - 48), // Decrease the approximation until it is smaller than the remainder.  Note
        // that if it is too large, the product overflows and is negative.
        approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);
        while(approxRem.isNegative() || approxRem.gt(rem)){
            approx -= delta;
            approxRes = fromNumber(approx, this.unsigned);
            approxRem = approxRes.mul(divisor);
        }
        // We know the answer can't be zero... and actually, zero would cause
        // infinite recursion since we would make no progress.
        if (approxRes.isZero()) approxRes = ONE;
        res = res.add(approxRes);
        rem = rem.sub(approxRem);
    }
    return res;
};
/**
 * Returns this Long divided by the specified. This is an alias of {@link Long#divide}.
 * @function
 * @param {!Long|number|string} divisor Divisor
 * @returns {!Long} Quotient
 */ LongPrototype.div = LongPrototype.divide;
/**
 * Returns this Long modulo the specified.
 * @param {!Long|number|string} divisor Divisor
 * @returns {!Long} Remainder
 */ LongPrototype.modulo = function modulo(divisor) {
    if (!isLong(divisor)) divisor = fromValue(divisor);
    // use wasm support if present
    if (wasm) {
        var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(this.low, this.high, divisor.low, divisor.high);
        return fromBits(low, wasm.get_high(), this.unsigned);
    }
    return this.sub(this.div(divisor).mul(divisor));
};
/**
 * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.
 * @function
 * @param {!Long|number|string} divisor Divisor
 * @returns {!Long} Remainder
 */ LongPrototype.mod = LongPrototype.modulo;
/**
 * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}.
 * @function
 * @param {!Long|number|string} divisor Divisor
 * @returns {!Long} Remainder
 */ LongPrototype.rem = LongPrototype.modulo;
/**
 * Returns the bitwise NOT of this Long.
 * @returns {!Long}
 */ LongPrototype.not = function not() {
    return fromBits(~this.low, ~this.high, this.unsigned);
};
/**
 * Returns the bitwise AND of this Long and the specified.
 * @param {!Long|number|string} other Other Long
 * @returns {!Long}
 */ LongPrototype.and = function and(other) {
    if (!isLong(other)) other = fromValue(other);
    return fromBits(this.low & other.low, this.high & other.high, this.unsigned);
};
/**
 * Returns the bitwise OR of this Long and the specified.
 * @param {!Long|number|string} other Other Long
 * @returns {!Long}
 */ LongPrototype.or = function or(other) {
    if (!isLong(other)) other = fromValue(other);
    return fromBits(this.low | other.low, this.high | other.high, this.unsigned);
};
/**
 * Returns the bitwise XOR of this Long and the given one.
 * @param {!Long|number|string} other Other Long
 * @returns {!Long}
 */ LongPrototype.xor = function xor(other) {
    if (!isLong(other)) other = fromValue(other);
    return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);
};
/**
 * Returns this Long with bits shifted to the left by the given amount.
 * @param {number|!Long} numBits Number of bits
 * @returns {!Long} Shifted Long
 */ LongPrototype.shiftLeft = function shiftLeft(numBits) {
    if (isLong(numBits)) numBits = numBits.toInt();
    if ((numBits &= 63) === 0) return this;
    else if (numBits < 32) return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);
    else return fromBits(0, this.low << numBits - 32, this.unsigned);
};
/**
 * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}.
 * @function
 * @param {number|!Long} numBits Number of bits
 * @returns {!Long} Shifted Long
 */ LongPrototype.shl = LongPrototype.shiftLeft;
/**
 * Returns this Long with bits arithmetically shifted to the right by the given amount.
 * @param {number|!Long} numBits Number of bits
 * @returns {!Long} Shifted Long
 */ LongPrototype.shiftRight = function shiftRight(numBits) {
    if (isLong(numBits)) numBits = numBits.toInt();
    if ((numBits &= 63) === 0) return this;
    else if (numBits < 32) return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);
    else return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);
};
/**
 * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}.
 * @function
 * @param {number|!Long} numBits Number of bits
 * @returns {!Long} Shifted Long
 */ LongPrototype.shr = LongPrototype.shiftRight;
/**
 * Returns this Long with bits logically shifted to the right by the given amount.
 * @param {number|!Long} numBits Number of bits
 * @returns {!Long} Shifted Long
 */ LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {
    if (isLong(numBits)) numBits = numBits.toInt();
    numBits &= 63;
    if (numBits === 0) return this;
    else {
        var high = this.high;
        if (numBits < 32) {
            var low = this.low;
            return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);
        } else if (numBits === 32) return fromBits(high, 0, this.unsigned);
        else return fromBits(high >>> numBits - 32, 0, this.unsigned);
    }
};
/**
 * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.
 * @function
 * @param {number|!Long} numBits Number of bits
 * @returns {!Long} Shifted Long
 */ LongPrototype.shru = LongPrototype.shiftRightUnsigned;
/**
 * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}.
 * @function
 * @param {number|!Long} numBits Number of bits
 * @returns {!Long} Shifted Long
 */ LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;
/**
 * Converts this Long to signed.
 * @returns {!Long} Signed long
 */ LongPrototype.toSigned = function toSigned() {
    if (!this.unsigned) return this;
    return fromBits(this.low, this.high, false);
};
/**
 * Converts this Long to unsigned.
 * @returns {!Long} Unsigned long
 */ LongPrototype.toUnsigned = function toUnsigned() {
    if (this.unsigned) return this;
    return fromBits(this.low, this.high, true);
};
/**
 * Converts this Long to its byte representation.
 * @param {boolean=} le Whether little or big endian, defaults to big endian
 * @returns {!Array.<number>} Byte representation
 */ LongPrototype.toBytes = function toBytes(le) {
    return le ? this.toBytesLE() : this.toBytesBE();
};
/**
 * Converts this Long to its little endian byte representation.
 * @returns {!Array.<number>} Little endian byte representation
 */ LongPrototype.toBytesLE = function toBytesLE() {
    var hi = this.high, lo = this.low;
    return [
        lo & 0xff,
        lo >>> 8 & 0xff,
        lo >>> 16 & 0xff,
        lo >>> 24,
        hi & 0xff,
        hi >>> 8 & 0xff,
        hi >>> 16 & 0xff,
        hi >>> 24
    ];
};
/**
 * Converts this Long to its big endian byte representation.
 * @returns {!Array.<number>} Big endian byte representation
 */ LongPrototype.toBytesBE = function toBytesBE() {
    var hi = this.high, lo = this.low;
    return [
        hi >>> 24,
        hi >>> 16 & 0xff,
        hi >>> 8 & 0xff,
        hi & 0xff,
        lo >>> 24,
        lo >>> 16 & 0xff,
        lo >>> 8 & 0xff,
        lo & 0xff
    ];
};
/**
 * Creates a Long from its byte representation.
 * @param {!Array.<number>} bytes Byte representation
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @param {boolean=} le Whether little or big endian, defaults to big endian
 * @returns {Long} The corresponding Long value
 */ Long.fromBytes = function fromBytes(bytes, unsigned, le) {
    return le ? Long.fromBytesLE(bytes, unsigned) : Long.fromBytesBE(bytes, unsigned);
};
/**
 * Creates a Long from its little endian byte representation.
 * @param {!Array.<number>} bytes Little endian byte representation
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @returns {Long} The corresponding Long value
 */ Long.fromBytesLE = function fromBytesLE(bytes, unsigned) {
    return new Long(bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24, bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24, unsigned);
};
/**
 * Creates a Long from its big endian byte representation.
 * @param {!Array.<number>} bytes Big endian byte representation
 * @param {boolean=} unsigned Whether unsigned or not, defaults to signed
 * @returns {Long} The corresponding Long value
 */ Long.fromBytesBE = function fromBytesBE(bytes, unsigned) {
    return new Long(bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7], bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3], unsigned);
};

},{}],"cUi5z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Computes a list of TapeNodes that connect x to y, filtering everything else
 * out and preserving the order of the original tape elements.
 *
 * @param tape The tape elements to filter.
 * @param xs The input Tensors.
 * @param y The output Tensor.
 */ parcelHelpers.export(exports, "getFilteredNodesXToY", ()=>getFilteredNodesXToY);
/**
 * Backpropagate gradients through the filtered TapeNodes.
 *
 * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map
 * is mutated by this method.
 * @param filteredTape The filtered TapeNodes to backprop through.
 */ parcelHelpers.export(exports, "backpropagateGradients", ()=>backpropagateGradients);
var _util = require("./util");
function getFilteredNodesXToY(tape, xs, y) {
    // Forward pass to compute all the nodes and Tensors that are transitively a
    // function of x.
    const tensorsFromX = {};
    const nodesFromX = {};
    for(let i = 0; i < xs.length; i++)tensorsFromX[xs[i].id] = true;
    for(let i = 0; i < tape.length; i++){
        const node = tape[i];
        const nodeInputs = node.inputs;
        for(const inputName in nodeInputs){
            const input = nodeInputs[inputName];
            let anyInputFromX = false;
            for(let j = 0; j < xs.length; j++)if (tensorsFromX[input.id]) {
                node.outputs.forEach((output)=>tensorsFromX[output.id] = true);
                anyInputFromX = true;
                nodesFromX[node.id] = true;
                break;
            }
            if (anyInputFromX) break;
        }
    }
    // Backward pass to find all of the nodes and Tensors that lead to y.
    const tensorsLeadToY = {};
    tensorsLeadToY[y.id] = true;
    const nodesToY = {};
    for(let i = tape.length - 1; i >= 0; i--){
        const node = tape[i];
        const nodeInputs = node.inputs;
        // If any of the outputs lead to y, mark all of the inputs as leading to y.
        for(let j = 0; j < node.outputs.length; j++)if (tensorsLeadToY[node.outputs[j].id]) {
            for(const inputName in nodeInputs){
                tensorsLeadToY[nodeInputs[inputName].id] = true;
                nodesToY[node.id] = true;
            }
            break;
        }
    }
    // Return the paths that come from x and lead to y.
    const filteredTape = [];
    for(let i = 0; i < tape.length; i++){
        const node = tape[i];
        if (nodesFromX[node.id] && nodesToY[node.id]) {
            // Prune the inputs from the node that aren't a function of x.
            const prunedInputs = {};
            for(const inputName in node.inputs){
                const nodeInput = node.inputs[inputName];
                if (tensorsFromX[nodeInput.id]) prunedInputs[inputName] = nodeInput;
            }
            // Copy the node and overwrite inputsAndArgs to the pruned version.
            const prunedNode = Object.assign({}, node);
            prunedNode.inputs = prunedInputs;
            prunedNode.outputs = node.outputs;
            filteredTape.push(prunedNode);
        }
    }
    return filteredTape;
}
function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) {
    // Walk the tape backward and keep a map of Tensor to its gradient.
    for(let i = filteredTape.length - 1; i >= 0; i--){
        const node = filteredTape[i];
        const dys = [];
        node.outputs.forEach((o)=>{
            const gradTensor = tensorAccumulatedGradientMap[o.id];
            if (gradTensor != null) dys.push(gradTensor);
            else // This particular output is not in the back-propagation subgraph, so it
            // does not affect the final output, thus we put null for its dy.
            dys.push(null);
        });
        if (node.gradient == null) throw new Error(`Cannot compute gradient: gradient function not found ` + `for ${node.kernelName}.`);
        // Backprop dy through this node and accumulate gradients over the inputs.
        const inputGradients = node.gradient(dys);
        for(const inputName in node.inputs){
            if (!(inputName in inputGradients)) throw new Error(`Cannot backprop through input ${inputName}. ` + `Available gradients found: ${Object.keys(inputGradients)}.`);
            // Call the gradient function.
            const dx = tidy(()=>inputGradients[inputName]());
            if (dx.dtype !== "float32") throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` + `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);
            const x = node.inputs[inputName];
            if (!_util.arraysEqual(dx.shape, x.shape)) throw new Error(`Error in gradient for op ${node.kernelName}. The gradient of input ` + `'${inputName}' has shape '${dx.shape}', which does not match ` + `the shape of the input '${x.shape}'`);
            if (tensorAccumulatedGradientMap[x.id] == null) tensorAccumulatedGradientMap[x.id] = dx;
            else {
                const curGradient = tensorAccumulatedGradientMap[x.id];
                tensorAccumulatedGradientMap[x.id] = add(curGradient, dx);
                curGradient.dispose();
            }
        }
    }
}

},{"./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cJNoh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Workaround for: https://github.com/bazelbuild/rules_nodejs/issues/1265
/// <reference types="@webgpu/types/dist" />
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * A mutable object, similar to `tf.Tensor`, that allows users to set values
 * at locations before converting to an immutable `tf.Tensor`.
 *
 * See `tf.buffer` for creating a tensor buffer.
 *
 * @doc {heading: 'Tensors', subheading: 'Classes'}
 */ parcelHelpers.export(exports, "TensorBuffer", ()=>TensorBuffer);
/**
 * An external consumer can register itself as the tensor tracker. This way
 * the Tensor class can notify the tracker for every tensor created and
 * disposed.
 */ parcelHelpers.export(exports, "setTensorTracker", ()=>setTensorTracker);
/**
 * An external consumer can register itself as the op handler. This way the
 * Tensor class can have chaining methods that call into ops via the op
 * handler.
 */ parcelHelpers.export(exports, "setOpHandler", ()=>setOpHandler);
/**
 * Sets the deprecation warning function to be used by this file. This way the
 * Tensor class can be a leaf but still use the environment.
 */ parcelHelpers.export(exports, "setDeprecationWarningFn", ()=>setDeprecationWarningFn);
/**
 * A `tf.Tensor` object represents an immutable, multidimensional array of
 * numbers that has a shape and a data type.
 *
 * For performance reasons, functions that create tensors do not necessarily
 * perform a copy of the data passed to them (e.g. if the data is passed as a
 * `Float32Array`), and changes to the data will change the tensor. This is not
 * a feature and is not supported. To avoid this behavior, use the tensor before
 * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`.
 *
 * See `tf.tensor` for details on how to create a `tf.Tensor`.
 *
 * @doc {heading: 'Tensors', subheading: 'Classes'}
 */ parcelHelpers.export(exports, "Tensor", ()=>Tensor);
parcelHelpers.export(exports, "getGlobalTensorClass", ()=>getGlobalTensorClass);
/**
 * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
 *
 * @doc {heading: 'Tensors', subheading: 'Classes'}
 */ parcelHelpers.export(exports, "Variable", ()=>Variable);
var _globalUtil = require("./global_util");
var _tensorFormat = require("./tensor_format");
var _util = require("./util");
class TensorBuffer {
    constructor(shape, dtype, values){
        this.dtype = dtype;
        this.shape = shape.slice();
        this.size = _util.sizeFromShape(shape);
        if (values != null) {
            const n = values.length;
            _util.assert(n === this.size, ()=>`Length of values '${n}' does not match the size ` + `inferred by the shape '${this.size}'.`);
        }
        if (dtype === "complex64") throw new Error(`complex64 dtype TensorBuffers are not supported. Please create ` + `a TensorBuffer for the real and imaginary parts separately and ` + `call tf.complex(real, imag).`);
        this.values = values || _util.getArrayFromDType(dtype, this.size);
        this.strides = (0, _util.computeStrides)(shape);
    }
    /**
     * Sets a value in the buffer at a given location.
     *
     * @param value The value to set.
     * @param locs  The location indices.
     *
     * @doc {heading: 'Tensors', subheading: 'Creation'}
     */ set(value, ...locs) {
        if (locs.length === 0) locs = [
            0
        ];
        _util.assert(locs.length === this.rank, ()=>`The number of provided coordinates (${locs.length}) must ` + `match the rank (${this.rank})`);
        const index = this.locToIndex(locs);
        this.values[index] = value;
    }
    /**
     * Returns the value in the buffer at the provided location.
     *
     * @param locs The location indices.
     *
     * @doc {heading: 'Tensors', subheading: 'Creation'}
     */ get(...locs) {
        if (locs.length === 0) locs = [
            0
        ];
        let i = 0;
        for (const loc of locs){
            if (loc < 0 || loc >= this.shape[i]) {
                const msg = `Requested out of range element at ${locs}. ` + `  Buffer shape=${this.shape}`;
                throw new Error(msg);
            }
            i++;
        }
        let index = locs[locs.length - 1];
        for(let i = 0; i < locs.length - 1; ++i)index += this.strides[i] * locs[i];
        return this.values[index];
    }
    locToIndex(locs) {
        if (this.rank === 0) return 0;
        else if (this.rank === 1) return locs[0];
        let index = locs[locs.length - 1];
        for(let i = 0; i < locs.length - 1; ++i)index += this.strides[i] * locs[i];
        return index;
    }
    indexToLoc(index) {
        if (this.rank === 0) return [];
        else if (this.rank === 1) return [
            index
        ];
        const locs = new Array(this.shape.length);
        for(let i = 0; i < locs.length - 1; ++i){
            locs[i] = Math.floor(index / this.strides[i]);
            index -= locs[i] * this.strides[i];
        }
        locs[locs.length - 1] = index;
        return locs;
    }
    get rank() {
        return this.shape.length;
    }
    /**
     * Creates an immutable `tf.Tensor` object from the buffer.
     *
     * @doc {heading: 'Tensors', subheading: 'Creation'}
     */ toTensor() {
        return trackerFn().makeTensor(this.values, this.shape, this.dtype);
    }
}
// For tracking tensor creation and disposal.
let trackerFn = null;
// Used by chaining methods to call into ops.
let opHandler = null;
// Used to warn about deprecated methods.
let deprecationWarningFn = null;
function setTensorTracker(fn) {
    trackerFn = fn;
}
function setOpHandler(handler) {
    opHandler = handler;
}
function setDeprecationWarningFn(fn) {
    deprecationWarningFn = fn;
}
class Tensor {
    constructor(shape, dtype, dataId, id){
        /** Whether this tensor has been globally kept. */ this.kept = false;
        this.isDisposedInternal = false;
        this.shape = shape.slice();
        this.dtype = dtype || "float32";
        this.size = _util.sizeFromShape(shape);
        this.strides = (0, _util.computeStrides)(shape);
        this.dataId = dataId;
        this.id = id;
        this.rankType = this.rank < 5 ? this.rank.toString() : "higher";
    }
    get rank() {
        return this.shape.length;
    }
    /**
     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ async buffer() {
        const vals = await this.data();
        return opHandler.buffer(this.shape, this.dtype, vals);
    }
    /**
     * Returns a `tf.TensorBuffer` that holds the underlying data.
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ bufferSync() {
        return opHandler.buffer(this.shape, this.dtype, this.dataSync());
    }
    /**
     * Returns the tensor data as a nested array. The transfer of data is done
     * asynchronously.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ async array() {
        const vals = await this.data();
        return (0, _util.toNestedArray)(this.shape, vals, this.dtype === "complex64");
    }
    /**
     * Returns the tensor data as a nested array. The transfer of data is done
     * synchronously.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ arraySync() {
        return (0, _util.toNestedArray)(this.shape, this.dataSync(), this.dtype === "complex64");
    }
    /**
     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
     * promise of `TypedArray` that resolves when the computation has finished.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ async data() {
        this.throwIfDisposed();
        const data = trackerFn().read(this.dataId);
        if (this.dtype === "string") {
            const bytes = await data;
            try {
                return bytes.map((b)=>_util.decodeString(b));
            } catch (_a) {
                throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
            }
        }
        return data;
    }
    /**
     * Copy the tensor's data to a new GPU resource. Comparing to the `dataSync()`
     * and `data()`, this method prevents data from being downloaded to CPU.
     *
     * For WebGL backend, the data will be stored on a densely packed texture.
     * This means that the texture will use the RGBA channels to store value.
     *
     * For WebGPU backend, the data will be stored on a buffer. There is no
     * parameter, so can not use a user-defined size to create the buffer.
     *
     * @param options:
     *     For WebGL,
     *         - customTexShape: Optional. If set, will use the user defined
     *     texture shape to create the texture.
     *
     * @returns For WebGL backend, a GPUData contains the new texture and
     *     its information.
     *     {
     *        tensorRef: The tensor that is associated with this texture,
     *        texture: WebGLTexture,
     *        texShape: [number, number] // [height, width]
     *     }
     *
     *     For WebGPU backend, a GPUData contains the new buffer.
     *     {
     *        tensorRef: The tensor that is associated with this buffer,
     *        buffer: GPUBuffer,
     *     }
     *
     *     Remember to dispose the GPUData after it is used by
     *     `res.tensorRef.dispose()`.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ dataToGPU(options) {
        this.throwIfDisposed();
        return trackerFn().readToGPU(this.dataId, options);
    }
    /**
     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
     * UI thread until the values are ready, which can cause performance issues.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ dataSync() {
        this.throwIfDisposed();
        const data = trackerFn().readSync(this.dataId);
        if (this.dtype === "string") try {
            return data.map((b)=>_util.decodeString(b));
        } catch (_a) {
            throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().");
        }
        return data;
    }
    /** Returns the underlying bytes of the tensor's data. */ async bytes() {
        this.throwIfDisposed();
        const data = await trackerFn().read(this.dataId);
        if (this.dtype === "string") return data;
        else return new Uint8Array(data.buffer);
    }
    /**
     * Disposes `tf.Tensor` from memory.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ dispose() {
        if (this.isDisposed) return;
        if (this.kerasMask) this.kerasMask.dispose();
        trackerFn().disposeTensor(this);
        this.isDisposedInternal = true;
    }
    get isDisposed() {
        return this.isDisposedInternal;
    }
    throwIfDisposed() {
        if (this.isDisposed) throw new Error(`Tensor is disposed.`);
    }
    /**
     * Prints the `tf.Tensor`. See `tf.print` for details.
     *
     * @param verbose Whether to print verbose information about the tensor,
     *    including dtype and size.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ print(verbose = false) {
        return opHandler.print(this, verbose);
    }
    /**
     * Returns a copy of the tensor. See `tf.clone` for details.
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ clone() {
        this.throwIfDisposed();
        return opHandler.clone(this);
    }
    /**
     * Returns a human-readable description of the tensor. Useful for logging.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ toString(verbose = false) {
        const vals = this.dataSync();
        return (0, _tensorFormat.tensorToString)(vals, this.shape, this.dtype, verbose);
    }
    cast(dtype) {
        this.throwIfDisposed();
        return opHandler.cast(this, dtype);
    }
    variable(trainable = true, name, dtype) {
        this.throwIfDisposed();
        return trackerFn().makeVariable(this, trainable, name, dtype);
    }
}
Object.defineProperty(Tensor, Symbol.hasInstance, {
    value: (instance)=>{
        // Implementation note: we should use properties of the object that will be
        // defined before the constructor body has finished executing (methods).
        // This is because when this code is transpiled by babel, babel will call
        // classCallCheck before the constructor body is run.
        // See https://github.com/tensorflow/tfjs/issues/3384 for backstory.
        return !!instance && instance.data != null && instance.dataSync != null && instance.throwIfDisposed != null;
    }
});
function getGlobalTensorClass() {
    // Use getGlobal so that we can augment the Tensor class across package
    // boundaries becase the node resolution alg may result in different modules
    // being returned for this file depending on the path they are loaded from.
    return (0, _globalUtil.getGlobal)("Tensor", ()=>{
        return Tensor;
    });
}
// Global side effect. Cache global reference to Tensor class
getGlobalTensorClass();
class Variable extends Tensor {
    constructor(initialValue, trainable, name, tensorId){
        super(initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId);
        this.trainable = trainable;
        this.name = name;
    }
    /**
     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
     * the same shape and dtype as the old `tf.Tensor`.
     *
     * @param newValue New tensor to be assigned to this variable.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */ assign(newValue) {
        if (newValue.dtype !== this.dtype) throw new Error(`dtype of the new value (${newValue.dtype}) and ` + `previous value (${this.dtype}) must match`);
        if (!_util.arraysEqual(newValue.shape, this.shape)) throw new Error(`shape of the new value (${newValue.shape}) and ` + `previous value (${this.shape}) must match`);
        trackerFn().disposeTensor(this);
        this.dataId = newValue.dataId;
        trackerFn().incRef(this, null);
    }
    dispose() {
        trackerFn().disposeVariable(this);
        this.isDisposedInternal = true;
    }
}
Object.defineProperty(Variable, Symbol.hasInstance, {
    value: (instance)=>{
        return instance instanceof Tensor && instance.assign != null && instance.assign instanceof Function;
    }
});

},{"./global_util":"82SAe","./tensor_format":"k2Ex9","./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"k2Ex9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tensorToString", ()=>tensorToString);
var _util = require("./util");
// Maximum number of values before we decide to show ellipsis.
const FORMAT_LIMIT_NUM_VALS = 20;
// Number of first and last values to show when displaying a, b,...,y, z.
const FORMAT_NUM_FIRST_LAST_VALS = 3;
// Number of significant digits to show.
const FORMAT_NUM_SIG_DIGITS = 7;
function tensorToString(vals, shape, dtype, verbose) {
    const strides = (0, _util.computeStrides)(shape);
    const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
    const rank = shape.length;
    const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
    const lines = [
        "Tensor"
    ];
    if (verbose) {
        lines.push(`  dtype: ${dtype}`);
        lines.push(`  rank: ${rank}`);
        lines.push(`  shape: [${shape}]`);
        lines.push(`  values:`);
    }
    lines.push(valsLines.map((l)=>"    " + l).join("\n"));
    return lines.join("\n");
}
function computeMaxSizePerColumn(vals, shape, dtype, strides) {
    const n = (0, _util.sizeFromShape)(shape);
    const numCols = strides[strides.length - 1];
    const padPerCol = new Array(numCols).fill(0);
    const rank = shape.length;
    const valuesOrTuples = dtype === "complex64" ? createComplexTuples(vals) : vals;
    if (rank > 1) for(let row = 0; row < n / numCols; row++){
        const offset = row * numCols;
        for(let j = 0; j < numCols; j++)padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length);
    }
    return padPerCol;
}
function valToString(val, pad, dtype) {
    let valStr;
    if (Array.isArray(val)) valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` + `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;
    else if ((0, _util.isString)(val)) valStr = `'${val}'`;
    else if (dtype === "bool") valStr = boolNumToString(val);
    else valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
    return (0, _util.rightPad)(valStr, pad);
}
function boolNumToString(v) {
    return v === 0 ? "false" : "true";
}
function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast = true) {
    const storagePerElement = dtype === "complex64" ? 2 : 1;
    const size = shape[0];
    const rank = shape.length;
    if (rank === 0) {
        if (dtype === "complex64") {
            const complexTuple = createComplexTuples(vals);
            return [
                valToString(complexTuple[0], 0, dtype)
            ];
        }
        if (dtype === "bool") return [
            boolNumToString(vals[0])
        ];
        return [
            vals[0].toString()
        ];
    }
    if (rank === 1) {
        if (size > FORMAT_LIMIT_NUM_VALS) {
            const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
            let firstVals = Array.from(vals.slice(0, firstValsSize));
            let lastVals = Array.from(vals.slice((size - FORMAT_NUM_FIRST_LAST_VALS) * storagePerElement, size * storagePerElement));
            if (dtype === "complex64") {
                firstVals = createComplexTuples(firstVals);
                lastVals = createComplexTuples(lastVals);
            }
            return [
                "[" + firstVals.map((x, i)=>valToString(x, padPerCol[i], dtype)).join(", ") + ", ..., " + lastVals.map((x, i)=>valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype)).join(", ") + "]"
            ];
        }
        const displayVals = dtype === "complex64" ? createComplexTuples(vals) : Array.from(vals);
        return [
            "[" + displayVals.map((x, i)=>valToString(x, padPerCol[i], dtype)).join(", ") + "]"
        ];
    }
    // The array is rank 2 or more.
    const subshape = shape.slice(1);
    const substrides = strides.slice(1);
    const stride = strides[0] * storagePerElement;
    const lines = [];
    if (size > FORMAT_LIMIT_NUM_VALS) {
        for(let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++){
            const start = i * stride;
            const end = start + stride;
            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
        }
        lines.push("...");
        for(let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++){
            const start = i * stride;
            const end = start + stride;
            lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */ ));
        }
    } else for(let i = 0; i < size; i++){
        const start = i * stride;
        const end = start + stride;
        lines.push(...subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */ ));
    }
    const sep = rank === 2 ? "," : "";
    lines[0] = "[" + (size > 0 ? lines[0] + sep : "");
    for(let i = 1; i < lines.length - 1; i++)lines[i] = " " + lines[i] + sep;
    let newLineSep = ",\n";
    for(let i = 2; i < rank; i++)newLineSep += "\n";
    lines[lines.length - 1] = " " + lines[lines.length - 1] + "]" + (isLast ? "" : newLineSep);
    return lines;
}
function createComplexTuples(vals) {
    const complexTuples = [];
    for(let i = 0; i < vals.length; i += 2)complexTuples.push([
        vals[i],
        vals[i + 1]
    ]);
    return complexTuples;
}

},{"./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8jzJG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "makeTypesMatch", ()=>makeTypesMatch);
parcelHelpers.export(exports, "assertTypesMatch", ()=>assertTypesMatch);
parcelHelpers.export(exports, "isTensorInList", ()=>isTensorInList);
/**
 * Extracts any `Tensor`s found within the provided object.
 *
 * @param container an object that may be a `Tensor` or may directly contain
 *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it
 *   is safe to pass any object here, except that `Promise`s are not
 *   supported.
 * @returns An array of `Tensors` found within the passed object. If the
 *   argument is simply a `Tensor', a list containing that `Tensor` is
 *   returned. If the object is not a `Tensor` or does not
 *   contain `Tensors`, an empty list is returned.
 */ parcelHelpers.export(exports, "getTensorsInContainer", ()=>getTensorsInContainer);
var _tensor = require("./tensor");
var _types = require("./types");
var _util = require("./util");
function makeTypesMatch(a, b) {
    if (a.dtype === b.dtype) return [
        a,
        b
    ];
    const dtype = (0, _types.upcastType)(a.dtype, b.dtype);
    return [
        a.cast(dtype),
        b.cast(dtype)
    ];
}
function assertTypesMatch(a, b) {
    (0, _util.assert)(a.dtype === b.dtype, ()=>`The dtypes of the first(${a.dtype}) and` + ` second(${b.dtype}) input must match`);
}
function isTensorInList(tensor, tensorList) {
    return tensorList.some((x)=>x.id === tensor.id);
}
function getTensorsInContainer(result) {
    const list = [];
    const seen = new Set();
    walkTensorContainer(result, list, seen);
    return list;
}
function walkTensorContainer(container, list, seen) {
    if (container == null) return;
    if (container instanceof (0, _tensor.Tensor)) {
        list.push(container);
        return;
    }
    if (!isIterable(container)) return;
    // Iteration over keys works also for arrays.
    const iterable = container;
    for(const k in iterable){
        const val = iterable[k];
        if (!seen.has(val)) {
            seen.add(val);
            walkTensorContainer(val, list, seen);
        }
    }
}
// tslint:disable-next-line:no-any
function isIterable(obj) {
    return Array.isArray(obj) || typeof obj === "object";
}

},{"./tensor":"cJNoh","./types":"10R8n","./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"10R8n":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Rank", ()=>Rank);
parcelHelpers.export(exports, "upcastType", ()=>upcastType);
/** Returns the output type after summation. */ parcelHelpers.export(exports, "sumOutType", ()=>sumOutType);
parcelHelpers.export(exports, "isWebGLData", ()=>isWebGLData);
parcelHelpers.export(exports, "isWebGPUData", ()=>isWebGPUData);
var Rank;
(function(Rank) {
    Rank["R0"] = "R0";
    Rank["R1"] = "R1";
    Rank["R2"] = "R2";
    Rank["R3"] = "R3";
    Rank["R4"] = "R4";
    Rank["R5"] = "R5";
    Rank["R6"] = "R6";
})(Rank || (Rank = {}));
// Looks for upcasting types. Used, for example, in operations with mixed dtype
// inputs.
var UpcastInt32AndMap;
(function(UpcastInt32AndMap) {
    UpcastInt32AndMap["float32"] = "float32";
    UpcastInt32AndMap["int32"] = "int32";
    UpcastInt32AndMap["bool"] = "int32";
    UpcastInt32AndMap["complex64"] = "complex64";
})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
var UpcastBoolAndMap;
(function(UpcastBoolAndMap) {
    UpcastBoolAndMap["float32"] = "float32";
    UpcastBoolAndMap["int32"] = "int32";
    UpcastBoolAndMap["bool"] = "bool";
    UpcastBoolAndMap["complex64"] = "complex64";
})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
var UpcastFloat32AndMap;
(function(UpcastFloat32AndMap) {
    UpcastFloat32AndMap["float32"] = "float32";
    UpcastFloat32AndMap["int32"] = "float32";
    UpcastFloat32AndMap["bool"] = "float32";
    UpcastFloat32AndMap["complex64"] = "complex64";
})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
var UpcastComplex64AndMap;
(function(UpcastComplex64AndMap) {
    UpcastComplex64AndMap["float32"] = "complex64";
    UpcastComplex64AndMap["int32"] = "complex64";
    UpcastComplex64AndMap["bool"] = "complex64";
    UpcastComplex64AndMap["complex64"] = "complex64";
})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
const upcastTypeMap = {
    "float32": UpcastFloat32AndMap,
    "int32": UpcastInt32AndMap,
    "bool": UpcastBoolAndMap,
    "complex64": UpcastComplex64AndMap
};
function upcastType(typeA, typeB) {
    if (typeA === "string" || typeB === "string") {
        if (typeA === "string" && typeB === "string") return "string";
        throw new Error(`Can not upcast ${typeA} with ${typeB}`);
    }
    return upcastTypeMap[typeA][typeB];
}
function sumOutType(type) {
    return upcastType(type, "int32");
}
function isWebGLData(values) {
    return values != null && typeof values === "object" && "texture" in values && values.texture instanceof WebGLTexture;
}
function isWebGPUData(values) {
    return typeof GPUBuffer !== "undefined" && values != null && typeof values === "object" && "buffer" in values && values.buffer instanceof GPUBuffer;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5yJGC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var _engine = require("./engine");
var _deviceUtil = require("./device_util");
var _environment = require("./environment");
var process = require("329ebbdb86789035");
const ENV = (0, _environment.env)();
/**
 * This file contains environment-related flag registrations.
 */ /** Whether to enable debug mode. */ ENV.registerFlag("DEBUG", ()=>false, (debugValue)=>{
    if (debugValue) console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");
});
/** Whether we are in a browser (as versus, say, node.js) environment. */ ENV.registerFlag("IS_BROWSER", ()=>_deviceUtil.isBrowser());
/** Whether we are in a browser (as versus, say, node.js) environment. */ ENV.registerFlag("IS_NODE", ()=>typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined");
/** Whether this browser is Chrome. */ ENV.registerFlag("IS_CHROME", ()=>typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
/** Whether this browser is Safari. */ ENV.registerFlag("IS_SAFARI", ()=>typeof navigator !== "undefined" && navigator != null && navigator.userAgent != null && /Safari/.test(navigator.userAgent) && /Apple/.test(navigator.vendor));
/**
 * True when the environment is "production" where we disable safety checks
 * to gain performance.
 */ ENV.registerFlag("PROD", ()=>false);
/**
 * Whether to do sanity checks when inferring a shape from user-provided
 * values, used when creating a new tensor.
 */ ENV.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", ()=>ENV.getBool("DEBUG"));
/** Whether deprecation warnings are enabled. */ ENV.registerFlag("DEPRECATION_WARNINGS_ENABLED", ()=>true);
/** True if running unit tests. */ ENV.registerFlag("IS_TEST", ()=>false);
/** Whether to check computation result for errors. */ ENV.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", ()=>ENV.getBool("DEBUG"));
/** Whether the backend needs to wrap input to imageBitmap. */ ENV.registerFlag("WRAP_TO_IMAGEBITMAP", ()=>false);
/** Whether to enable canvas2d willReadFrequently for GPU backends */ ENV.registerFlag("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU", ()=>false);
/** Whether to use setTimeoutCustom */ ENV.registerFlag("USE_SETTIMEOUTCUSTOM", ()=>false);

},{"329ebbdb86789035":"h0aBX","./engine":"1g2qo","./device_util":"5fozk","./environment":"dXmHm"}],"5fozk":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line:no-any
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mockIsMobile", ()=>mockIsMobile);
parcelHelpers.export(exports, "isMobile", ()=>isMobile);
parcelHelpers.export(exports, "isBrowser", ()=>isBrowser);
function _isNavigatorDefined() {
    return typeof navigator !== "undefined" && navigator != null;
}
let isMobileMockValue;
function mockIsMobile(value) {
    isMobileMockValue = value;
}
function isMobile(nav) {
    if (isMobileMockValue !== undefined) return isMobileMockValue;
    if (nav || _isNavigatorDefined()) {
        if (!nav) nav = navigator;
        if (nav.product === "ReactNative") return true;
        const a = nav.userAgent || nav.vendor || // tslint:disable-next-line:no-any
        (typeof window !== "undefined" ? window.opera : "");
        // Use `navigator.userAgentData.mobile` as fallback.
        if (!a) {
            // tslint:disable-next-line:no-any
            const navAny = nav;
            return navAny.userAgentData && navAny.userAgentData.mobile;
        }
        // tslint:disable-next-line:max-line-length
        return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(a) || // tslint:disable-next-line:max-line-length
        /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(a.substr(0, 4));
    }
    return false;
}
function isBrowser() {
    return typeof window !== "undefined" && window.document != null || //@ts-ignore
    typeof WorkerGlobalScope !== "undefined";
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"k39df":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "PlatformBrowser", ()=>PlatformBrowser);
var _flags = require("../flags");
var _environment = require("../environment");
var _indexedDb = require("../io/indexed_db");
var _localStorage = require("../io/local_storage");
var _modelManagement = require("../io/model_management");
var _isTypedArrayBrowser = require("./is_typed_array_browser");
class PlatformBrowser {
    constructor(){
        // For setTimeoutCustom
        this.messageName = "setTimeoutCustom";
        this.functionRefs = [];
        this.handledMessageCount = 0;
        this.hasEventListener = false;
    }
    fetch(path, init) {
        return fetch(path, init);
    }
    now() {
        return performance.now();
    }
    encode(text, encoding) {
        if (encoding !== "utf-8" && encoding !== "utf8") throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);
        if (this.textEncoder == null) this.textEncoder = new TextEncoder();
        return this.textEncoder.encode(text);
    }
    decode(bytes, encoding) {
        return new TextDecoder(encoding).decode(bytes);
    }
    // If the setTimeout nesting level is greater than 5 and timeout is less
    // than 4ms, timeout will be clamped to 4ms, which hurts the perf.
    // Interleaving window.postMessage and setTimeout will trick the browser and
    // avoid the clamp.
    setTimeoutCustom(functionRef, delay) {
        if (typeof window === "undefined" || !(0, _environment.env)().getBool("USE_SETTIMEOUTCUSTOM")) {
            setTimeout(functionRef, delay);
            return;
        }
        this.functionRefs.push(functionRef);
        setTimeout(()=>{
            window.postMessage({
                name: this.messageName,
                index: this.functionRefs.length - 1
            }, "*");
        }, delay);
        if (!this.hasEventListener) {
            this.hasEventListener = true;
            window.addEventListener("message", (event)=>{
                if (event.source === window && event.data.name === this.messageName) {
                    event.stopPropagation();
                    const functionRef = this.functionRefs[event.data.index];
                    functionRef();
                    this.handledMessageCount++;
                    if (this.handledMessageCount === this.functionRefs.length) {
                        this.functionRefs = [];
                        this.handledMessageCount = 0;
                    }
                }
            }, true);
        }
    }
    isTypedArray(a) {
        return (0, _isTypedArrayBrowser.isTypedArrayBrowser)(a);
    }
}
if ((0, _environment.env)().get("IS_BROWSER")) {
    (0, _environment.env)().setPlatform("browser", new PlatformBrowser());
    // Register LocalStorage IOHandler
    try {
        (0, _modelManagement.ModelStoreManagerRegistry).registerManager((0, _localStorage.BrowserLocalStorage).URL_SCHEME, new (0, _localStorage.BrowserLocalStorageManager)());
    } catch (err) {}
    // Register IndexedDB IOHandler
    try {
        (0, _modelManagement.ModelStoreManagerRegistry).registerManager((0, _indexedDb.BrowserIndexedDB).URL_SCHEME, new (0, _indexedDb.BrowserIndexedDBManager)());
    } catch (err) {}
}

},{"../flags":"5yJGC","../environment":"dXmHm","../io/indexed_db":"aWBtv","../io/local_storage":"c3TPR","../io/model_management":"iB0Mw","./is_typed_array_browser":"cjcvP","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aWBtv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Delete the entire database for tensorflow.js, including the models store.
 */ parcelHelpers.export(exports, "deleteDatabase", ()=>deleteDatabase);
parcelHelpers.export(exports, "BrowserIndexedDB", ()=>BrowserIndexedDB);
parcelHelpers.export(exports, "indexedDBRouter", ()=>indexedDBRouter);
/**
 * Creates a browser IndexedDB IOHandler for saving and loading models.
 *
 * ```js
 * const model = tf.sequential();
 * model.add(
 *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
 *
 * const saveResult = await model.save('indexeddb://MyModel'));
 * console.log(saveResult);
 * ```
 *
 * @param modelPath A unique identifier for the model to be saved. Must be a
 *   non-empty string.
 * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),
 *   which can be used with, e.g., `tf.Model.save`.
 */ parcelHelpers.export(exports, "browserIndexedDB", ()=>browserIndexedDB);
parcelHelpers.export(exports, "BrowserIndexedDBManager", ()=>BrowserIndexedDBManager);
var _flags = require("../flags");
var _environment = require("../environment");
var _ioUtils = require("./io_utils");
var _routerRegistry = require("./router_registry");
var _compositeArrayBuffer = require("./composite_array_buffer");
const DATABASE_NAME = "tensorflowjs";
const DATABASE_VERSION = 1;
// Model data and ModelArtifactsInfo (metadata) are stored in two separate
// stores for efficient access of the list of stored models and their metadata.
// 1. The object store for model data: topology, weights and weight manifests.
const MODEL_STORE_NAME = "models_store";
// 2. The object store for ModelArtifactsInfo, including meta-information such
//    as the type of topology (JSON vs binary), byte size of the topology, byte
//    size of the weights, etc.
const INFO_STORE_NAME = "model_info_store";
async function deleteDatabase() {
    const idbFactory = getIndexedDBFactory();
    return new Promise((resolve, reject)=>{
        const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);
        deleteRequest.onsuccess = ()=>resolve();
        deleteRequest.onerror = (error)=>reject(error);
    });
}
function getIndexedDBFactory() {
    if (!(0, _environment.env)().getBool("IS_BROWSER")) // TODO(cais): Add more info about what IOHandler subtypes are available.
    //   Maybe point to a doc page on the web and/or automatically determine
    //   the available IOHandlers and print them in the error message.
    throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
    // tslint:disable-next-line:no-any
    const theWindow = typeof window === "undefined" ? self : window;
    const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;
    if (factory == null) throw new Error("The current browser does not appear to support IndexedDB.");
    return factory;
}
function setUpDatabase(openRequest) {
    const db = openRequest.result;
    db.createObjectStore(MODEL_STORE_NAME, {
        keyPath: "modelPath"
    });
    db.createObjectStore(INFO_STORE_NAME, {
        keyPath: "modelPath"
    });
}
/**
 * IOHandler subclass: Browser IndexedDB.
 *
 * See the doc string of `browserIndexedDB` for more details.
 */ class BrowserIndexedDB {
    constructor(modelPath){
        this.indexedDB = getIndexedDBFactory();
        if (modelPath == null || !modelPath) throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
        this.modelPath = modelPath;
    }
    async save(modelArtifacts) {
        // TODO(cais): Support saving GraphDef models.
        if (modelArtifacts.modelTopology instanceof ArrayBuffer) throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
        return this.databaseAction(this.modelPath, modelArtifacts);
    }
    async load() {
        return this.databaseAction(this.modelPath);
    }
    /**
     * Perform database action to put model artifacts into or read model artifacts
     * from IndexedDB object store.
     *
     * Whether the action is put or get depends on whether `modelArtifacts` is
     * specified. If it is specified, the action will be put; otherwise the action
     * will be get.
     *
     * @param modelPath A unique string path for the model.
     * @param modelArtifacts If specified, it will be the model artifacts to be
     *   stored in IndexedDB.
     * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`
     *   of `ModelArtifacts`, if the action is get.
     */ databaseAction(modelPath, modelArtifacts) {
        return new Promise((resolve, reject)=>{
            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
            openRequest.onupgradeneeded = ()=>setUpDatabase(openRequest);
            openRequest.onsuccess = ()=>{
                const db = openRequest.result;
                if (modelArtifacts == null) {
                    // Read model out from object store.
                    const modelTx = db.transaction(MODEL_STORE_NAME, "readonly");
                    const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
                    const getRequest = modelStore.get(this.modelPath);
                    getRequest.onsuccess = ()=>{
                        if (getRequest.result == null) {
                            db.close();
                            return reject(new Error(`Cannot find model with path '${this.modelPath}' ` + `in IndexedDB.`));
                        } else resolve(getRequest.result.modelArtifacts);
                    };
                    getRequest.onerror = (error)=>{
                        db.close();
                        return reject(getRequest.error);
                    };
                    modelTx.oncomplete = ()=>db.close();
                } else {
                    // Put model into object store.
                    // Concatenate all the model weights into a single ArrayBuffer. Large
                    // models (~1GB) have problems saving if they are not concatenated.
                    // TODO(mattSoulanille): Save large models to multiple indexeddb
                    // records.
                    modelArtifacts.weightData = (0, _compositeArrayBuffer.CompositeArrayBuffer).join(modelArtifacts.weightData);
                    const modelArtifactsInfo = (0, _ioUtils.getModelArtifactsInfoForJSON)(modelArtifacts);
                    // First, put ModelArtifactsInfo into info store.
                    const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
                    let infoStore = infoTx.objectStore(INFO_STORE_NAME);
                    let putInfoRequest;
                    try {
                        putInfoRequest = infoStore.put({
                            modelPath: this.modelPath,
                            modelArtifactsInfo
                        });
                    } catch (error) {
                        return reject(error);
                    }
                    let modelTx;
                    putInfoRequest.onsuccess = ()=>{
                        // Second, put model data into model store.
                        modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
                        const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
                        let putModelRequest;
                        try {
                            putModelRequest = modelStore.put({
                                modelPath: this.modelPath,
                                modelArtifacts,
                                modelArtifactsInfo
                            });
                        } catch (error) {
                            // Sometimes, the serialized value is too large to store.
                            return reject(error);
                        }
                        putModelRequest.onsuccess = ()=>resolve({
                                modelArtifactsInfo
                            });
                        putModelRequest.onerror = (error)=>{
                            // If the put-model request fails, roll back the info entry as
                            // well.
                            infoStore = infoTx.objectStore(INFO_STORE_NAME);
                            const deleteInfoRequest = infoStore.delete(this.modelPath);
                            deleteInfoRequest.onsuccess = ()=>{
                                db.close();
                                return reject(putModelRequest.error);
                            };
                            deleteInfoRequest.onerror = (error)=>{
                                db.close();
                                return reject(putModelRequest.error);
                            };
                        };
                    };
                    putInfoRequest.onerror = (error)=>{
                        db.close();
                        return reject(putInfoRequest.error);
                    };
                    infoTx.oncomplete = ()=>{
                        if (modelTx == null) db.close();
                        else modelTx.oncomplete = ()=>db.close();
                    };
                }
            };
            openRequest.onerror = (error)=>reject(openRequest.error);
        });
    }
}
BrowserIndexedDB.URL_SCHEME = "indexeddb://";
const indexedDBRouter = (url)=>{
    if (!(0, _environment.env)().getBool("IS_BROWSER")) return null;
    else {
        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
        else return null;
    }
};
(0, _routerRegistry.IORouterRegistry).registerSaveRouter(indexedDBRouter);
(0, _routerRegistry.IORouterRegistry).registerLoadRouter(indexedDBRouter);
function browserIndexedDB(modelPath) {
    return new BrowserIndexedDB(modelPath);
}
function maybeStripScheme(key) {
    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;
}
class BrowserIndexedDBManager {
    constructor(){
        this.indexedDB = getIndexedDBFactory();
    }
    async listModels() {
        return new Promise((resolve, reject)=>{
            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
            openRequest.onupgradeneeded = ()=>setUpDatabase(openRequest);
            openRequest.onsuccess = ()=>{
                const db = openRequest.result;
                const tx = db.transaction(INFO_STORE_NAME, "readonly");
                const store = tx.objectStore(INFO_STORE_NAME);
                // tslint:disable:max-line-length
                // Need to cast `store` as `any` here because TypeScript's DOM
                // library does not have the `getAll()` method even though the
                // method is supported in the latest version of most mainstream
                // browsers:
                // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll
                // tslint:enable:max-line-length
                // tslint:disable-next-line:no-any
                const getAllInfoRequest = store.getAll();
                getAllInfoRequest.onsuccess = ()=>{
                    const out = {};
                    for (const item of getAllInfoRequest.result)out[item.modelPath] = item.modelArtifactsInfo;
                    resolve(out);
                };
                getAllInfoRequest.onerror = (error)=>{
                    db.close();
                    return reject(getAllInfoRequest.error);
                };
                tx.oncomplete = ()=>db.close();
            };
            openRequest.onerror = (error)=>reject(openRequest.error);
        });
    }
    async removeModel(path) {
        path = maybeStripScheme(path);
        return new Promise((resolve, reject)=>{
            const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
            openRequest.onupgradeneeded = ()=>setUpDatabase(openRequest);
            openRequest.onsuccess = ()=>{
                const db = openRequest.result;
                const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
                const infoStore = infoTx.objectStore(INFO_STORE_NAME);
                const getInfoRequest = infoStore.get(path);
                let modelTx;
                getInfoRequest.onsuccess = ()=>{
                    if (getInfoRequest.result == null) {
                        db.close();
                        return reject(new Error(`Cannot find model with path '${path}' ` + `in IndexedDB.`));
                    } else {
                        // First, delete the entry in the info store.
                        const deleteInfoRequest = infoStore.delete(path);
                        const deleteModelData = ()=>{
                            // Second, delete the entry in the model store.
                            modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
                            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
                            const deleteModelRequest = modelStore.delete(path);
                            deleteModelRequest.onsuccess = ()=>resolve(getInfoRequest.result.modelArtifactsInfo);
                            deleteModelRequest.onerror = (error)=>reject(getInfoRequest.error);
                        };
                        // Proceed with deleting model data regardless of whether deletion
                        // of info data succeeds or not.
                        deleteInfoRequest.onsuccess = deleteModelData;
                        deleteInfoRequest.onerror = (error)=>{
                            deleteModelData();
                            db.close();
                            return reject(getInfoRequest.error);
                        };
                    }
                };
                getInfoRequest.onerror = (error)=>{
                    db.close();
                    return reject(getInfoRequest.error);
                };
                infoTx.oncomplete = ()=>{
                    if (modelTx == null) db.close();
                    else modelTx.oncomplete = ()=>db.close();
                };
            };
            openRequest.onerror = (error)=>reject(openRequest.error);
        });
    }
}

},{"../flags":"5yJGC","../environment":"dXmHm","./io_utils":"hW5iQ","./router_registry":"btpsw","./composite_array_buffer":"gHH7C","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hW5iQ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Encode a map from names to weight values as an ArrayBuffer, along with an
 * `Array` of `WeightsManifestEntry` as specification of the encoded weights.
 *
 * This function does not perform sharding.
 *
 * This function is the reverse of `decodeWeights`.
 *
 * @param tensors A map ("dict") from names to tensors.
 * @param group Group to which the weights belong (optional).
 * @returns A `Promise` of
 *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s
 *     concatenated.
 *   - An `Array` of `WeightManifestEntry`s, carrying information including
 *     tensor names, `dtype`s and shapes.
 * @throws Error: on unsupported tensor `dtype`.
 */ parcelHelpers.export(exports, "encodeWeights", ()=>encodeWeights);
/**
 * Decode flat ArrayBuffer as weights.
 *
 * This function does not handle sharding.
 *
 * This function is the reverse of `encodeWeights`.
 *
 * @param weightData A flat ArrayBuffer or an array of ArrayBuffers carrying the
 *   binary values of the tensors concatenated in the order specified in
 *   `specs`.
 * @param specs Specifications of the names, dtypes and shapes of the tensors
 *   whose value are encoded by `buffer`.
 * @return A map from tensor name to tensor value, with the names corresponding
 *   to names in `specs`.
 * @throws Error, if any of the tensors has unsupported dtype.
 */ parcelHelpers.export(exports, "decodeWeights", ()=>decodeWeights);
parcelHelpers.export(exports, "decodeWeightsStream", ()=>decodeWeightsStream);
/**
 * Concatenate TypedArrays into an ArrayBuffer.
 */ parcelHelpers.export(exports, "concatenateTypedArrays", ()=>concatenateTypedArrays);
/**
 * Calculate the byte length of a JavaScript string.
 *
 * Note that a JavaScript string can contain wide characters, therefore the
 * length of the string is not necessarily equal to the byte length.
 *
 * @param str Input string.
 * @returns Byte length.
 */ parcelHelpers.export(exports, "stringByteLength", ()=>stringByteLength);
/**
 * Encode an ArrayBuffer as a base64 encoded string.
 *
 * @param buffer `ArrayBuffer` to be converted.
 * @returns A string that base64-encodes `buffer`.
 */ parcelHelpers.export(exports, "arrayBufferToBase64String", ()=>arrayBufferToBase64String);
/**
 * Decode a base64 string as an ArrayBuffer.
 *
 * @param str Base64 string.
 * @returns Decoded `ArrayBuffer`.
 */ parcelHelpers.export(exports, "base64StringToArrayBuffer", ()=>base64StringToArrayBuffer);
/**
 * Concatenate a number of ArrayBuffers into one.
 *
 * @param buffers An array of ArrayBuffers to concatenate, or a single
 *     ArrayBuffer.
 * @returns Result of concatenating `buffers` in order.
 *
 * @deprecated Use tf.io.CompositeArrayBuffer.join() instead.
 */ parcelHelpers.export(exports, "concatenateArrayBuffers", ()=>concatenateArrayBuffers);
/**
 * Get the basename of a path.
 *
 * Behaves in a way analogous to Linux's basename command.
 *
 * @param path
 */ parcelHelpers.export(exports, "basename", ()=>basename);
/**
 * Create `ModelJSON` from `ModelArtifacts`.
 *
 * @param artifacts Model artifacts, describing the model and its weights.
 * @param manifest Weight manifest, describing where the weights of the
 *     `ModelArtifacts` are stored, and some metadata about them.
 * @returns Object representing the `model.json` file describing the model
 *     artifacts and weights
 */ parcelHelpers.export(exports, "getModelJSONForModelArtifacts", ()=>getModelJSONForModelArtifacts);
/**
 * Create `ModelArtifacts` from a JSON file and weights.
 *
 * @param modelJSON Object containing the parsed JSON of `model.json`
 * @param weightSpecs The list of WeightsManifestEntry for the model. Must be
 *     passed if the modelJSON has a weightsManifest.
 * @param weightData An ArrayBuffer or array of ArrayBuffers of weight data for
 *     the model corresponding to the weights in weightSpecs. Must be passed if
 *     the modelJSON has a weightsManifest.
 * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
 */ parcelHelpers.export(exports, "getModelArtifactsForJSONSync", ()=>getModelArtifactsForJSONSync);
/**
 * Create `ModelArtifacts` from a JSON file.
 *
 * @param modelJSON Object containing the parsed JSON of `model.json`
 * @param loadWeights Function that takes the JSON file's weights manifest,
 *     reads weights from the listed path(s), and returns a Promise of the
 *     weight manifest entries along with the weights data.
 * @returns A Promise of the `ModelArtifacts`, as described by the JSON file.
 */ parcelHelpers.export(exports, "getModelArtifactsForJSON", ()=>getModelArtifactsForJSON);
/**
 * Populate ModelArtifactsInfo fields for a model with JSON topology.
 * @param modelArtifacts
 * @returns A ModelArtifactsInfo object.
 */ parcelHelpers.export(exports, "getModelArtifactsInfoForJSON", ()=>getModelArtifactsInfoForJSON);
/**
 * Concatenate the weights stored in a WeightsManifestConfig into a list of
 * WeightsManifestEntry
 *
 * @param weightsManifest The WeightsManifestConfig to extract weights from.
 * @returns A list of WeightsManifestEntry of the weights in the weightsManifest
 */ parcelHelpers.export(exports, "getWeightSpecs", ()=>getWeightSpecs);
/**
 * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values
 * to a Float32Array.
 *
 * @returns Function (buffer: Uint16Array) => Float32Array which decodes
 *          the Uint16Array of Float16 bytes to a Float32Array.
 */ parcelHelpers.export(exports, "getFloat16Decoder", ()=>getFloat16Decoder);
var _complex = require("../ops/complex");
var _tensor = require("../ops/tensor");
var _util = require("../util");
var _types = require("./types");
var _compositeArrayBuffer = require("./composite_array_buffer");
var _globals = require("../globals");
var _environment = require("../environment");
var Buffer = require("cc5f092a6233570f").Buffer;
/** Number of bytes reserved for the length of the string. (32bit integer). */ const NUM_BYTES_STRING_LENGTH = 4;
async function encodeWeights(tensors, group) {
    // TODO(adarob, cais): Support quantization.
    const specs = [];
    const dataPromises = [];
    const names = Array.isArray(tensors) ? tensors.map((tensor)=>tensor.name) : Object.keys(tensors);
    for(let i = 0; i < names.length; ++i){
        const name = names[i];
        const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
        if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);
        const spec = {
            name,
            shape: t.shape,
            dtype: t.dtype
        };
        if (t.dtype === "string") {
            const utf8bytes = new Promise(async (resolve)=>{
                const vals = await t.bytes();
                const totalNumBytes = vals.reduce((p, c)=>p + c.length, 0) + NUM_BYTES_STRING_LENGTH * vals.length;
                const bytes = new Uint8Array(totalNumBytes);
                let offset = 0;
                for(let i = 0; i < vals.length; i++){
                    const val = vals[i];
                    const bytesOfLength = new Uint8Array(new Uint32Array([
                        val.length
                    ]).buffer);
                    bytes.set(bytesOfLength, offset);
                    offset += NUM_BYTES_STRING_LENGTH;
                    bytes.set(val, offset);
                    offset += val.length;
                }
                resolve(bytes);
            });
            dataPromises.push(utf8bytes);
        } else dataPromises.push(t.data());
        if (group != null) spec.group = group;
        specs.push(spec);
    }
    const tensorValues = await Promise.all(dataPromises);
    return {
        data: concatenateTypedArrays(tensorValues),
        specs
    };
}
function decodeWeights(weightData, specs) {
    // TODO(adarob, cais): Support quantization.
    const compositeBuffer = new (0, _compositeArrayBuffer.CompositeArrayBuffer)(weightData);
    const out = {};
    let offset = 0;
    for (const spec of specs){
        const byteLength = getWeightBytelength(spec, (start, end)=>{
            return compositeBuffer.slice(offset + start, offset + end);
        });
        out[spec.name] = decodeWeight(spec, compositeBuffer.slice(offset, offset + byteLength));
        offset += byteLength;
    }
    return out;
}
function getWeightBytelength(spec, slice) {
    const size = (0, _util.sizeFromShape)(spec.shape);
    let bytesPerValue;
    if ("quantization" in spec) {
        const quantization = spec.quantization;
        bytesPerValue = (0, _types.DTYPE_VALUE_SIZE_MAP)[quantization.dtype];
    } else if (spec.dtype === "string") {
        // Can not statically determine string length.
        let byteLength = 0;
        for(let i = 0; i < size; i++)byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(slice(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0];
        return byteLength;
    } else bytesPerValue = (0, _types.DTYPE_VALUE_SIZE_MAP)[spec.dtype];
    return size * bytesPerValue;
}
async function getWeightBytelengthAsync(spec, slice) {
    const size = (0, _util.sizeFromShape)(spec.shape);
    let bytesPerValue;
    if ("quantization" in spec) {
        const quantization = spec.quantization;
        bytesPerValue = (0, _types.DTYPE_VALUE_SIZE_MAP)[quantization.dtype];
    } else if (spec.dtype === "string") {
        // Can not statically determine string length.
        let byteLength = 0;
        for(let i = 0; i < size; i++)byteLength += NUM_BYTES_STRING_LENGTH + new Uint32Array(await slice(byteLength, byteLength + NUM_BYTES_STRING_LENGTH))[0];
        return byteLength;
    } else bytesPerValue = (0, _types.DTYPE_VALUE_SIZE_MAP)[spec.dtype];
    return size * bytesPerValue;
}
function decodeWeight(spec, byteBuffer) {
    const name = spec.name;
    const dtype = spec.dtype;
    const shape = spec.shape;
    const size = (0, _util.sizeFromShape)(shape);
    let values;
    let offset = 0;
    if ("quantization" in spec) {
        const quantization = spec.quantization;
        if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
            if (!("min" in quantization && "scale" in quantization)) throw new Error(`Weight ${spec.name} with quantization ${quantization.dtype} ` + `doesn't have corresponding metadata min and scale.`);
        } else if (quantization.dtype === "float16") {
            if (dtype !== "float32") throw new Error(`Weight ${spec.name} is quantized with ${quantization.dtype} ` + `which only supports weights of type float32 not ${dtype}.`);
        } else throw new Error(`Weight ${spec.name} has unknown ` + `quantization dtype ${quantization.dtype}. ` + `Supported quantization dtypes are: ` + `'uint8', 'uint16', and 'float16'.`);
        const quantizationSizeFactor = (0, _types.DTYPE_VALUE_SIZE_MAP)[quantization.dtype];
        const quantizedArray = quantization.dtype === "uint8" ? new Uint8Array(byteBuffer) : new Uint16Array(byteBuffer);
        if (dtype === "float32") {
            if (quantization.dtype === "uint8" || quantization.dtype === "uint16") {
                values = new Float32Array(quantizedArray.length);
                for(let i = 0; i < quantizedArray.length; i++){
                    const v = quantizedArray[i];
                    values[i] = v * quantization.scale + quantization.min;
                }
            } else if (quantization.dtype === "float16") {
                // TODO: This is inefficient. Make getFloat16Decoder efficient.
                const float16Decode = getFloat16Decoder();
                values = float16Decode(quantizedArray);
            } else throw new Error(`Unsupported quantization type ${quantization.dtype} ` + `for weight type float32.`);
        } else if (dtype === "int32") {
            if (quantization.dtype !== "uint8" && quantization.dtype !== "uint16") throw new Error(`Unsupported quantization type ${quantization.dtype} ` + `for weight type int32.`);
            values = new Int32Array(quantizedArray.length);
            for(let i = 0; i < quantizedArray.length; i++){
                const v = quantizedArray[i];
                values[i] = Math.round(v * quantization.scale + quantization.min);
            }
        } else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
        offset += size * quantizationSizeFactor;
    } else if (dtype === "string") {
        const size = (0, _util.sizeFromShape)(spec.shape);
        values = [];
        for(let i = 0; i < size; i++){
            const byteLength = new Uint32Array(byteBuffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
            offset += NUM_BYTES_STRING_LENGTH;
            const bytes = new Uint8Array(byteBuffer.slice(offset, offset + byteLength));
            values.push(bytes);
            offset += byteLength;
        }
    } else {
        const dtypeFactor = (0, _types.DTYPE_VALUE_SIZE_MAP)[dtype];
        if (dtype === "float32") values = new Float32Array(byteBuffer);
        else if (dtype === "int32") values = new Int32Array(byteBuffer);
        else if (dtype === "bool") values = new Uint8Array(byteBuffer);
        else if (dtype === "complex64") {
            values = new Float32Array(byteBuffer);
            const real = new Float32Array(values.length / 2);
            const image = new Float32Array(values.length / 2);
            for(let i = 0; i < real.length; i++){
                real[i] = values[i * 2];
                image[i] = values[i * 2 + 1];
            }
            const realTensor = (0, _tensor.tensor)(real, shape, "float32");
            const imageTensor = (0, _tensor.tensor)(image, shape, "float32");
            const complexTensor = (0, _complex.complex)(realTensor, imageTensor);
            realTensor.dispose();
            imageTensor.dispose();
            return complexTensor;
        } else throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
        offset += size * dtypeFactor;
    }
    return (0, _tensor.tensor)(values, shape, dtype);
}
async function readToLength(reader, initialData, length) {
    let data = new Uint8Array(initialData);
    while(data.byteLength < length){
        const { done, value } = await reader.read();
        if (done && value == null) {
            const missing = length - data.byteLength;
            throw new Error(`Reader is done but ${missing} bytes are still expected`);
        }
        // TODO: Don't create a new array every loop.
        const newData = new Uint8Array(data.length + value.byteLength);
        newData.set(data, 0);
        newData.set(new Uint8Array(value), data.length);
        data = newData;
    }
    return data.buffer;
}
async function decodeWeightsStream(weightStream, specs) {
    const tensors = {};
    const reader = weightStream.getReader();
    let data = new ArrayBuffer(0);
    for (const spec of specs){
        const byteLength = await getWeightBytelengthAsync(spec, async (start, end)=>{
            data = await readToLength(reader, data, end);
            return data.slice(start, end);
        });
        data = await readToLength(reader, data, byteLength);
        // Slice the tensor out
        const tensorData = data.slice(0, byteLength);
        data = data.slice(byteLength);
        const weightTensor = decodeWeight(spec, tensorData);
        tensors[spec.name] = weightTensor;
        // TODO(mattsoulanille): Better way to call uploadToGPU.
        // TODO(mattsoulanille): Make this work for webgl too.
        if ((0, _globals.getBackend)() === "webgpu") {
            const b = (0, _globals.backend)();
            if ("uploadToGPU" in b && (0, _util.sizeFromShape)(weightTensor.shape) >= (0, _environment.env)().get("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD")) b.uploadToGPU(weightTensor.dataId);
        }
    }
    return tensors;
}
function concatenateTypedArrays(xs) {
    // TODO(adarob, cais): Support quantization.
    if (xs === null) throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);
    let totalByteLength = 0;
    // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'
    // can have a different byte length from that of the `TypedArray` itself,
    // for example, when the `TypedArray` is created from an offset in an
    // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match
    // the `TypedArray` in byte length. If an element of `xs` does not show
    // this property, a new `TypedArray` that satisfy this property will be
    // constructed and pushed into `normalizedXs`.
    const normalizedXs = [];
    xs.forEach((x)=>{
        totalByteLength += x.byteLength;
        // tslint:disable:no-any
        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x : new x.constructor(x));
        if (!(x instanceof Float32Array || x instanceof Int32Array || x instanceof Uint8Array)) throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);
    // tslint:enable:no-any
    });
    const y = new Uint8Array(totalByteLength);
    let offset = 0;
    normalizedXs.forEach((x)=>{
        y.set(new Uint8Array(x.buffer), offset);
        offset += x.byteLength;
    });
    return y.buffer;
}
// Use Buffer on Node.js instead of Blob/atob/btoa
const useNodeBuffer = typeof Buffer !== "undefined" && (typeof Blob === "undefined" || typeof atob === "undefined" || typeof btoa === "undefined");
function stringByteLength(str) {
    if (useNodeBuffer) return Buffer.byteLength(str, "utf8");
    return new Blob([
        str
    ]).size;
}
function arrayBufferToBase64String(buffer) {
    if (useNodeBuffer) return Buffer.from(buffer).toString("base64");
    const buf = new Uint8Array(buffer);
    let s = "";
    for(let i = 0, l = buf.length; i < l; i++)s += String.fromCharCode(buf[i]);
    return btoa(s);
}
function base64StringToArrayBuffer(str) {
    if (useNodeBuffer) {
        const buf = Buffer.from(str, "base64");
        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
    }
    const s = atob(str);
    const buffer = new Uint8Array(s.length);
    for(let i = 0; i < s.length; ++i)buffer.set([
        s.charCodeAt(i)
    ], i);
    return buffer.buffer;
}
function concatenateArrayBuffers(buffers) {
    return (0, _compositeArrayBuffer.CompositeArrayBuffer).join(buffers);
}
function basename(path) {
    const SEPARATOR = "/";
    path = path.trim();
    while(path.endsWith(SEPARATOR))path = path.slice(0, path.length - 1);
    const items = path.split(SEPARATOR);
    return items[items.length - 1];
}
function getModelJSONForModelArtifacts(artifacts, manifest) {
    const result = {
        modelTopology: artifacts.modelTopology,
        format: artifacts.format,
        generatedBy: artifacts.generatedBy,
        convertedBy: artifacts.convertedBy,
        weightsManifest: manifest
    };
    if (artifacts.signature != null) result.signature = artifacts.signature;
    if (artifacts.userDefinedMetadata != null) result.userDefinedMetadata = artifacts.userDefinedMetadata;
    if (artifacts.modelInitializer != null) result.modelInitializer = artifacts.modelInitializer;
    if (artifacts.initializerSignature != null) result.initializerSignature = artifacts.initializerSignature;
    if (artifacts.trainingConfig != null) result.trainingConfig = artifacts.trainingConfig;
    return result;
}
function getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData) {
    const modelArtifacts = {
        modelTopology: modelJSON.modelTopology,
        format: modelJSON.format,
        generatedBy: modelJSON.generatedBy,
        convertedBy: modelJSON.convertedBy
    };
    if (modelJSON.trainingConfig != null) modelArtifacts.trainingConfig = modelJSON.trainingConfig;
    if (modelJSON.weightsManifest != null) {
        if (!weightSpecs) throw new Error("modelJSON has weightsManifest but weightSpecs is null");
        if (!weightData) throw new Error("modelJSON has weightsManifest but weightData is null");
        modelArtifacts.weightSpecs = weightSpecs;
        modelArtifacts.weightData = weightData;
    }
    if (modelJSON.signature != null) modelArtifacts.signature = modelJSON.signature;
    if (modelJSON.userDefinedMetadata != null) modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;
    if (modelJSON.modelInitializer != null) modelArtifacts.modelInitializer = modelJSON.modelInitializer;
    if (modelJSON.initializerSignature != null) modelArtifacts.initializerSignature = modelJSON.initializerSignature;
    return modelArtifacts;
}
async function getModelArtifactsForJSON(modelJSON, loadWeights) {
    let weightSpecs;
    let weightData;
    if (modelJSON.weightsManifest != null) [weightSpecs, weightData] = await loadWeights(modelJSON.weightsManifest);
    return getModelArtifactsForJSONSync(modelJSON, weightSpecs, weightData);
}
function getModelArtifactsInfoForJSON(modelArtifacts) {
    if (modelArtifacts.modelTopology instanceof ArrayBuffer) throw new Error("Expected JSON model topology, received ArrayBuffer.");
    return {
        dateSaved: new Date(),
        modelTopologyType: "JSON",
        modelTopologyBytes: modelArtifacts.modelTopology == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
        weightSpecsBytes: modelArtifacts.weightSpecs == null ? 0 : stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
        weightDataBytes: modelArtifacts.weightData == null ? 0 : new (0, _compositeArrayBuffer.CompositeArrayBuffer)(modelArtifacts.weightData).byteLength
    };
}
function getWeightSpecs(weightsManifest) {
    const weightSpecs = [];
    for (const entry of weightsManifest)weightSpecs.push(...entry.weights);
    return weightSpecs;
}
/**
 * Computes mantisa table for casting Float16 to Float32
 * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
 *
 * @returns Uint32Array, 2048 mantissa lookup values.
 */ function computeFloat16MantisaTable() {
    const convertMantissa = (i)=>{
        let m = i << 13;
        let e = 0;
        while((m & 0x00800000) === 0){
            e -= 0x00800000;
            m <<= 1;
        }
        m &= -8388609;
        e += 0x38800000;
        return m | e;
    };
    const mantisaTable = new Uint32Array(2048);
    mantisaTable[0] = 0;
    for(let i = 1; i < 1024; i++)mantisaTable[i] = convertMantissa(i);
    for(let i = 1024; i < 2048; i++)mantisaTable[i] = 0x38000000 + (i - 1024 << 13);
    return mantisaTable;
}
/**
 * Computes exponent table for casting Float16 to Float32
 * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
 *
 * @returns Uint32Array, 64 exponent lookup values.
 */ function computeFloat16ExponentTable() {
    const exponentTable = new Uint32Array(64);
    exponentTable[0] = 0;
    exponentTable[31] = 0x47800000;
    exponentTable[32] = 0x80000000;
    exponentTable[63] = 0xc7800000;
    for(let i = 1; i < 31; i++)exponentTable[i] = i << 23;
    for(let i = 33; i < 63; i++)exponentTable[i] = 0x80000000 + (i - 32 << 23);
    return exponentTable;
}
/**
 * Computes offset table for casting Float16 to Float32
 * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
 *
 * @returns Uint32Array, 6d offset values.
 */ function computeFloat16OffsetTable() {
    const offsetTable = new Uint32Array(64);
    for(let i = 0; i < 64; i++)offsetTable[i] = 1024;
    offsetTable[0] = offsetTable[32] = 0;
    return offsetTable;
}
function getFloat16Decoder() {
    // Algorithm is based off of
    // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf
    // Cache lookup tables
    const mantisaTable = computeFloat16MantisaTable();
    const exponentTable = computeFloat16ExponentTable();
    const offsetTable = computeFloat16OffsetTable();
    return (quantizedArray)=>{
        const buffer = new ArrayBuffer(4 * quantizedArray.length);
        const bufferUint32View = new Uint32Array(buffer);
        for(let index = 0; index < quantizedArray.length; index++){
            const float16Bits = quantizedArray[index];
            const float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] + exponentTable[float16Bits >> 10];
            bufferUint32View[index] = float32Bits;
        }
        return new Float32Array(buffer);
    };
}

},{"cc5f092a6233570f":"kLBSi","../ops/complex":"bPDAx","../ops/tensor":"jdbb1","../util":"ctavo","./types":"ixgVc","./composite_array_buffer":"gHH7C","../globals":"eO1Ja","../environment":"dXmHm","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kLBSi":[function(require,module,exports) {
/*!
 * The buffer module from node.js, for the browser.
 *
 * @author   Feross Aboukhadijeh <https://feross.org>
 * @license  MIT
 */ /* eslint-disable no-proto */ "use strict";
var base64 = require("9c62938f1dccc73c");
var ieee754 = require("aceacb6a4531a9d2");
var customInspectSymbol = typeof Symbol === "function" && typeof Symbol["for"] === "function" // eslint-disable-line dot-notation
 ? Symbol["for"]("nodejs.util.inspect.custom") // eslint-disable-line dot-notation
 : null;
exports.Buffer = Buffer;
exports.SlowBuffer = SlowBuffer;
exports.INSPECT_MAX_BYTES = 50;
var K_MAX_LENGTH = 0x7fffffff;
exports.kMaxLength = K_MAX_LENGTH;
/**
 * If `Buffer.TYPED_ARRAY_SUPPORT`:
 *   === true    Use Uint8Array implementation (fastest)
 *   === false   Print warning and recommend using `buffer` v4.x which has an Object
 *               implementation (most compatible, even IE6)
 *
 * Browsers that support typed arrays are IE 10+, Firefox 4+, Chrome 7+, Safari 5.1+,
 * Opera 11.6+, iOS 4.2+.
 *
 * We report that the browser does not support typed arrays if the are not subclassable
 * using __proto__. Firefox 4-29 lacks support for adding new properties to `Uint8Array`
 * (See: https://bugzilla.mozilla.org/show_bug.cgi?id=695438). IE 10 lacks support
 * for __proto__ and has a buggy typed array implementation.
 */ Buffer.TYPED_ARRAY_SUPPORT = typedArraySupport();
if (!Buffer.TYPED_ARRAY_SUPPORT && typeof console !== "undefined" && typeof console.error === "function") console.error("This browser lacks typed array (Uint8Array) support which is required by `buffer` v5.x. Use `buffer` v4.x if you require old browser support.");
function typedArraySupport() {
    // Can typed array instances can be augmented?
    try {
        var arr = new Uint8Array(1);
        var proto = {
            foo: function() {
                return 42;
            }
        };
        Object.setPrototypeOf(proto, Uint8Array.prototype);
        Object.setPrototypeOf(arr, proto);
        return arr.foo() === 42;
    } catch (e) {
        return false;
    }
}
Object.defineProperty(Buffer.prototype, "parent", {
    enumerable: true,
    get: function() {
        if (!Buffer.isBuffer(this)) return undefined;
        return this.buffer;
    }
});
Object.defineProperty(Buffer.prototype, "offset", {
    enumerable: true,
    get: function() {
        if (!Buffer.isBuffer(this)) return undefined;
        return this.byteOffset;
    }
});
function createBuffer(length) {
    if (length > K_MAX_LENGTH) throw new RangeError('The value "' + length + '" is invalid for option "size"');
    // Return an augmented `Uint8Array` instance
    var buf = new Uint8Array(length);
    Object.setPrototypeOf(buf, Buffer.prototype);
    return buf;
}
/**
 * The Buffer constructor returns instances of `Uint8Array` that have their
 * prototype changed to `Buffer.prototype`. Furthermore, `Buffer` is a subclass of
 * `Uint8Array`, so the returned instances will have all the node `Buffer` methods
 * and the `Uint8Array` methods. Square bracket notation works as expected -- it
 * returns a single octet.
 *
 * The `Uint8Array` prototype remains unmodified.
 */ function Buffer(arg, encodingOrOffset, length) {
    // Common case.
    if (typeof arg === "number") {
        if (typeof encodingOrOffset === "string") throw new TypeError('The "string" argument must be of type string. Received type number');
        return allocUnsafe(arg);
    }
    return from(arg, encodingOrOffset, length);
}
Buffer.poolSize = 8192 // not used by this implementation
;
function from(value, encodingOrOffset, length) {
    if (typeof value === "string") return fromString(value, encodingOrOffset);
    if (ArrayBuffer.isView(value)) return fromArrayView(value);
    if (value == null) throw new TypeError("The first argument must be one of type string, Buffer, ArrayBuffer, Array, or Array-like Object. Received type " + typeof value);
    if (isInstance(value, ArrayBuffer) || value && isInstance(value.buffer, ArrayBuffer)) return fromArrayBuffer(value, encodingOrOffset, length);
    if (typeof SharedArrayBuffer !== "undefined" && (isInstance(value, SharedArrayBuffer) || value && isInstance(value.buffer, SharedArrayBuffer))) return fromArrayBuffer(value, encodingOrOffset, length);
    if (typeof value === "number") throw new TypeError('The "value" argument must not be of type number. Received type number');
    var valueOf = value.valueOf && value.valueOf();
    if (valueOf != null && valueOf !== value) return Buffer.from(valueOf, encodingOrOffset, length);
    var b = fromObject(value);
    if (b) return b;
    if (typeof Symbol !== "undefined" && Symbol.toPrimitive != null && typeof value[Symbol.toPrimitive] === "function") return Buffer.from(value[Symbol.toPrimitive]("string"), encodingOrOffset, length);
    throw new TypeError("The first argument must be one of type string, Buffer, ArrayBuffer, Array, or Array-like Object. Received type " + typeof value);
}
/**
 * Functionally equivalent to Buffer(arg, encoding) but throws a TypeError
 * if value is a number.
 * Buffer.from(str[, encoding])
 * Buffer.from(array)
 * Buffer.from(buffer)
 * Buffer.from(arrayBuffer[, byteOffset[, length]])
 **/ Buffer.from = function(value, encodingOrOffset, length) {
    return from(value, encodingOrOffset, length);
};
// Note: Change prototype *after* Buffer.from is defined to workaround Chrome bug:
// https://github.com/feross/buffer/pull/148
Object.setPrototypeOf(Buffer.prototype, Uint8Array.prototype);
Object.setPrototypeOf(Buffer, Uint8Array);
function assertSize(size) {
    if (typeof size !== "number") throw new TypeError('"size" argument must be of type number');
    else if (size < 0) throw new RangeError('The value "' + size + '" is invalid for option "size"');
}
function alloc(size, fill, encoding) {
    assertSize(size);
    if (size <= 0) return createBuffer(size);
    if (fill !== undefined) // Only pay attention to encoding if it's a string. This
    // prevents accidentally sending in a number that would
    // be interpreted as a start offset.
    return typeof encoding === "string" ? createBuffer(size).fill(fill, encoding) : createBuffer(size).fill(fill);
    return createBuffer(size);
}
/**
 * Creates a new filled Buffer instance.
 * alloc(size[, fill[, encoding]])
 **/ Buffer.alloc = function(size, fill, encoding) {
    return alloc(size, fill, encoding);
};
function allocUnsafe(size) {
    assertSize(size);
    return createBuffer(size < 0 ? 0 : checked(size) | 0);
}
/**
 * Equivalent to Buffer(num), by default creates a non-zero-filled Buffer instance.
 * */ Buffer.allocUnsafe = function(size) {
    return allocUnsafe(size);
};
/**
 * Equivalent to SlowBuffer(num), by default creates a non-zero-filled Buffer instance.
 */ Buffer.allocUnsafeSlow = function(size) {
    return allocUnsafe(size);
};
function fromString(string, encoding) {
    if (typeof encoding !== "string" || encoding === "") encoding = "utf8";
    if (!Buffer.isEncoding(encoding)) throw new TypeError("Unknown encoding: " + encoding);
    var length = byteLength(string, encoding) | 0;
    var buf = createBuffer(length);
    var actual = buf.write(string, encoding);
    if (actual !== length) // Writing a hex string, for example, that contains invalid characters will
    // cause everything after the first invalid character to be ignored. (e.g.
    // 'abxxcd' will be treated as 'ab')
    buf = buf.slice(0, actual);
    return buf;
}
function fromArrayLike(array) {
    var length = array.length < 0 ? 0 : checked(array.length) | 0;
    var buf = createBuffer(length);
    for(var i = 0; i < length; i += 1)buf[i] = array[i] & 255;
    return buf;
}
function fromArrayView(arrayView) {
    if (isInstance(arrayView, Uint8Array)) {
        var copy = new Uint8Array(arrayView);
        return fromArrayBuffer(copy.buffer, copy.byteOffset, copy.byteLength);
    }
    return fromArrayLike(arrayView);
}
function fromArrayBuffer(array, byteOffset, length) {
    if (byteOffset < 0 || array.byteLength < byteOffset) throw new RangeError('"offset" is outside of buffer bounds');
    if (array.byteLength < byteOffset + (length || 0)) throw new RangeError('"length" is outside of buffer bounds');
    var buf;
    if (byteOffset === undefined && length === undefined) buf = new Uint8Array(array);
    else if (length === undefined) buf = new Uint8Array(array, byteOffset);
    else buf = new Uint8Array(array, byteOffset, length);
    // Return an augmented `Uint8Array` instance
    Object.setPrototypeOf(buf, Buffer.prototype);
    return buf;
}
function fromObject(obj) {
    if (Buffer.isBuffer(obj)) {
        var len = checked(obj.length) | 0;
        var buf = createBuffer(len);
        if (buf.length === 0) return buf;
        obj.copy(buf, 0, 0, len);
        return buf;
    }
    if (obj.length !== undefined) {
        if (typeof obj.length !== "number" || numberIsNaN(obj.length)) return createBuffer(0);
        return fromArrayLike(obj);
    }
    if (obj.type === "Buffer" && Array.isArray(obj.data)) return fromArrayLike(obj.data);
}
function checked(length) {
    // Note: cannot use `length < K_MAX_LENGTH` here because that fails when
    // length is NaN (which is otherwise coerced to zero.)
    if (length >= K_MAX_LENGTH) throw new RangeError("Attempt to allocate Buffer larger than maximum size: 0x" + K_MAX_LENGTH.toString(16) + " bytes");
    return length | 0;
}
function SlowBuffer(length) {
    if (+length != length) length = 0;
    return Buffer.alloc(+length);
}
Buffer.isBuffer = function isBuffer(b) {
    return b != null && b._isBuffer === true && b !== Buffer.prototype // so Buffer.isBuffer(Buffer.prototype) will be false
    ;
};
Buffer.compare = function compare(a, b) {
    if (isInstance(a, Uint8Array)) a = Buffer.from(a, a.offset, a.byteLength);
    if (isInstance(b, Uint8Array)) b = Buffer.from(b, b.offset, b.byteLength);
    if (!Buffer.isBuffer(a) || !Buffer.isBuffer(b)) throw new TypeError('The "buf1", "buf2" arguments must be one of type Buffer or Uint8Array');
    if (a === b) return 0;
    var x = a.length;
    var y = b.length;
    for(var i = 0, len = Math.min(x, y); i < len; ++i)if (a[i] !== b[i]) {
        x = a[i];
        y = b[i];
        break;
    }
    if (x < y) return -1;
    if (y < x) return 1;
    return 0;
};
Buffer.isEncoding = function isEncoding(encoding) {
    switch(String(encoding).toLowerCase()){
        case "hex":
        case "utf8":
        case "utf-8":
        case "ascii":
        case "latin1":
        case "binary":
        case "base64":
        case "ucs2":
        case "ucs-2":
        case "utf16le":
        case "utf-16le":
            return true;
        default:
            return false;
    }
};
Buffer.concat = function concat(list, length) {
    if (!Array.isArray(list)) throw new TypeError('"list" argument must be an Array of Buffers');
    if (list.length === 0) return Buffer.alloc(0);
    var i;
    if (length === undefined) {
        length = 0;
        for(i = 0; i < list.length; ++i)length += list[i].length;
    }
    var buffer = Buffer.allocUnsafe(length);
    var pos = 0;
    for(i = 0; i < list.length; ++i){
        var buf = list[i];
        if (isInstance(buf, Uint8Array)) {
            if (pos + buf.length > buffer.length) Buffer.from(buf).copy(buffer, pos);
            else Uint8Array.prototype.set.call(buffer, buf, pos);
        } else if (!Buffer.isBuffer(buf)) throw new TypeError('"list" argument must be an Array of Buffers');
        else buf.copy(buffer, pos);
        pos += buf.length;
    }
    return buffer;
};
function byteLength(string, encoding) {
    if (Buffer.isBuffer(string)) return string.length;
    if (ArrayBuffer.isView(string) || isInstance(string, ArrayBuffer)) return string.byteLength;
    if (typeof string !== "string") throw new TypeError('The "string" argument must be one of type string, Buffer, or ArrayBuffer. Received type ' + typeof string);
    var len = string.length;
    var mustMatch = arguments.length > 2 && arguments[2] === true;
    if (!mustMatch && len === 0) return 0;
    // Use a for loop to avoid recursion
    var loweredCase = false;
    for(;;)switch(encoding){
        case "ascii":
        case "latin1":
        case "binary":
            return len;
        case "utf8":
        case "utf-8":
            return utf8ToBytes(string).length;
        case "ucs2":
        case "ucs-2":
        case "utf16le":
        case "utf-16le":
            return len * 2;
        case "hex":
            return len >>> 1;
        case "base64":
            return base64ToBytes(string).length;
        default:
            if (loweredCase) return mustMatch ? -1 : utf8ToBytes(string).length // assume utf8
            ;
            encoding = ("" + encoding).toLowerCase();
            loweredCase = true;
    }
}
Buffer.byteLength = byteLength;
function slowToString(encoding, start, end) {
    var loweredCase = false;
    // No need to verify that "this.length <= MAX_UINT32" since it's a read-only
    // property of a typed array.
    // This behaves neither like String nor Uint8Array in that we set start/end
    // to their upper/lower bounds if the value passed is out of range.
    // undefined is handled specially as per ECMA-262 6th Edition,
    // Section 13.3.3.7 Runtime Semantics: KeyedBindingInitialization.
    if (start === undefined || start < 0) start = 0;
    // Return early if start > this.length. Done here to prevent potential uint32
    // coercion fail below.
    if (start > this.length) return "";
    if (end === undefined || end > this.length) end = this.length;
    if (end <= 0) return "";
    // Force coercion to uint32. This will also coerce falsey/NaN values to 0.
    end >>>= 0;
    start >>>= 0;
    if (end <= start) return "";
    if (!encoding) encoding = "utf8";
    while(true)switch(encoding){
        case "hex":
            return hexSlice(this, start, end);
        case "utf8":
        case "utf-8":
            return utf8Slice(this, start, end);
        case "ascii":
            return asciiSlice(this, start, end);
        case "latin1":
        case "binary":
            return latin1Slice(this, start, end);
        case "base64":
            return base64Slice(this, start, end);
        case "ucs2":
        case "ucs-2":
        case "utf16le":
        case "utf-16le":
            return utf16leSlice(this, start, end);
        default:
            if (loweredCase) throw new TypeError("Unknown encoding: " + encoding);
            encoding = (encoding + "").toLowerCase();
            loweredCase = true;
    }
}
// This property is used by `Buffer.isBuffer` (and the `is-buffer` npm package)
// to detect a Buffer instance. It's not possible to use `instanceof Buffer`
// reliably in a browserify context because there could be multiple different
// copies of the 'buffer' package in use. This method works even for Buffer
// instances that were created from another copy of the `buffer` package.
// See: https://github.com/feross/buffer/issues/154
Buffer.prototype._isBuffer = true;
function swap(b, n, m) {
    var i = b[n];
    b[n] = b[m];
    b[m] = i;
}
Buffer.prototype.swap16 = function swap16() {
    var len = this.length;
    if (len % 2 !== 0) throw new RangeError("Buffer size must be a multiple of 16-bits");
    for(var i = 0; i < len; i += 2)swap(this, i, i + 1);
    return this;
};
Buffer.prototype.swap32 = function swap32() {
    var len = this.length;
    if (len % 4 !== 0) throw new RangeError("Buffer size must be a multiple of 32-bits");
    for(var i = 0; i < len; i += 4){
        swap(this, i, i + 3);
        swap(this, i + 1, i + 2);
    }
    return this;
};
Buffer.prototype.swap64 = function swap64() {
    var len = this.length;
    if (len % 8 !== 0) throw new RangeError("Buffer size must be a multiple of 64-bits");
    for(var i = 0; i < len; i += 8){
        swap(this, i, i + 7);
        swap(this, i + 1, i + 6);
        swap(this, i + 2, i + 5);
        swap(this, i + 3, i + 4);
    }
    return this;
};
Buffer.prototype.toString = function toString() {
    var length = this.length;
    if (length === 0) return "";
    if (arguments.length === 0) return utf8Slice(this, 0, length);
    return slowToString.apply(this, arguments);
};
Buffer.prototype.toLocaleString = Buffer.prototype.toString;
Buffer.prototype.equals = function equals(b) {
    if (!Buffer.isBuffer(b)) throw new TypeError("Argument must be a Buffer");
    if (this === b) return true;
    return Buffer.compare(this, b) === 0;
};
Buffer.prototype.inspect = function inspect() {
    var str = "";
    var max = exports.INSPECT_MAX_BYTES;
    str = this.toString("hex", 0, max).replace(/(.{2})/g, "$1 ").trim();
    if (this.length > max) str += " ... ";
    return "<Buffer " + str + ">";
};
if (customInspectSymbol) Buffer.prototype[customInspectSymbol] = Buffer.prototype.inspect;
Buffer.prototype.compare = function compare(target, start, end, thisStart, thisEnd) {
    if (isInstance(target, Uint8Array)) target = Buffer.from(target, target.offset, target.byteLength);
    if (!Buffer.isBuffer(target)) throw new TypeError('The "target" argument must be one of type Buffer or Uint8Array. Received type ' + typeof target);
    if (start === undefined) start = 0;
    if (end === undefined) end = target ? target.length : 0;
    if (thisStart === undefined) thisStart = 0;
    if (thisEnd === undefined) thisEnd = this.length;
    if (start < 0 || end > target.length || thisStart < 0 || thisEnd > this.length) throw new RangeError("out of range index");
    if (thisStart >= thisEnd && start >= end) return 0;
    if (thisStart >= thisEnd) return -1;
    if (start >= end) return 1;
    start >>>= 0;
    end >>>= 0;
    thisStart >>>= 0;
    thisEnd >>>= 0;
    if (this === target) return 0;
    var x = thisEnd - thisStart;
    var y = end - start;
    var len = Math.min(x, y);
    var thisCopy = this.slice(thisStart, thisEnd);
    var targetCopy = target.slice(start, end);
    for(var i = 0; i < len; ++i)if (thisCopy[i] !== targetCopy[i]) {
        x = thisCopy[i];
        y = targetCopy[i];
        break;
    }
    if (x < y) return -1;
    if (y < x) return 1;
    return 0;
};
// Finds either the first index of `val` in `buffer` at offset >= `byteOffset`,
// OR the last index of `val` in `buffer` at offset <= `byteOffset`.
//
// Arguments:
// - buffer - a Buffer to search
// - val - a string, Buffer, or number
// - byteOffset - an index into `buffer`; will be clamped to an int32
// - encoding - an optional encoding, relevant is val is a string
// - dir - true for indexOf, false for lastIndexOf
function bidirectionalIndexOf(buffer, val, byteOffset, encoding, dir) {
    // Empty buffer means no match
    if (buffer.length === 0) return -1;
    // Normalize byteOffset
    if (typeof byteOffset === "string") {
        encoding = byteOffset;
        byteOffset = 0;
    } else if (byteOffset > 0x7fffffff) byteOffset = 0x7fffffff;
    else if (byteOffset < -2147483648) byteOffset = -2147483648;
    byteOffset = +byteOffset // Coerce to Number.
    ;
    if (numberIsNaN(byteOffset)) // byteOffset: it it's undefined, null, NaN, "foo", etc, search whole buffer
    byteOffset = dir ? 0 : buffer.length - 1;
    // Normalize byteOffset: negative offsets start from the end of the buffer
    if (byteOffset < 0) byteOffset = buffer.length + byteOffset;
    if (byteOffset >= buffer.length) {
        if (dir) return -1;
        else byteOffset = buffer.length - 1;
    } else if (byteOffset < 0) {
        if (dir) byteOffset = 0;
        else return -1;
    }
    // Normalize val
    if (typeof val === "string") val = Buffer.from(val, encoding);
    // Finally, search either indexOf (if dir is true) or lastIndexOf
    if (Buffer.isBuffer(val)) {
        // Special case: looking for empty string/buffer always fails
        if (val.length === 0) return -1;
        return arrayIndexOf(buffer, val, byteOffset, encoding, dir);
    } else if (typeof val === "number") {
        val = val & 0xFF // Search for a byte value [0-255]
        ;
        if (typeof Uint8Array.prototype.indexOf === "function") {
            if (dir) return Uint8Array.prototype.indexOf.call(buffer, val, byteOffset);
            else return Uint8Array.prototype.lastIndexOf.call(buffer, val, byteOffset);
        }
        return arrayIndexOf(buffer, [
            val
        ], byteOffset, encoding, dir);
    }
    throw new TypeError("val must be string, number or Buffer");
}
function arrayIndexOf(arr, val, byteOffset, encoding, dir) {
    var indexSize = 1;
    var arrLength = arr.length;
    var valLength = val.length;
    if (encoding !== undefined) {
        encoding = String(encoding).toLowerCase();
        if (encoding === "ucs2" || encoding === "ucs-2" || encoding === "utf16le" || encoding === "utf-16le") {
            if (arr.length < 2 || val.length < 2) return -1;
            indexSize = 2;
            arrLength /= 2;
            valLength /= 2;
            byteOffset /= 2;
        }
    }
    function read(buf, i) {
        if (indexSize === 1) return buf[i];
        else return buf.readUInt16BE(i * indexSize);
    }
    var i;
    if (dir) {
        var foundIndex = -1;
        for(i = byteOffset; i < arrLength; i++)if (read(arr, i) === read(val, foundIndex === -1 ? 0 : i - foundIndex)) {
            if (foundIndex === -1) foundIndex = i;
            if (i - foundIndex + 1 === valLength) return foundIndex * indexSize;
        } else {
            if (foundIndex !== -1) i -= i - foundIndex;
            foundIndex = -1;
        }
    } else {
        if (byteOffset + valLength > arrLength) byteOffset = arrLength - valLength;
        for(i = byteOffset; i >= 0; i--){
            var found = true;
            for(var j = 0; j < valLength; j++)if (read(arr, i + j) !== read(val, j)) {
                found = false;
                break;
            }
            if (found) return i;
        }
    }
    return -1;
}
Buffer.prototype.includes = function includes(val, byteOffset, encoding) {
    return this.indexOf(val, byteOffset, encoding) !== -1;
};
Buffer.prototype.indexOf = function indexOf(val, byteOffset, encoding) {
    return bidirectionalIndexOf(this, val, byteOffset, encoding, true);
};
Buffer.prototype.lastIndexOf = function lastIndexOf(val, byteOffset, encoding) {
    return bidirectionalIndexOf(this, val, byteOffset, encoding, false);
};
function hexWrite(buf, string, offset, length) {
    offset = Number(offset) || 0;
    var remaining = buf.length - offset;
    if (!length) length = remaining;
    else {
        length = Number(length);
        if (length > remaining) length = remaining;
    }
    var strLen = string.length;
    if (length > strLen / 2) length = strLen / 2;
    for(var i = 0; i < length; ++i){
        var parsed = parseInt(string.substr(i * 2, 2), 16);
        if (numberIsNaN(parsed)) return i;
        buf[offset + i] = parsed;
    }
    return i;
}
function utf8Write(buf, string, offset, length) {
    return blitBuffer(utf8ToBytes(string, buf.length - offset), buf, offset, length);
}
function asciiWrite(buf, string, offset, length) {
    return blitBuffer(asciiToBytes(string), buf, offset, length);
}
function base64Write(buf, string, offset, length) {
    return blitBuffer(base64ToBytes(string), buf, offset, length);
}
function ucs2Write(buf, string, offset, length) {
    return blitBuffer(utf16leToBytes(string, buf.length - offset), buf, offset, length);
}
Buffer.prototype.write = function write(string, offset, length, encoding) {
    // Buffer#write(string)
    if (offset === undefined) {
        encoding = "utf8";
        length = this.length;
        offset = 0;
    // Buffer#write(string, encoding)
    } else if (length === undefined && typeof offset === "string") {
        encoding = offset;
        length = this.length;
        offset = 0;
    // Buffer#write(string, offset[, length][, encoding])
    } else if (isFinite(offset)) {
        offset = offset >>> 0;
        if (isFinite(length)) {
            length = length >>> 0;
            if (encoding === undefined) encoding = "utf8";
        } else {
            encoding = length;
            length = undefined;
        }
    } else throw new Error("Buffer.write(string, encoding, offset[, length]) is no longer supported");
    var remaining = this.length - offset;
    if (length === undefined || length > remaining) length = remaining;
    if (string.length > 0 && (length < 0 || offset < 0) || offset > this.length) throw new RangeError("Attempt to write outside buffer bounds");
    if (!encoding) encoding = "utf8";
    var loweredCase = false;
    for(;;)switch(encoding){
        case "hex":
            return hexWrite(this, string, offset, length);
        case "utf8":
        case "utf-8":
            return utf8Write(this, string, offset, length);
        case "ascii":
        case "latin1":
        case "binary":
            return asciiWrite(this, string, offset, length);
        case "base64":
            // Warning: maxLength not taken into account in base64Write
            return base64Write(this, string, offset, length);
        case "ucs2":
        case "ucs-2":
        case "utf16le":
        case "utf-16le":
            return ucs2Write(this, string, offset, length);
        default:
            if (loweredCase) throw new TypeError("Unknown encoding: " + encoding);
            encoding = ("" + encoding).toLowerCase();
            loweredCase = true;
    }
};
Buffer.prototype.toJSON = function toJSON() {
    return {
        type: "Buffer",
        data: Array.prototype.slice.call(this._arr || this, 0)
    };
};
function base64Slice(buf, start, end) {
    if (start === 0 && end === buf.length) return base64.fromByteArray(buf);
    else return base64.fromByteArray(buf.slice(start, end));
}
function utf8Slice(buf, start, end) {
    end = Math.min(buf.length, end);
    var res = [];
    var i = start;
    while(i < end){
        var firstByte = buf[i];
        var codePoint = null;
        var bytesPerSequence = firstByte > 0xEF ? 4 : firstByte > 0xDF ? 3 : firstByte > 0xBF ? 2 : 1;
        if (i + bytesPerSequence <= end) {
            var secondByte, thirdByte, fourthByte, tempCodePoint;
            switch(bytesPerSequence){
                case 1:
                    if (firstByte < 0x80) codePoint = firstByte;
                    break;
                case 2:
                    secondByte = buf[i + 1];
                    if ((secondByte & 0xC0) === 0x80) {
                        tempCodePoint = (firstByte & 0x1F) << 0x6 | secondByte & 0x3F;
                        if (tempCodePoint > 0x7F) codePoint = tempCodePoint;
                    }
                    break;
                case 3:
                    secondByte = buf[i + 1];
                    thirdByte = buf[i + 2];
                    if ((secondByte & 0xC0) === 0x80 && (thirdByte & 0xC0) === 0x80) {
                        tempCodePoint = (firstByte & 0xF) << 0xC | (secondByte & 0x3F) << 0x6 | thirdByte & 0x3F;
                        if (tempCodePoint > 0x7FF && (tempCodePoint < 0xD800 || tempCodePoint > 0xDFFF)) codePoint = tempCodePoint;
                    }
                    break;
                case 4:
                    secondByte = buf[i + 1];
                    thirdByte = buf[i + 2];
                    fourthByte = buf[i + 3];
                    if ((secondByte & 0xC0) === 0x80 && (thirdByte & 0xC0) === 0x80 && (fourthByte & 0xC0) === 0x80) {
                        tempCodePoint = (firstByte & 0xF) << 0x12 | (secondByte & 0x3F) << 0xC | (thirdByte & 0x3F) << 0x6 | fourthByte & 0x3F;
                        if (tempCodePoint > 0xFFFF && tempCodePoint < 0x110000) codePoint = tempCodePoint;
                    }
            }
        }
        if (codePoint === null) {
            // we did not generate a valid codePoint so insert a
            // replacement char (U+FFFD) and advance only 1 byte
            codePoint = 0xFFFD;
            bytesPerSequence = 1;
        } else if (codePoint > 0xFFFF) {
            // encode to utf16 (surrogate pair dance)
            codePoint -= 0x10000;
            res.push(codePoint >>> 10 & 0x3FF | 0xD800);
            codePoint = 0xDC00 | codePoint & 0x3FF;
        }
        res.push(codePoint);
        i += bytesPerSequence;
    }
    return decodeCodePointsArray(res);
}
// Based on http://stackoverflow.com/a/22747272/680742, the browser with
// the lowest limit is Chrome, with 0x10000 args.
// We go 1 magnitude less, for safety
var MAX_ARGUMENTS_LENGTH = 0x1000;
function decodeCodePointsArray(codePoints) {
    var len = codePoints.length;
    if (len <= MAX_ARGUMENTS_LENGTH) return String.fromCharCode.apply(String, codePoints) // avoid extra slice()
    ;
    // Decode in chunks to avoid "call stack size exceeded".
    var res = "";
    var i = 0;
    while(i < len)res += String.fromCharCode.apply(String, codePoints.slice(i, i += MAX_ARGUMENTS_LENGTH));
    return res;
}
function asciiSlice(buf, start, end) {
    var ret = "";
    end = Math.min(buf.length, end);
    for(var i = start; i < end; ++i)ret += String.fromCharCode(buf[i] & 0x7F);
    return ret;
}
function latin1Slice(buf, start, end) {
    var ret = "";
    end = Math.min(buf.length, end);
    for(var i = start; i < end; ++i)ret += String.fromCharCode(buf[i]);
    return ret;
}
function hexSlice(buf, start, end) {
    var len = buf.length;
    if (!start || start < 0) start = 0;
    if (!end || end < 0 || end > len) end = len;
    var out = "";
    for(var i = start; i < end; ++i)out += hexSliceLookupTable[buf[i]];
    return out;
}
function utf16leSlice(buf, start, end) {
    var bytes = buf.slice(start, end);
    var res = "";
    // If bytes.length is odd, the last 8 bits must be ignored (same as node.js)
    for(var i = 0; i < bytes.length - 1; i += 2)res += String.fromCharCode(bytes[i] + bytes[i + 1] * 256);
    return res;
}
Buffer.prototype.slice = function slice(start, end) {
    var len = this.length;
    start = ~~start;
    end = end === undefined ? len : ~~end;
    if (start < 0) {
        start += len;
        if (start < 0) start = 0;
    } else if (start > len) start = len;
    if (end < 0) {
        end += len;
        if (end < 0) end = 0;
    } else if (end > len) end = len;
    if (end < start) end = start;
    var newBuf = this.subarray(start, end);
    // Return an augmented `Uint8Array` instance
    Object.setPrototypeOf(newBuf, Buffer.prototype);
    return newBuf;
};
/*
 * Need to make sure that buffer isn't trying to write out of bounds.
 */ function checkOffset(offset, ext, length) {
    if (offset % 1 !== 0 || offset < 0) throw new RangeError("offset is not uint");
    if (offset + ext > length) throw new RangeError("Trying to access beyond buffer length");
}
Buffer.prototype.readUintLE = Buffer.prototype.readUIntLE = function readUIntLE(offset, byteLength, noAssert) {
    offset = offset >>> 0;
    byteLength = byteLength >>> 0;
    if (!noAssert) checkOffset(offset, byteLength, this.length);
    var val = this[offset];
    var mul = 1;
    var i = 0;
    while(++i < byteLength && (mul *= 0x100))val += this[offset + i] * mul;
    return val;
};
Buffer.prototype.readUintBE = Buffer.prototype.readUIntBE = function readUIntBE(offset, byteLength, noAssert) {
    offset = offset >>> 0;
    byteLength = byteLength >>> 0;
    if (!noAssert) checkOffset(offset, byteLength, this.length);
    var val = this[offset + --byteLength];
    var mul = 1;
    while(byteLength > 0 && (mul *= 0x100))val += this[offset + --byteLength] * mul;
    return val;
};
Buffer.prototype.readUint8 = Buffer.prototype.readUInt8 = function readUInt8(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 1, this.length);
    return this[offset];
};
Buffer.prototype.readUint16LE = Buffer.prototype.readUInt16LE = function readUInt16LE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 2, this.length);
    return this[offset] | this[offset + 1] << 8;
};
Buffer.prototype.readUint16BE = Buffer.prototype.readUInt16BE = function readUInt16BE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 2, this.length);
    return this[offset] << 8 | this[offset + 1];
};
Buffer.prototype.readUint32LE = Buffer.prototype.readUInt32LE = function readUInt32LE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 4, this.length);
    return (this[offset] | this[offset + 1] << 8 | this[offset + 2] << 16) + this[offset + 3] * 0x1000000;
};
Buffer.prototype.readUint32BE = Buffer.prototype.readUInt32BE = function readUInt32BE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 4, this.length);
    return this[offset] * 0x1000000 + (this[offset + 1] << 16 | this[offset + 2] << 8 | this[offset + 3]);
};
Buffer.prototype.readIntLE = function readIntLE(offset, byteLength, noAssert) {
    offset = offset >>> 0;
    byteLength = byteLength >>> 0;
    if (!noAssert) checkOffset(offset, byteLength, this.length);
    var val = this[offset];
    var mul = 1;
    var i = 0;
    while(++i < byteLength && (mul *= 0x100))val += this[offset + i] * mul;
    mul *= 0x80;
    if (val >= mul) val -= Math.pow(2, 8 * byteLength);
    return val;
};
Buffer.prototype.readIntBE = function readIntBE(offset, byteLength, noAssert) {
    offset = offset >>> 0;
    byteLength = byteLength >>> 0;
    if (!noAssert) checkOffset(offset, byteLength, this.length);
    var i = byteLength;
    var mul = 1;
    var val = this[offset + --i];
    while(i > 0 && (mul *= 0x100))val += this[offset + --i] * mul;
    mul *= 0x80;
    if (val >= mul) val -= Math.pow(2, 8 * byteLength);
    return val;
};
Buffer.prototype.readInt8 = function readInt8(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 1, this.length);
    if (!(this[offset] & 0x80)) return this[offset];
    return (0xff - this[offset] + 1) * -1;
};
Buffer.prototype.readInt16LE = function readInt16LE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 2, this.length);
    var val = this[offset] | this[offset + 1] << 8;
    return val & 0x8000 ? val | 0xFFFF0000 : val;
};
Buffer.prototype.readInt16BE = function readInt16BE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 2, this.length);
    var val = this[offset + 1] | this[offset] << 8;
    return val & 0x8000 ? val | 0xFFFF0000 : val;
};
Buffer.prototype.readInt32LE = function readInt32LE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 4, this.length);
    return this[offset] | this[offset + 1] << 8 | this[offset + 2] << 16 | this[offset + 3] << 24;
};
Buffer.prototype.readInt32BE = function readInt32BE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 4, this.length);
    return this[offset] << 24 | this[offset + 1] << 16 | this[offset + 2] << 8 | this[offset + 3];
};
Buffer.prototype.readFloatLE = function readFloatLE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 4, this.length);
    return ieee754.read(this, offset, true, 23, 4);
};
Buffer.prototype.readFloatBE = function readFloatBE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 4, this.length);
    return ieee754.read(this, offset, false, 23, 4);
};
Buffer.prototype.readDoubleLE = function readDoubleLE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 8, this.length);
    return ieee754.read(this, offset, true, 52, 8);
};
Buffer.prototype.readDoubleBE = function readDoubleBE(offset, noAssert) {
    offset = offset >>> 0;
    if (!noAssert) checkOffset(offset, 8, this.length);
    return ieee754.read(this, offset, false, 52, 8);
};
function checkInt(buf, value, offset, ext, max, min) {
    if (!Buffer.isBuffer(buf)) throw new TypeError('"buffer" argument must be a Buffer instance');
    if (value > max || value < min) throw new RangeError('"value" argument is out of bounds');
    if (offset + ext > buf.length) throw new RangeError("Index out of range");
}
Buffer.prototype.writeUintLE = Buffer.prototype.writeUIntLE = function writeUIntLE(value, offset, byteLength, noAssert) {
    value = +value;
    offset = offset >>> 0;
    byteLength = byteLength >>> 0;
    if (!noAssert) {
        var maxBytes = Math.pow(2, 8 * byteLength) - 1;
        checkInt(this, value, offset, byteLength, maxBytes, 0);
    }
    var mul = 1;
    var i = 0;
    this[offset] = value & 0xFF;
    while(++i < byteLength && (mul *= 0x100))this[offset + i] = value / mul & 0xFF;
    return offset + byteLength;
};
Buffer.prototype.writeUintBE = Buffer.prototype.writeUIntBE = function writeUIntBE(value, offset, byteLength, noAssert) {
    value = +value;
    offset = offset >>> 0;
    byteLength = byteLength >>> 0;
    if (!noAssert) {
        var maxBytes = Math.pow(2, 8 * byteLength) - 1;
        checkInt(this, value, offset, byteLength, maxBytes, 0);
    }
    var i = byteLength - 1;
    var mul = 1;
    this[offset + i] = value & 0xFF;
    while(--i >= 0 && (mul *= 0x100))this[offset + i] = value / mul & 0xFF;
    return offset + byteLength;
};
Buffer.prototype.writeUint8 = Buffer.prototype.writeUInt8 = function writeUInt8(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 1, 0xff, 0);
    this[offset] = value & 0xff;
    return offset + 1;
};
Buffer.prototype.writeUint16LE = Buffer.prototype.writeUInt16LE = function writeUInt16LE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 2, 0xffff, 0);
    this[offset] = value & 0xff;
    this[offset + 1] = value >>> 8;
    return offset + 2;
};
Buffer.prototype.writeUint16BE = Buffer.prototype.writeUInt16BE = function writeUInt16BE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 2, 0xffff, 0);
    this[offset] = value >>> 8;
    this[offset + 1] = value & 0xff;
    return offset + 2;
};
Buffer.prototype.writeUint32LE = Buffer.prototype.writeUInt32LE = function writeUInt32LE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 4, 0xffffffff, 0);
    this[offset + 3] = value >>> 24;
    this[offset + 2] = value >>> 16;
    this[offset + 1] = value >>> 8;
    this[offset] = value & 0xff;
    return offset + 4;
};
Buffer.prototype.writeUint32BE = Buffer.prototype.writeUInt32BE = function writeUInt32BE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 4, 0xffffffff, 0);
    this[offset] = value >>> 24;
    this[offset + 1] = value >>> 16;
    this[offset + 2] = value >>> 8;
    this[offset + 3] = value & 0xff;
    return offset + 4;
};
Buffer.prototype.writeIntLE = function writeIntLE(value, offset, byteLength, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) {
        var limit = Math.pow(2, 8 * byteLength - 1);
        checkInt(this, value, offset, byteLength, limit - 1, -limit);
    }
    var i = 0;
    var mul = 1;
    var sub = 0;
    this[offset] = value & 0xFF;
    while(++i < byteLength && (mul *= 0x100)){
        if (value < 0 && sub === 0 && this[offset + i - 1] !== 0) sub = 1;
        this[offset + i] = (value / mul >> 0) - sub & 0xFF;
    }
    return offset + byteLength;
};
Buffer.prototype.writeIntBE = function writeIntBE(value, offset, byteLength, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) {
        var limit = Math.pow(2, 8 * byteLength - 1);
        checkInt(this, value, offset, byteLength, limit - 1, -limit);
    }
    var i = byteLength - 1;
    var mul = 1;
    var sub = 0;
    this[offset + i] = value & 0xFF;
    while(--i >= 0 && (mul *= 0x100)){
        if (value < 0 && sub === 0 && this[offset + i + 1] !== 0) sub = 1;
        this[offset + i] = (value / mul >> 0) - sub & 0xFF;
    }
    return offset + byteLength;
};
Buffer.prototype.writeInt8 = function writeInt8(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 1, 0x7f, -128);
    if (value < 0) value = 0xff + value + 1;
    this[offset] = value & 0xff;
    return offset + 1;
};
Buffer.prototype.writeInt16LE = function writeInt16LE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 2, 0x7fff, -32768);
    this[offset] = value & 0xff;
    this[offset + 1] = value >>> 8;
    return offset + 2;
};
Buffer.prototype.writeInt16BE = function writeInt16BE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 2, 0x7fff, -32768);
    this[offset] = value >>> 8;
    this[offset + 1] = value & 0xff;
    return offset + 2;
};
Buffer.prototype.writeInt32LE = function writeInt32LE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 4, 0x7fffffff, -2147483648);
    this[offset] = value & 0xff;
    this[offset + 1] = value >>> 8;
    this[offset + 2] = value >>> 16;
    this[offset + 3] = value >>> 24;
    return offset + 4;
};
Buffer.prototype.writeInt32BE = function writeInt32BE(value, offset, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkInt(this, value, offset, 4, 0x7fffffff, -2147483648);
    if (value < 0) value = 0xffffffff + value + 1;
    this[offset] = value >>> 24;
    this[offset + 1] = value >>> 16;
    this[offset + 2] = value >>> 8;
    this[offset + 3] = value & 0xff;
    return offset + 4;
};
function checkIEEE754(buf, value, offset, ext, max, min) {
    if (offset + ext > buf.length) throw new RangeError("Index out of range");
    if (offset < 0) throw new RangeError("Index out of range");
}
function writeFloat(buf, value, offset, littleEndian, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkIEEE754(buf, value, offset, 4, 3.4028234663852886e+38, -340282346638528860000000000000000000000);
    ieee754.write(buf, value, offset, littleEndian, 23, 4);
    return offset + 4;
}
Buffer.prototype.writeFloatLE = function writeFloatLE(value, offset, noAssert) {
    return writeFloat(this, value, offset, true, noAssert);
};
Buffer.prototype.writeFloatBE = function writeFloatBE(value, offset, noAssert) {
    return writeFloat(this, value, offset, false, noAssert);
};
function writeDouble(buf, value, offset, littleEndian, noAssert) {
    value = +value;
    offset = offset >>> 0;
    if (!noAssert) checkIEEE754(buf, value, offset, 8, 1.7976931348623157E+308, -179769313486231570000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000);
    ieee754.write(buf, value, offset, littleEndian, 52, 8);
    return offset + 8;
}
Buffer.prototype.writeDoubleLE = function writeDoubleLE(value, offset, noAssert) {
    return writeDouble(this, value, offset, true, noAssert);
};
Buffer.prototype.writeDoubleBE = function writeDoubleBE(value, offset, noAssert) {
    return writeDouble(this, value, offset, false, noAssert);
};
// copy(targetBuffer, targetStart=0, sourceStart=0, sourceEnd=buffer.length)
Buffer.prototype.copy = function copy(target, targetStart, start, end) {
    if (!Buffer.isBuffer(target)) throw new TypeError("argument should be a Buffer");
    if (!start) start = 0;
    if (!end && end !== 0) end = this.length;
    if (targetStart >= target.length) targetStart = target.length;
    if (!targetStart) targetStart = 0;
    if (end > 0 && end < start) end = start;
    // Copy 0 bytes; we're done
    if (end === start) return 0;
    if (target.length === 0 || this.length === 0) return 0;
    // Fatal error conditions
    if (targetStart < 0) throw new RangeError("targetStart out of bounds");
    if (start < 0 || start >= this.length) throw new RangeError("Index out of range");
    if (end < 0) throw new RangeError("sourceEnd out of bounds");
    // Are we oob?
    if (end > this.length) end = this.length;
    if (target.length - targetStart < end - start) end = target.length - targetStart + start;
    var len = end - start;
    if (this === target && typeof Uint8Array.prototype.copyWithin === "function") // Use built-in when available, missing from IE11
    this.copyWithin(targetStart, start, end);
    else Uint8Array.prototype.set.call(target, this.subarray(start, end), targetStart);
    return len;
};
// Usage:
//    buffer.fill(number[, offset[, end]])
//    buffer.fill(buffer[, offset[, end]])
//    buffer.fill(string[, offset[, end]][, encoding])
Buffer.prototype.fill = function fill(val, start, end, encoding) {
    // Handle string cases:
    if (typeof val === "string") {
        if (typeof start === "string") {
            encoding = start;
            start = 0;
            end = this.length;
        } else if (typeof end === "string") {
            encoding = end;
            end = this.length;
        }
        if (encoding !== undefined && typeof encoding !== "string") throw new TypeError("encoding must be a string");
        if (typeof encoding === "string" && !Buffer.isEncoding(encoding)) throw new TypeError("Unknown encoding: " + encoding);
        if (val.length === 1) {
            var code = val.charCodeAt(0);
            if (encoding === "utf8" && code < 128 || encoding === "latin1") // Fast path: If `val` fits into a single byte, use that numeric value.
            val = code;
        }
    } else if (typeof val === "number") val = val & 255;
    else if (typeof val === "boolean") val = Number(val);
    // Invalid ranges are not set to a default, so can range check early.
    if (start < 0 || this.length < start || this.length < end) throw new RangeError("Out of range index");
    if (end <= start) return this;
    start = start >>> 0;
    end = end === undefined ? this.length : end >>> 0;
    if (!val) val = 0;
    var i;
    if (typeof val === "number") for(i = start; i < end; ++i)this[i] = val;
    else {
        var bytes = Buffer.isBuffer(val) ? val : Buffer.from(val, encoding);
        var len = bytes.length;
        if (len === 0) throw new TypeError('The value "' + val + '" is invalid for argument "value"');
        for(i = 0; i < end - start; ++i)this[i + start] = bytes[i % len];
    }
    return this;
};
// HELPER FUNCTIONS
// ================
var INVALID_BASE64_RE = /[^+/0-9A-Za-z-_]/g;
function base64clean(str) {
    // Node takes equal signs as end of the Base64 encoding
    str = str.split("=")[0];
    // Node strips out invalid characters like \n and \t from the string, base64-js does not
    str = str.trim().replace(INVALID_BASE64_RE, "");
    // Node converts strings with length < 2 to ''
    if (str.length < 2) return "";
    // Node allows for non-padded base64 strings (missing trailing ===), base64-js does not
    while(str.length % 4 !== 0)str = str + "=";
    return str;
}
function utf8ToBytes(string, units) {
    units = units || Infinity;
    var codePoint;
    var length = string.length;
    var leadSurrogate = null;
    var bytes = [];
    for(var i = 0; i < length; ++i){
        codePoint = string.charCodeAt(i);
        // is surrogate component
        if (codePoint > 0xD7FF && codePoint < 0xE000) {
            // last char was a lead
            if (!leadSurrogate) {
                // no lead yet
                if (codePoint > 0xDBFF) {
                    // unexpected trail
                    if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);
                    continue;
                } else if (i + 1 === length) {
                    // unpaired lead
                    if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);
                    continue;
                }
                // valid lead
                leadSurrogate = codePoint;
                continue;
            }
            // 2 leads in a row
            if (codePoint < 0xDC00) {
                if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);
                leadSurrogate = codePoint;
                continue;
            }
            // valid surrogate pair
            codePoint = (leadSurrogate - 0xD800 << 10 | codePoint - 0xDC00) + 0x10000;
        } else if (leadSurrogate) // valid bmp char, but last char was a lead
        {
            if ((units -= 3) > -1) bytes.push(0xEF, 0xBF, 0xBD);
        }
        leadSurrogate = null;
        // encode utf8
        if (codePoint < 0x80) {
            if ((units -= 1) < 0) break;
            bytes.push(codePoint);
        } else if (codePoint < 0x800) {
            if ((units -= 2) < 0) break;
            bytes.push(codePoint >> 0x6 | 0xC0, codePoint & 0x3F | 0x80);
        } else if (codePoint < 0x10000) {
            if ((units -= 3) < 0) break;
            bytes.push(codePoint >> 0xC | 0xE0, codePoint >> 0x6 & 0x3F | 0x80, codePoint & 0x3F | 0x80);
        } else if (codePoint < 0x110000) {
            if ((units -= 4) < 0) break;
            bytes.push(codePoint >> 0x12 | 0xF0, codePoint >> 0xC & 0x3F | 0x80, codePoint >> 0x6 & 0x3F | 0x80, codePoint & 0x3F | 0x80);
        } else throw new Error("Invalid code point");
    }
    return bytes;
}
function asciiToBytes(str) {
    var byteArray = [];
    for(var i = 0; i < str.length; ++i)// Node's code seems to be doing this and not & 0x7F..
    byteArray.push(str.charCodeAt(i) & 0xFF);
    return byteArray;
}
function utf16leToBytes(str, units) {
    var c, hi, lo;
    var byteArray = [];
    for(var i = 0; i < str.length; ++i){
        if ((units -= 2) < 0) break;
        c = str.charCodeAt(i);
        hi = c >> 8;
        lo = c % 256;
        byteArray.push(lo);
        byteArray.push(hi);
    }
    return byteArray;
}
function base64ToBytes(str) {
    return base64.toByteArray(base64clean(str));
}
function blitBuffer(src, dst, offset, length) {
    for(var i = 0; i < length; ++i){
        if (i + offset >= dst.length || i >= src.length) break;
        dst[i + offset] = src[i];
    }
    return i;
}
// ArrayBuffer or Uint8Array objects from other contexts (i.e. iframes) do not pass
// the `instanceof` check but they should be treated as of that type.
// See: https://github.com/feross/buffer/issues/166
function isInstance(obj, type) {
    return obj instanceof type || obj != null && obj.constructor != null && obj.constructor.name != null && obj.constructor.name === type.name;
}
function numberIsNaN(obj) {
    // For IE11 support
    return obj !== obj // eslint-disable-line no-self-compare
    ;
}
// Create lookup table for `toString('hex')`
// See: https://github.com/feross/buffer/issues/219
var hexSliceLookupTable = function() {
    var alphabet = "0123456789abcdef";
    var table = new Array(256);
    for(var i = 0; i < 16; ++i){
        var i16 = i * 16;
        for(var j = 0; j < 16; ++j)table[i16 + j] = alphabet[i] + alphabet[j];
    }
    return table;
}();

},{"9c62938f1dccc73c":"jvQRR","aceacb6a4531a9d2":"9hhlZ"}],"jvQRR":[function(require,module,exports) {
"use strict";
exports.byteLength = byteLength;
exports.toByteArray = toByteArray;
exports.fromByteArray = fromByteArray;
var lookup = [];
var revLookup = [];
var Arr = typeof Uint8Array !== "undefined" ? Uint8Array : Array;
var code = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
for(var i = 0, len = code.length; i < len; ++i){
    lookup[i] = code[i];
    revLookup[code.charCodeAt(i)] = i;
}
// Support decoding URL-safe base64 strings, as Node.js does.
// See: https://en.wikipedia.org/wiki/Base64#URL_applications
revLookup["-".charCodeAt(0)] = 62;
revLookup["_".charCodeAt(0)] = 63;
function getLens(b64) {
    var len = b64.length;
    if (len % 4 > 0) throw new Error("Invalid string. Length must be a multiple of 4");
    // Trim off extra bytes after placeholder bytes are found
    // See: https://github.com/beatgammit/base64-js/issues/42
    var validLen = b64.indexOf("=");
    if (validLen === -1) validLen = len;
    var placeHoldersLen = validLen === len ? 0 : 4 - validLen % 4;
    return [
        validLen,
        placeHoldersLen
    ];
}
// base64 is 4/3 + up to two characters of the original data
function byteLength(b64) {
    var lens = getLens(b64);
    var validLen = lens[0];
    var placeHoldersLen = lens[1];
    return (validLen + placeHoldersLen) * 3 / 4 - placeHoldersLen;
}
function _byteLength(b64, validLen, placeHoldersLen) {
    return (validLen + placeHoldersLen) * 3 / 4 - placeHoldersLen;
}
function toByteArray(b64) {
    var tmp;
    var lens = getLens(b64);
    var validLen = lens[0];
    var placeHoldersLen = lens[1];
    var arr = new Arr(_byteLength(b64, validLen, placeHoldersLen));
    var curByte = 0;
    // if there are placeholders, only get up to the last complete 4 chars
    var len = placeHoldersLen > 0 ? validLen - 4 : validLen;
    var i;
    for(i = 0; i < len; i += 4){
        tmp = revLookup[b64.charCodeAt(i)] << 18 | revLookup[b64.charCodeAt(i + 1)] << 12 | revLookup[b64.charCodeAt(i + 2)] << 6 | revLookup[b64.charCodeAt(i + 3)];
        arr[curByte++] = tmp >> 16 & 0xFF;
        arr[curByte++] = tmp >> 8 & 0xFF;
        arr[curByte++] = tmp & 0xFF;
    }
    if (placeHoldersLen === 2) {
        tmp = revLookup[b64.charCodeAt(i)] << 2 | revLookup[b64.charCodeAt(i + 1)] >> 4;
        arr[curByte++] = tmp & 0xFF;
    }
    if (placeHoldersLen === 1) {
        tmp = revLookup[b64.charCodeAt(i)] << 10 | revLookup[b64.charCodeAt(i + 1)] << 4 | revLookup[b64.charCodeAt(i + 2)] >> 2;
        arr[curByte++] = tmp >> 8 & 0xFF;
        arr[curByte++] = tmp & 0xFF;
    }
    return arr;
}
function tripletToBase64(num) {
    return lookup[num >> 18 & 0x3F] + lookup[num >> 12 & 0x3F] + lookup[num >> 6 & 0x3F] + lookup[num & 0x3F];
}
function encodeChunk(uint8, start, end) {
    var tmp;
    var output = [];
    for(var i = start; i < end; i += 3){
        tmp = (uint8[i] << 16 & 0xFF0000) + (uint8[i + 1] << 8 & 0xFF00) + (uint8[i + 2] & 0xFF);
        output.push(tripletToBase64(tmp));
    }
    return output.join("");
}
function fromByteArray(uint8) {
    var tmp;
    var len = uint8.length;
    var extraBytes = len % 3 // if we have 1 byte left, pad 2 bytes
    ;
    var parts = [];
    var maxChunkLength = 16383 // must be multiple of 3
    ;
    // go through the array every three bytes, we'll deal with trailing stuff later
    for(var i = 0, len2 = len - extraBytes; i < len2; i += maxChunkLength)parts.push(encodeChunk(uint8, i, i + maxChunkLength > len2 ? len2 : i + maxChunkLength));
    // pad the end with zeros, but make sure to not forget the extra bytes
    if (extraBytes === 1) {
        tmp = uint8[len - 1];
        parts.push(lookup[tmp >> 2] + lookup[tmp << 4 & 0x3F] + "==");
    } else if (extraBytes === 2) {
        tmp = (uint8[len - 2] << 8) + uint8[len - 1];
        parts.push(lookup[tmp >> 10] + lookup[tmp >> 4 & 0x3F] + lookup[tmp << 2 & 0x3F] + "=");
    }
    return parts.join("");
}

},{}],"9hhlZ":[function(require,module,exports) {
/*! ieee754. BSD-3-Clause License. Feross Aboukhadijeh <https://feross.org/opensource> */ exports.read = function(buffer, offset, isLE, mLen, nBytes) {
    var e, m;
    var eLen = nBytes * 8 - mLen - 1;
    var eMax = (1 << eLen) - 1;
    var eBias = eMax >> 1;
    var nBits = -7;
    var i = isLE ? nBytes - 1 : 0;
    var d = isLE ? -1 : 1;
    var s = buffer[offset + i];
    i += d;
    e = s & (1 << -nBits) - 1;
    s >>= -nBits;
    nBits += eLen;
    for(; nBits > 0; e = e * 256 + buffer[offset + i], i += d, nBits -= 8);
    m = e & (1 << -nBits) - 1;
    e >>= -nBits;
    nBits += mLen;
    for(; nBits > 0; m = m * 256 + buffer[offset + i], i += d, nBits -= 8);
    if (e === 0) e = 1 - eBias;
    else if (e === eMax) return m ? NaN : (s ? -1 : 1) * Infinity;
    else {
        m = m + Math.pow(2, mLen);
        e = e - eBias;
    }
    return (s ? -1 : 1) * m * Math.pow(2, e - mLen);
};
exports.write = function(buffer, value, offset, isLE, mLen, nBytes) {
    var e, m, c;
    var eLen = nBytes * 8 - mLen - 1;
    var eMax = (1 << eLen) - 1;
    var eBias = eMax >> 1;
    var rt = mLen === 23 ? Math.pow(2, -24) - Math.pow(2, -77) : 0;
    var i = isLE ? 0 : nBytes - 1;
    var d = isLE ? 1 : -1;
    var s = value < 0 || value === 0 && 1 / value < 0 ? 1 : 0;
    value = Math.abs(value);
    if (isNaN(value) || value === Infinity) {
        m = isNaN(value) ? 1 : 0;
        e = eMax;
    } else {
        e = Math.floor(Math.log(value) / Math.LN2);
        if (value * (c = Math.pow(2, -e)) < 1) {
            e--;
            c *= 2;
        }
        if (e + eBias >= 1) value += rt / c;
        else value += rt * Math.pow(2, 1 - eBias);
        if (value * c >= 2) {
            e++;
            c /= 2;
        }
        if (e + eBias >= eMax) {
            m = 0;
            e = eMax;
        } else if (e + eBias >= 1) {
            m = (value * c - 1) * Math.pow(2, mLen);
            e = e + eBias;
        } else {
            m = value * Math.pow(2, eBias - 1) * Math.pow(2, mLen);
            e = 0;
        }
    }
    for(; mLen >= 8; buffer[offset + i] = m & 0xff, i += d, m /= 256, mLen -= 8);
    e = e << mLen | m;
    eLen += mLen;
    for(; eLen > 0; buffer[offset + i] = e & 0xff, i += d, e /= 256, eLen -= 8);
    buffer[offset + i - d] |= s * 128;
};

},{}],"bPDAx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "complex", ()=>complex);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Converts two real numbers to a complex number.
 *
 * Given a tensor `real` representing the real part of a complex number, and a
 * tensor `imag` representing the imaginary part of a complex number, this
 * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],
 * where r represents the real part and i represents the imag part.
 *
 * The input tensors real and imag must have the same shape.
 *
 * ```js
 * const real = tf.tensor1d([2.25, 3.25]);
 * const imag = tf.tensor1d([4.75, 5.75]);
 * const complex = tf.complex(real, imag);
 *
 * complex.print();
 * ```
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function complex_(real, imag) {
    const $real = (0, _tensorUtilEnv.convertToTensor)(real, "real", "complex");
    const $imag = (0, _tensorUtilEnv.convertToTensor)(imag, "imag", "complex");
    _util.assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` + `must match in call to tf.complex().`);
    const inputs = {
        real: $real,
        imag: $imag
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Complex), inputs);
}
const complex = /* @__PURE__ */ (0, _operation.op)({
    complex_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6vfHI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "inferShape", ()=>inferShape);
parcelHelpers.export(exports, "convertToTensor", ()=>convertToTensor);
parcelHelpers.export(exports, "convertToTensorArray", ()=>convertToTensorArray);
var _engine = require("./engine");
var _environment = require("./environment");
var _tensor = require("./tensor");
var _types = require("./types");
var _util = require("./util");
var _utilBase = require("./util_base");
function inferShape(val, dtype) {
    let firstElem = val;
    if ((0, _util.isTypedArray)(val)) return dtype === "string" ? [] : [
        val.length
    ];
    if ((0, _types.isWebGLData)(val)) {
        const usedChannels = val.channels || "RGBA";
        return [
            val.height,
            val.width * usedChannels.length
        ];
    } else if ((0, _types.isWebGPUData)(val)) return [
        val.buffer.size / (dtype == null ? 4 : (0, _utilBase.bytesPerElement)(dtype))
    ];
    if (!Array.isArray(val)) return []; // Scalar.
    const shape = [];
    while(Array.isArray(firstElem) || (0, _util.isTypedArray)(firstElem) && dtype !== "string"){
        shape.push(firstElem.length);
        firstElem = firstElem[0];
    }
    if (Array.isArray(val) && (0, _environment.env)().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")) deepAssertShapeConsistency(val, shape, []);
    return shape;
}
function deepAssertShapeConsistency(val, shape, indices) {
    indices = indices || [];
    if (!Array.isArray(val) && !(0, _util.isTypedArray)(val)) {
        (0, _util.assert)(shape.length === 0, ()=>`Element arr[${indices.join("][")}] is a primitive, ` + `but should be an array/TypedArray of ${shape[0]} elements`);
        return;
    }
    (0, _util.assert)(shape.length > 0, ()=>`Element arr[${indices.join("][")}] should be a primitive, ` + `but is an array of ${val.length} elements`);
    (0, _util.assert)(val.length === shape[0], ()=>`Element arr[${indices.join("][")}] should have ${shape[0]} ` + `elements, but has ${val.length} elements`);
    const subShape = shape.slice(1);
    for(let i = 0; i < val.length; ++i)deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
}
function assertDtype(expectedDtype, actualDType, argName, functionName) {
    if (expectedDtype === "string_or_numeric") return;
    if (expectedDtype == null) throw new Error(`Expected dtype cannot be null.`);
    if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") throw new Error(`Argument '${argName}' passed to '${functionName}' must ` + `be ${expectedDtype} tensor, but got ${actualDType} tensor`);
}
function convertToTensor(x, argName, functionName, parseAsDtype = "numeric") {
    if (x instanceof (0, _tensor.getGlobalTensorClass)()) {
        assertDtype(parseAsDtype, x.dtype, argName, functionName);
        return x;
    }
    let inferredDtype = (0, _util.inferDtype)(x);
    // If the user expects a bool/int/float, use that info to update the
    // inferredDtype when it is not a string.
    if (inferredDtype !== "string" && [
        "bool",
        "int32",
        "float32"
    ].indexOf(parseAsDtype) >= 0) inferredDtype = parseAsDtype;
    assertDtype(parseAsDtype, inferredDtype, argName, functionName);
    if (x == null || !(0, _util.isTypedArray)(x) && !Array.isArray(x) && typeof x !== "number" && typeof x !== "boolean" && typeof x !== "string") {
        const type = x == null ? "null" : x.constructor.name;
        throw new Error(`Argument '${argName}' passed to '${functionName}' must be a ` + `Tensor or TensorLike, but got '${type}'`);
    }
    const inferredShape = inferShape(x, inferredDtype);
    if (!(0, _util.isTypedArray)(x) && !Array.isArray(x)) x = [
        x
    ];
    const skipTypedArray = true;
    const values = inferredDtype !== "string" ? (0, _util.toTypedArray)(x, inferredDtype) : (0, _util.flatten)(x, [], skipTypedArray);
    return (0, _engine.ENGINE).makeTensor(values, inferredShape, inferredDtype);
}
function convertToTensorArray(arg, argName, functionName, parseAsDtype = "numeric") {
    if (!Array.isArray(arg)) throw new Error(`Argument ${argName} passed to ${functionName} must be a ` + "`Tensor[]` or `TensorLike[]`");
    const tensors = arg;
    return tensors.map((t, i)=>convertToTensor(t, `${argName}[${i}]`, functionName, parseAsDtype));
}

},{"./engine":"1g2qo","./environment":"dXmHm","./tensor":"cJNoh","./types":"10R8n","./util":"ctavo","./util_base":"4Vo4a","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ip64W":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "OP_SCOPE_SUFFIX", ()=>OP_SCOPE_SUFFIX);
/**
 * Used for wrapping functions that perform math operations on
 * Tensors. The function will be wrapped in a named scope that cleans all
 * memory usage after the function is done.
 */ parcelHelpers.export(exports, "op", ()=>op);
var _engine = require("../engine");
var _util = require("../util");
const OP_SCOPE_SUFFIX = "__op";
function op(f) {
    const keys = Object.keys(f);
    if (keys.length !== 1) throw new Error(`Please provide an object with a single key ` + `(operation name) mapping to a function. Got an object with ` + `${keys.length} keys.`);
    let opName = keys[0];
    const fn = f[opName];
    // Strip the underscore from the end of the function name.
    if (opName.endsWith("_")) opName = opName.substring(0, opName.length - 1);
    // add an __op suffix to distinguish ops from kernels in tf.profile
    opName = opName + OP_SCOPE_SUFFIX;
    // tslint:disable-next-line:no-any
    const f2 = (...args)=>{
        (0, _engine.ENGINE).startScope(opName);
        try {
            const result = fn(...args);
            if ((0, _util.isPromise)(result)) console.error("Cannot return a Promise inside of tidy.");
            (0, _engine.ENGINE).endScope(result);
            return result;
        } catch (ex) {
            (0, _engine.ENGINE).endScope(null);
            throw ex;
        }
    };
    Object.defineProperty(f2, "name", {
        value: opName,
        configurable: true
    });
    // tslint:disable-next-line:no-any
    return f2;
}

},{"../engine":"1g2qo","../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jdbb1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates a `tf.Tensor` with the provided values, shape and dtype.
 *
 * ```js
 * // Pass an array of values to create a vector.
 * tf.tensor([1, 2, 3, 4]).print();
 * ```
 *
 * ```js
 * // Pass a nested array of values to make a matrix or a higher
 * // dimensional tensor.
 * tf.tensor([[1, 2], [3, 4]]).print();
 * ```
 *
 * ```js
 * // Pass a flat array and specify a shape yourself.
 * tf.tensor([1, 2, 3, 4], [2, 2]).print();
 * ```
 *
 * ```js
 * // Pass a `WebGLData` object and specify a shape yourself.
 *
 * // This makes it possible for TF.js applications to avoid GPU / CPU sync.
 * // For example, if your application includes a preprocessing step on the GPU,
 * // you could upload the GPU output directly to TF.js, rather than first
 * // downloading the values.
 *
 * // Example for WebGL2:
 * if (tf.findBackend('custom-webgl') == null) {
 *   const customCanvas = document.createElement('canvas');
 *   const customBackend = new tf.MathBackendWebGL(customCanvas);
 *   tf.registerBackend('custom-webgl', () => customBackend);
 * }
 * const savedBackend = tf.getBackend();
 * await tf.setBackend('custom-webgl');
 * const gl = tf.backend().gpgpu.gl;
 * const texture = gl.createTexture();
 * const tex2d = gl.TEXTURE_2D;
 * const width = 2;
 * const height = 2;
 *
 * gl.bindTexture(tex2d, texture);
 * gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);
 * gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);
 * gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST);
 * gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST);
 * gl.texImage2D(
 *   tex2d, 0, gl.RGBA32F, // internalFormat
 *   width, height, 0,
 *   gl.RGBA, // textureFormat
 *   gl.FLOAT, // textureType
 *   new Float32Array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
 * );
 *
 * // Currently, the `texture` has 4 pixels:
 * // Pixel0 is {R:0, G:1, B:2, A:3}
 * // Pixel1 is {R:4, G:5, B:6, A:7}
 * // Pixel2 is {R:8, G:9, B:10, A:11}
 * // Pixel3 is {R:12, G:13, B:14, A:15}
 *
 * const logicalShape = [height * width * 2];
 * const a = tf.tensor({texture, height, width, channels: 'BR'}, logicalShape);
 * a.print();
 * // Tensor value will be [2, 0, 6, 4, 10, 8, 14, 12], since [2, 0] is the
 * // values of 'B' and 'R' channels of Pixel0, [6, 4] is the values of 'B' and
 * 'R'
 * // channels of Pixel1...
 *
 * // For postprocessing on the GPU, it's possible to retrieve the texture
 * // backing any tensor by calling the tensor's `dataToGPU` method like
 * // so:
 *
 * const tex = a.dataToGPU();
 * await tf.setBackend(savedBackend);
 * ```
 *
 * ```js
 * // Pass a `WebGPUData` object and specify a shape yourself.
 *
 * // This makes it possible for TF.js applications to avoid GPU / CPU sync.
 * // For example, if your application includes a preprocessing step on the GPU,
 * // you could upload the GPU output directly to TF.js, rather than first
 * // downloading the values. Unlike WebGL, this optionally supports zero copy
 * // by WebGPUData.zeroCopy. When zeroCopy is false or undefined(default), this
 * // passing GPUBuffer can be destroyed after tensor is created. When zeroCopy
 * // is true, this GPUBuffer is bound directly by the tensor, so do not destroy
 * // this GPUBuffer until all access is done.
 *
 * // Example for WebGPU:
 * function createGPUBufferFromData(device, data, dtype) {
 *   const bytesPerElement = 4;
 *   const sizeInBytes = data.length * bytesPerElement;
 *
 *   const gpuWriteBuffer = device.createBuffer({
 *     mappedAtCreation: true,
 *     size: sizeInBytes,
 *     usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC
 *   });
 *   const arrayBuffer = gpuWriteBuffer.getMappedRange();
 *   if (dtype === 'float32') {
 *     new Float32Array(arrayBuffer).set(data);
 *   } else if (dtype === 'int32') {
 *     new Int32Array(arrayBuffer).set(data);
 *   } else {
 *     throw new Error(
 *         `Creating tensor from GPUBuffer only supports` +
 *         `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
 *   }
 *   gpuWriteBuffer.unmap();
 *
 *   const gpuReadBuffer = device.createBuffer({
 *     mappedAtCreation: false,
 *     size: sizeInBytes,
 *     usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.STORAGE |
 *         GPUBufferUsage.COPY_SRC
 *   });
 *
 *   const copyEncoder = device.createCommandEncoder();
 *   copyEncoder.copyBufferToBuffer(
 *       gpuWriteBuffer, 0, gpuReadBuffer, 0, sizeInBytes);
 *   const copyCommands = copyEncoder.finish();
 *   device.queue.submit([copyCommands]);
 *   gpuWriteBuffer.destroy();
 *   return gpuReadBuffer;
 * }
 *
 * const savedBackend = tf.getBackend();
 * await tf.setBackend('webgpu').catch(
 *     () => {throw new Error(
 *         'Failed to use WebGPU backend. Please use Chrome Canary to run.')});
 * const dtype = 'float32';
 * const device = tf.backend().device;
 * const aData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16];
 * const bData = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4];
 * const expected = [2, 4, 6, 8, 6, 8, 10, 12, 10, 12, 14, 16, 14, 16, 18, 20];
 * const aBuffer = createGPUBufferFromData(device, aData, dtype);
 * const shape = [aData.length];
 * // To use zeroCopy, use {buffer: aBuffer, zeroCopy: true} instead and destroy
 * // aBuffer untill all access is done.
 * const a = tf.tensor({buffer: aBuffer}, shape, dtype);
 * const b = tf.tensor(bData, shape, dtype);
 * const result = tf.add(a, b);
 * result.print();
 * a.dispose();
 * b.dispose();
 * result.dispose();
 * aBuffer.destroy();
 * await tf.setBackend(savedBackend);
 * ```
 * @param values The values of the tensor. Can be nested array of numbers,
 * or a flat array, or a `TypedArray`(At the moment it supports Uint8Array,
 * Uint8ClampedArray, Int32Array, Float32Array) data types, or a `WebGLData`
 * object, or a `WebGPUData` object. If the values are strings, they will be
 * encoded as utf-8 and kept as `Uint8Array[]`. If the values is a `WebGLData`
 * object, the dtype could only be 'float32' or 'int32' and the object has to
 * have: 1. texture, a `WebGLTexture`, the texture must share the same
 * `WebGLRenderingContext` with TFJS's WebGL backend (you could create a custom
 * WebGL backend from your texture's canvas) and the internal texture format
 * for the input texture must be floating point or normalized integer; 2.
 * height, the height of the texture; 3. width, the width of the texture; 4.
 * channels, a non-empty subset of 'RGBA', indicating the values of which
 * channels will be passed to the tensor, such as 'R' or 'BR' (The order of the
 * channels affect the order of tensor values. ). (If the values passed from
 * texture is less than the tensor size, zeros will be padded at the rear.). If
 * the values is a `WebGPUData` object, the dtype could only be 'float32' or
 * 'int32 and the object has to have: buffer, a `GPUBuffer`. The buffer must:
 * 1. share the same `GPUDevice` with TFJS's WebGPU backend; 2. buffer.usage
 * should at least support GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC; 3.
 * buffer.size should not be smaller than the byte size of tensor shape.
 * WebGPUData optionally supports zero copy by flag zeroCopy. When zeroCopy is
 * false or undefined(default),this passing GPUBuffer can be destroyed after
 * tensor is created. When zeroCopy is true, this GPUBuffer is bound directly
 * by the tensor, so do not destroy this GPUBuffer until all access is done.
 * @param shape The shape of the tensor. Optional. If not provided,
 *   it is inferred from `values`.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "tensor", ()=>tensor);
var _tensorUtilEnv = require("../tensor_util_env");
var _tensorOpsUtil = require("./tensor_ops_util");
function tensor(values, shape, dtype) {
    const inferredShape = (0, _tensorUtilEnv.inferShape)(values, dtype);
    return (0, _tensorOpsUtil.makeTensor)(values, shape, inferredShape, dtype);
}

},{"../tensor_util_env":"6vfHI","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"adYmX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/** This is shared code across all tensor creation methods. */ parcelHelpers.export(exports, "makeTensor", ()=>makeTensor);
var _engine = require("../engine");
var _types = require("../types");
var _util = require("../util");
function makeTensor(values, shape, inferredShape, dtype) {
    if (dtype == null) dtype = (0, _util.inferDtype)(values);
    else if (dtype === "complex64") throw new Error(`Cannot construct a complex64 tensor directly. ` + `Please use tf.complex(real, imag).`);
    if ((0, _types.isWebGPUData)(values) || (0, _types.isWebGLData)(values)) {
        if (dtype !== "float32" && dtype !== "int32") throw new Error(`Creating tensor from GPU data only supports ` + `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
        return (0, _engine.ENGINE).backend.createTensorFromGPUData(values, shape || inferredShape, dtype);
    }
    if (!(0, _util.isTypedArray)(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
    // Verify that the shape matches the inferred shape.
    if (shape != null) {
        (0, _util.assertNonNegativeIntegerDimensions)(shape);
        const providedSize = (0, _util.sizeFromShape)(shape);
        const inferredSize = (0, _util.sizeFromShape)(inferredShape);
        (0, _util.assert)(providedSize === inferredSize, ()=>`Based on the provided shape, [${shape}], the tensor should have ` + `${providedSize} values but has ${inferredSize}`);
        for(let i = 0; i < inferredShape.length; ++i){
            const inferred = inferredShape[i];
            const flatDimsDontMatch = i === inferredShape.length - 1 ? inferred !== (0, _util.sizeFromShape)(shape.slice(i)) : true;
            (0, _util.assert)(inferredShape[i] === shape[i] || !flatDimsDontMatch, ()=>`Error creating a new Tensor. Inferred shape ` + `(${inferredShape}) does not match the provided ` + `shape (${shape}). `);
        }
    }
    if (!(0, _util.isTypedArray)(values) && !Array.isArray(values)) values = [
        values
    ];
    shape = shape || inferredShape;
    values = dtype !== "string" ? (0, _util.toTypedArray)(values, dtype) : (0, _util.flatten)(values, [], true);
    return (0, _engine.ENGINE).makeTensor(values, shape, dtype);
}

},{"../engine":"1g2qo","../types":"10R8n","../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ixgVc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /* Type definitions for exporting and importing of models. */ /**
 * A map from Tensor dtype to number of bytes per element of the Tensor.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DTYPE_VALUE_SIZE_MAP", ()=>DTYPE_VALUE_SIZE_MAP);
const DTYPE_VALUE_SIZE_MAP = {
    "float32": 4,
    "float16": 2,
    "int32": 4,
    "uint16": 2,
    "uint8": 1,
    "bool": 1,
    "complex64": 8
};

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gHH7C":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Wraps a list of ArrayBuffers into a `slice()`-able object without allocating
 * a large ArrayBuffer.
 *
 * Allocating large ArrayBuffers (~2GB) can be unstable on Chrome. TFJS loads
 * its weights as a list of (usually) 4MB ArrayBuffers and then slices the
 * weight tensors out of them. For small models, it's safe to concatenate all
 * the weight buffers into a single ArrayBuffer and then slice the weight
 * tensors out of it, but for large models, a different approach is needed.
 */ parcelHelpers.export(exports, "CompositeArrayBuffer", ()=>CompositeArrayBuffer);
/**
 * Search for an element of a sorted array.
 *
 * @param sortedArray The sorted array to search
 * @param compare A function to compare the current value against the searched
 *     value. Return 0 on a match, negative if the searched value is less than
 *     the value passed to the function, and positive if the searched value is
 *     greater than the value passed to the function.
 * @returns The index of the element, or -1 if it's not in the array.
 */ parcelHelpers.export(exports, "search", ()=>search);
var _util = require("../util");
class CompositeArrayBuffer {
    /**
     * Concatenate a number of ArrayBuffers into one.
     *
     * @param buffers An array of ArrayBuffers to concatenate, or a single
     *     ArrayBuffer.
     * @returns Result of concatenating `buffers` in order.
     */ static join(buffers) {
        return new CompositeArrayBuffer(buffers).slice();
    }
    constructor(buffers){
        this.shards = [];
        this.previousShardIndex = 0;
        if (buffers == null) return;
        // Normalize the `buffers` input to be `ArrayBuffer[]`.
        if (!(buffers instanceof Array)) buffers = [
            buffers
        ];
        buffers = buffers.map((bufferOrTypedArray)=>{
            if (_util.isTypedArray(bufferOrTypedArray)) return bufferOrTypedArray.buffer;
            return bufferOrTypedArray;
        });
        // Skip setting up shards if there are no buffers.
        if (buffers.length === 0) return;
        this.bufferUniformSize = buffers[0].byteLength;
        let start = 0;
        for(let i = 0; i < buffers.length; i++){
            const buffer = buffers[i];
            // Check that all buffers except the last one have the same length.
            if (i !== buffers.length - 1 && buffer.byteLength !== this.bufferUniformSize) // Unset the buffer uniform size, since the buffer sizes are not
            // uniform.
            this.bufferUniformSize = undefined;
            // Create the shards, including their start and end points.
            const end = start + buffer.byteLength;
            this.shards.push({
                buffer,
                start,
                end
            });
            start = end;
        }
        // Set the byteLenghth
        if (this.shards.length === 0) this.byteLength = 0;
        this.byteLength = this.shards[this.shards.length - 1].end;
    }
    slice(start = 0, end = this.byteLength) {
        // If there are no shards, then the CompositeArrayBuffer was initialized
        // with no data.
        if (this.shards.length === 0) return new ArrayBuffer(0);
        // NaN is treated as zero for slicing. This matches ArrayBuffer's behavior.
        start = isNaN(Number(start)) ? 0 : start;
        end = isNaN(Number(end)) ? 0 : end;
        // Fix the bounds to within the array.
        start = Math.max(0, start);
        end = Math.min(this.byteLength, end);
        if (end <= start) return new ArrayBuffer(0);
        const startShardIndex = this.findShardForByte(start);
        if (startShardIndex === -1) // This should not happen since the start and end indices are always
        // within 0 and the composite array's length.
        throw new Error(`Could not find start shard for byte ${start}`);
        const size = end - start;
        const outputBuffer = new ArrayBuffer(size);
        const outputArray = new Uint8Array(outputBuffer);
        let sliced = 0;
        for(let i = startShardIndex; i < this.shards.length; i++){
            const shard = this.shards[i];
            const globalStart = start + sliced;
            const localStart = globalStart - shard.start;
            const outputStart = sliced;
            const globalEnd = Math.min(end, shard.end);
            const localEnd = globalEnd - shard.start;
            const outputSlice = new Uint8Array(shard.buffer, localStart, localEnd - localStart);
            outputArray.set(outputSlice, outputStart);
            sliced += outputSlice.length;
            if (end < shard.end) break;
        }
        return outputBuffer;
    }
    /**
     * Get the index of the shard that contains the byte at `byteIndex`.
     */ findShardForByte(byteIndex) {
        if (this.shards.length === 0 || byteIndex < 0 || byteIndex >= this.byteLength) return -1;
        // If the buffers have a uniform size, compute the shard directly.
        if (this.bufferUniformSize != null) {
            this.previousShardIndex = Math.floor(byteIndex / this.bufferUniformSize);
            return this.previousShardIndex;
        }
        // If the buffers don't have a uniform size, we need to search for the
        // shard. That means we need a function to check where the byteIndex lies
        // relative to a given shard.
        function check(shard) {
            if (byteIndex < shard.start) return -1;
            if (byteIndex >= shard.end) return 1;
            return 0;
        }
        // For efficiency, try the previous shard first.
        if (check(this.shards[this.previousShardIndex]) === 0) return this.previousShardIndex;
        // Otherwise, use a generic search function.
        // This should almost never end up being used in practice since the weight
        // entries should always be in order.
        const index = search(this.shards, check);
        if (index === -1) return -1;
        this.previousShardIndex = index;
        return this.previousShardIndex;
    }
}
function search(sortedArray, compare) {
    // Binary search
    let min = 0;
    let max = sortedArray.length;
    while(min <= max){
        const middle = Math.floor((max - min) / 2) + min;
        const side = compare(sortedArray[middle]);
        if (side === 0) return middle;
        else if (side < 0) max = middle;
        else min = middle + 1;
    }
    return -1;
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eO1Ja":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Enables production mode which disables correctness checks in favor of
 * performance.
 *
 * @doc {heading: 'Environment'}
 */ parcelHelpers.export(exports, "enableProdMode", ()=>enableProdMode);
/**
 * Enables debug mode which will log information about all executed kernels:
 * the elapsed time of the kernel execution, as well as the rank, shape, and
 * size of the output tensor.
 *
 * Debug mode will significantly slow down your application as it will
 * download the result of every operation to the CPU. This should not be used in
 * production. Debug mode does not affect the timing information of the kernel
 * execution as we do not measure download time in the kernel execution time.
 *
 * See also: `tf.profile`, `tf.memory`.
 *
 * @doc {heading: 'Environment'}
 */ parcelHelpers.export(exports, "enableDebugMode", ()=>enableDebugMode);
/** Globally disables deprecation warnings */ parcelHelpers.export(exports, "disableDeprecationWarnings", ()=>disableDeprecationWarnings);
/** Warn users about deprecated functionality. */ parcelHelpers.export(exports, "deprecationWarn", ()=>deprecationWarn);
/**
 * Dispose all variables kept in backend engine.
 *
 * @doc {heading: 'Environment'}
 */ parcelHelpers.export(exports, "disposeVariables", ()=>disposeVariables);
/**
 * It returns the global engine that keeps track of all tensors and backends.
 *
 * @doc {heading: 'Environment'}
 */ parcelHelpers.export(exports, "engine", ()=>engine);
/**
 * Returns memory info at the current time in the program. The result is an
 * object with the following properties:
 *
 * - `numBytes`: Number of bytes allocated (undisposed) at this time.
 * - `numTensors`: Number of unique tensors allocated.
 * - `numDataBuffers`: Number of unique data buffers allocated
 *   (undisposed) at this time, which is ≤ the number of tensors
 *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same
 *   data buffer with `a`).
 * - `unreliable`: True if the memory usage is unreliable. See `reasons` when
 *    `unreliable` is true.
 * - `reasons`: `string[]`, reasons why the memory is unreliable, present if
 *    `unreliable` is true.
 *
 * WebGL Properties:
 * - `numBytesInGPU`: Number of bytes allocated (undisposed) in the GPU only at
 *     this time.
 *
 * @doc {heading: 'Performance', subheading: 'Memory'}
 */ parcelHelpers.export(exports, "memory", ()=>memory);
/**
 * Executes the provided function `f()` and returns a promise that resolves
 * with information about the function's memory use:
 * - `newBytes`: the number of new bytes allocated
 * - `newTensors`: the number of new tensors created
 * - `peakBytes`: the peak number of bytes allocated
 * - `kernels`: an array of objects for each kernel involved that reports
 * their input and output shapes, number of bytes used, and number of new
 * tensors created.
 * - `kernelNames`: an array of unique strings with just the names of the
 * kernels in the `kernels` array.
 *
 * ```js
 * const profile = await tf.profile(() => {
 *   const x = tf.tensor1d([1, 2, 3]);
 *   let x2 = x.square();
 *   x2.dispose();
 *   x2 = x.square();
 *   x2.dispose();
 *   return x;
 * });
 *
 * console.log(`newBytes: ${profile.newBytes}`);
 * console.log(`newTensors: ${profile.newTensors}`);
 * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>
 * k.totalBytesSnapshot)}`);
 * ```
 *
 *
 * @doc {heading: 'Performance', subheading: 'Profile'}
 */ parcelHelpers.export(exports, "profile", ()=>profile);
/**
 * Executes the provided function `fn` and after it is executed, cleans up all
 * intermediate tensors allocated by `fn` except those returned by `fn`.
 * `fn` must not return a Promise (async functions not allowed). The returned
 * result can be a complex object.
 *
 * Using this method helps avoid memory leaks. In general, wrap calls to
 * operations in `tf.tidy` for automatic memory cleanup.
 *
 * NOTE: Variables do *not* get cleaned up when inside a tidy(). If you want to
 * dispose variables, please use `tf.disposeVariables` or call dispose()
 * directly on variables.
 *
 * ```js
 * // y = 2 ^ 2 + 1
 * const y = tf.tidy(() => {
 *   // a, b, and one will be cleaned up when the tidy ends.
 *   const one = tf.scalar(1);
 *   const a = tf.scalar(2);
 *   const b = a.square();
 *
 *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
 *
 *   // The value returned inside the tidy function will return
 *   // through the tidy, in this case to the variable y.
 *   return b.add(one);
 * });
 *
 * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
 * y.print();
 * ```
 *
 * @param nameOrFn The name of the closure, or the function to execute.
 *     If a name is provided, the 2nd argument should be the function.
 *     If debug mode is on, the timing and the memory usage of the function
 *     will be tracked and displayed on the console using the provided name.
 * @param fn The function to execute.
 *
 * @doc {heading: 'Performance', subheading: 'Memory'}
 */ parcelHelpers.export(exports, "tidy", ()=>tidy);
/**
 * Disposes any `tf.Tensor`s found within the provided object.
 *
 * @param container an object that may be a `tf.Tensor` or may directly
 *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If
 *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing
 *     happens. In general it is safe to pass any object here, except that
 *     `Promise`s are not supported.
 *
 * @doc {heading: 'Performance', subheading: 'Memory'}
 */ parcelHelpers.export(exports, "dispose", ()=>dispose);
/**
 * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed
 * automatically.
 *
 * ```js
 * let b;
 * const y = tf.tidy(() => {
 *   const one = tf.scalar(1);
 *   const a = tf.scalar(2);
 *
 *   // b will not be cleaned up by the tidy. a and one will be cleaned up
 *   // when the tidy ends.
 *   b = tf.keep(a.square());
 *
 *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
 *
 *   // The value returned inside the tidy function will return
 *   // through the tidy, in this case to the variable y.
 *   return b.add(one);
 * });
 *
 * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
 * console.log('y:');
 * y.print();
 * console.log('b:');
 * b.print();
 * ```
 *
 * @param result The tensor to keep from being disposed.
 *
 * @doc {heading: 'Performance', subheading: 'Memory'}
 */ parcelHelpers.export(exports, "keep", ()=>keep);
/**
 * Executes `f()` and returns a promise that resolves with timing
 * information.
 *
 * The result is an object with the following properties:
 *
 * - `wallMs`: Wall execution time.
 * - `kernelMs`: Kernel execution time, ignoring data transfer. If using the
 * WebGL backend and the query timer extension is not available, this will
 * return an error object.
 * - On `WebGL` The following additional properties exist:
 *   - `uploadWaitMs`: CPU blocking time on texture uploads.
 *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
 *
 * ```js
 * const x = tf.randomNormal([20, 20]);
 * const time = await tf.time(() => x.matMul(x));
 *
 * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);
 * ```
 *
 * @param f The function to execute and time.
 *
 * @doc {heading: 'Performance', subheading: 'Timing'}
 */ parcelHelpers.export(exports, "time", ()=>time);
/**
 * Sets the backend (cpu, webgl, wasm, etc) responsible for creating tensors and
 * executing operations on those tensors. Returns a promise that resolves
 * to a boolean if the backend initialization was successful.
 *
 * Note this disposes the current backend, if any, as well as any tensors
 * associated with it. A new backend is initialized, even if it is of the
 * same type as the previous one.
 *
 * @param backendName The name of the backend. Currently supports
 *     `'webgl'|'cpu'` in the browser, `'tensorflow'` under node.js
 *     (requires tfjs-node), and `'wasm'` (requires tfjs-backend-wasm).
 *
 * @doc {heading: 'Backends'}
 */ parcelHelpers.export(exports, "setBackend", ()=>setBackend);
/**
 * Returns a promise that resolves when the currently selected backend (or the
 * highest priority one) has initialized. Await this promise when you are using
 * a backend that has async initialization.
 *
 * @doc {heading: 'Backends'}
 */ parcelHelpers.export(exports, "ready", ()=>ready);
/**
 * Returns the current backend name (cpu, webgl, etc). The backend is
 * responsible for creating tensors and executing operations on those tensors.
 *
 * @doc {heading: 'Backends'}
 */ parcelHelpers.export(exports, "getBackend", ()=>getBackend);
/**
 * Removes a backend and the registered factory.
 *
 * @doc {heading: 'Backends'}
 */ parcelHelpers.export(exports, "removeBackend", ()=>removeBackend);
/**
 * Finds the backend registered under the provided name. Returns null if the
 * name is not in the registry, or the registration hasn't finished yet.
 */ parcelHelpers.export(exports, "findBackend", ()=>findBackend);
/**
 * Finds the backend factory registered under the provided name. Returns a
 * function that produces a new backend when called. Returns null if the name
 * is not in the registry.
 */ parcelHelpers.export(exports, "findBackendFactory", ()=>findBackendFactory);
/**
 * Registers a global backend. The registration should happen when importing
 * a module file (e.g. when importing `backend_webgl.ts`), and is used for
 * modular builds (e.g. custom tfjs bundle with only webgl support).
 *
 * @param factory The backend factory function. When called, it should
 * return a backend instance, or a promise of an instance.
 * @param priority The priority of the backend (higher = more important).
 *     In case multiple backends are registered, the priority is used to find
 *     the best backend. Defaults to 1.
 * @return False if there is already a registered backend under this name, true
 *     if not.
 *
 * @doc {heading: 'Backends'}
 */ parcelHelpers.export(exports, "registerBackend", ()=>registerBackend);
/**
 * Gets the current backend. If no backends have been initialized, this will
 * attempt to initialize the best backend. Will throw an error if the highest
 * priority backend has async initialization, in which case you should call
 * 'await tf.ready()' before running other code.
 *
 * @doc {heading: 'Backends'}
 */ parcelHelpers.export(exports, "backend", ()=>backend);
/**
 * Sets the global platform.
 *
 * @param platformName The name of this platform.
 * @param platform A platform implementation.
 */ parcelHelpers.export(exports, "setPlatform", ()=>setPlatform);
var _engine = require("./engine");
var _environment = require("./environment");
var _tensor = require("./tensor");
var _tensorUtil = require("./tensor_util");
function enableProdMode() {
    (0, _environment.env)().set("PROD", true);
}
function enableDebugMode() {
    (0, _environment.env)().set("DEBUG", true);
}
function disableDeprecationWarnings() {
    (0, _environment.env)().set("DEPRECATION_WARNINGS_ENABLED", false);
    console.warn(`TensorFlow.js deprecation warnings have been disabled.`);
}
function deprecationWarn(msg) {
    if ((0, _environment.env)().getBool("DEPRECATION_WARNINGS_ENABLED")) console.warn(msg + " You can disable deprecation warnings with " + "tf.disableDeprecationWarnings().");
}
(0, _tensor.setDeprecationWarningFn)(deprecationWarn);
function disposeVariables() {
    (0, _engine.ENGINE).disposeVariables();
}
function engine() {
    return 0, _engine.ENGINE;
}
function memory() {
    return (0, _engine.ENGINE).memory();
}
function profile(f) {
    return (0, _engine.ENGINE).profile(f);
}
function tidy(nameOrFn, fn) {
    return (0, _engine.ENGINE).tidy(nameOrFn, fn);
}
function dispose(container) {
    const tensors = (0, _tensorUtil.getTensorsInContainer)(container);
    tensors.forEach((tensor)=>tensor.dispose());
}
function keep(result) {
    return (0, _engine.ENGINE).keep(result);
}
function time(f) {
    return (0, _engine.ENGINE).time(f);
}
function setBackend(backendName) {
    return (0, _engine.ENGINE).setBackend(backendName);
}
function ready() {
    return (0, _engine.ENGINE).ready();
}
function getBackend() {
    return (0, _engine.ENGINE).backendName;
}
function removeBackend(name) {
    (0, _engine.ENGINE).removeBackend(name);
}
function findBackend(name) {
    return (0, _engine.ENGINE).findBackend(name);
}
function findBackendFactory(name) {
    return (0, _engine.ENGINE).findBackendFactory(name);
}
function registerBackend(name, factory, priority = 1) {
    return (0, _engine.ENGINE).registerBackend(name, factory, priority);
}
function backend() {
    return (0, _engine.ENGINE).backend;
}
function setPlatform(platformName, platform) {
    (0, _environment.env)().setPlatform(platformName, platform);
}

},{"./engine":"1g2qo","./environment":"dXmHm","./tensor":"cJNoh","./tensor_util":"8jzJG","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"btpsw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "IORouterRegistry", ()=>IORouterRegistry);
parcelHelpers.export(exports, "registerSaveRouter", ()=>registerSaveRouter);
parcelHelpers.export(exports, "registerLoadRouter", ()=>registerLoadRouter);
parcelHelpers.export(exports, "getSaveHandlers", ()=>getSaveHandlers);
parcelHelpers.export(exports, "getLoadHandlers", ()=>getLoadHandlers);
class IORouterRegistry {
    constructor(){
        this.saveRouters = [];
        this.loadRouters = [];
    }
    static getInstance() {
        if (IORouterRegistry.instance == null) IORouterRegistry.instance = new IORouterRegistry();
        return IORouterRegistry.instance;
    }
    /**
     * Register a save-handler router.
     *
     * @param saveRouter A function that maps a URL-like string onto an instance
     * of `IOHandler` with the `save` method defined or `null`.
     */ static registerSaveRouter(saveRouter) {
        IORouterRegistry.getInstance().saveRouters.push(saveRouter);
    }
    /**
     * Register a load-handler router.
     *
     * @param loadRouter A function that maps a URL-like string onto an instance
     * of `IOHandler` with the `load` method defined or `null`.
     */ static registerLoadRouter(loadRouter) {
        IORouterRegistry.getInstance().loadRouters.push(loadRouter);
    }
    /**
     * Look up IOHandler for saving, given a URL-like string.
     *
     * @param url
     * @returns If only one match is found, an instance of IOHandler with the
     * `save` method defined. If no match is found, `null`.
     * @throws Error, if more than one match is found.
     */ static getSaveHandlers(url) {
        return IORouterRegistry.getHandlers(url, "save");
    }
    /**
     * Look up IOHandler for loading, given a URL-like string.
     *
     * @param url
     * @param loadOptions Optional, custom load options.
     * @returns All valid handlers for `url`, given the currently registered
     *   handler routers.
     */ static getLoadHandlers(url, loadOptions) {
        return IORouterRegistry.getHandlers(url, "load", loadOptions);
    }
    static getHandlers(url, handlerType, loadOptions) {
        const validHandlers = [];
        const routers = handlerType === "load" ? IORouterRegistry.getInstance().loadRouters : IORouterRegistry.getInstance().saveRouters;
        routers.forEach((router)=>{
            const handler = router(url, loadOptions);
            if (handler !== null) validHandlers.push(handler);
        });
        return validHandlers;
    }
}
const registerSaveRouter = (loudRouter)=>IORouterRegistry.registerSaveRouter(loudRouter);
const registerLoadRouter = (loudRouter)=>IORouterRegistry.registerLoadRouter(loudRouter);
const getSaveHandlers = (url)=>IORouterRegistry.getSaveHandlers(url);
const getLoadHandlers = (url, loadOptions)=>IORouterRegistry.getLoadHandlers(url, loadOptions);

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c3TPR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Purge all tensorflow.js-saved model artifacts from local storage.
 *
 * @returns Paths of the models purged.
 */ parcelHelpers.export(exports, "purgeLocalStorageArtifacts", ()=>purgeLocalStorageArtifacts);
parcelHelpers.export(exports, "BrowserLocalStorage", ()=>BrowserLocalStorage);
parcelHelpers.export(exports, "localStorageRouter", ()=>localStorageRouter);
/**
 * Factory function for local storage IOHandler.
 *
 * This `IOHandler` supports both `save` and `load`.
 *
 * For each model's saved artifacts, four items are saved to local storage.
 *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the
 *     model, such as date saved, type of the topology, size in bytes, etc.
 *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-
 *     style models, this is a stringized JSON.
 *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the
 *     model, can be used to decode the saved binary weight values (see
 *     item below).
 *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary
 *     weight values, stored as a base64-encoded string.
 *
 * Saving may throw an `Error` if the total size of the artifacts exceed the
 * browser-specific quota.
 *
 * @param modelPath A unique identifier for the model to be saved. Must be a
 *   non-empty string.
 * @returns An instance of `IOHandler`, which can be used with, e.g.,
 *   `tf.Model.save`.
 */ parcelHelpers.export(exports, "browserLocalStorage", ()=>browserLocalStorage);
parcelHelpers.export(exports, "BrowserLocalStorageManager", ()=>BrowserLocalStorageManager);
var _flags = require("../flags");
var _environment = require("../environment");
var _util = require("../util");
var _ioUtils = require("./io_utils");
var _compositeArrayBuffer = require("./composite_array_buffer");
var _routerRegistry = require("./router_registry");
const PATH_SEPARATOR = "/";
const PATH_PREFIX = "tensorflowjs_models";
const INFO_SUFFIX = "info";
const MODEL_TOPOLOGY_SUFFIX = "model_topology";
const WEIGHT_SPECS_SUFFIX = "weight_specs";
const WEIGHT_DATA_SUFFIX = "weight_data";
const MODEL_METADATA_SUFFIX = "model_metadata";
function purgeLocalStorageArtifacts() {
    if (!(0, _environment.env)().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") throw new Error("purgeLocalStorageModels() cannot proceed because local storage is unavailable in the current environment.");
    const LS = window.localStorage;
    const purgedModelPaths = [];
    for(let i = 0; i < LS.length; ++i){
        const key = LS.key(i);
        const prefix = PATH_PREFIX + PATH_SEPARATOR;
        if (key.startsWith(prefix) && key.length > prefix.length) {
            LS.removeItem(key);
            const modelName = getModelPathFromKey(key);
            if (purgedModelPaths.indexOf(modelName) === -1) purgedModelPaths.push(modelName);
        }
    }
    return purgedModelPaths;
}
function getModelKeys(path) {
    return {
        info: [
            PATH_PREFIX,
            path,
            INFO_SUFFIX
        ].join(PATH_SEPARATOR),
        topology: [
            PATH_PREFIX,
            path,
            MODEL_TOPOLOGY_SUFFIX
        ].join(PATH_SEPARATOR),
        weightSpecs: [
            PATH_PREFIX,
            path,
            WEIGHT_SPECS_SUFFIX
        ].join(PATH_SEPARATOR),
        weightData: [
            PATH_PREFIX,
            path,
            WEIGHT_DATA_SUFFIX
        ].join(PATH_SEPARATOR),
        modelMetadata: [
            PATH_PREFIX,
            path,
            MODEL_METADATA_SUFFIX
        ].join(PATH_SEPARATOR)
    };
}
function removeItems(keys) {
    for (const key of Object.values(keys))window.localStorage.removeItem(key);
}
/**
 * Get model path from a local-storage key.
 *
 * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'
 *
 * @param key
 */ function getModelPathFromKey(key) {
    const items = key.split(PATH_SEPARATOR);
    if (items.length < 3) throw new Error(`Invalid key format: ${key}`);
    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
}
function maybeStripScheme(key) {
    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;
}
/**
 * IOHandler subclass: Browser Local Storage.
 *
 * See the doc string to `browserLocalStorage` for more details.
 */ class BrowserLocalStorage {
    constructor(modelPath){
        if (!(0, _environment.env)().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") // TODO(cais): Add more info about what IOHandler subtypes are
        // available.
        //   Maybe point to a doc page on the web and/or automatically determine
        //   the available IOHandlers and print them in the error message.
        throw new Error("The current environment does not support local storage.");
        this.LS = window.localStorage;
        if (modelPath == null || !modelPath) throw new Error("For local storage, modelPath must not be null, undefined or empty.");
        this.modelPath = modelPath;
        this.keys = getModelKeys(this.modelPath);
    }
    /**
     * Save model artifacts to browser local storage.
     *
     * See the documentation to `browserLocalStorage` for details on the saved
     * artifacts.
     *
     * @param modelArtifacts The model artifacts to be stored.
     * @returns An instance of SaveResult.
     */ async save(modelArtifacts) {
        if (modelArtifacts.modelTopology instanceof ArrayBuffer) throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
        else {
            const topology = JSON.stringify(modelArtifacts.modelTopology);
            const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
            const modelArtifactsInfo = (0, _ioUtils.getModelArtifactsInfoForJSON)(modelArtifacts);
            // TODO(mattsoulanille): Support saving models over 2GB that exceed
            // Chrome's ArrayBuffer size limit.
            const weightBuffer = (0, _compositeArrayBuffer.CompositeArrayBuffer).join(modelArtifacts.weightData);
            try {
                this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
                this.LS.setItem(this.keys.topology, topology);
                this.LS.setItem(this.keys.weightSpecs, weightSpecs);
                this.LS.setItem(this.keys.weightData, (0, _ioUtils.arrayBufferToBase64String)(weightBuffer));
                // Note that JSON.stringify doesn't write out keys that have undefined
                // values, so for some keys, we set undefined instead of a null-ish
                // value.
                const metadata = {
                    format: modelArtifacts.format,
                    generatedBy: modelArtifacts.generatedBy,
                    convertedBy: modelArtifacts.convertedBy,
                    signature: modelArtifacts.signature != null ? modelArtifacts.signature : undefined,
                    userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : undefined,
                    modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : undefined,
                    initializerSignature: modelArtifacts.initializerSignature != null ? modelArtifacts.initializerSignature : undefined,
                    trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : undefined
                };
                this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));
                return {
                    modelArtifactsInfo
                };
            } catch (err) {
                // If saving failed, clean up all items saved so far.
                removeItems(this.keys);
                throw new Error(`Failed to save model '${this.modelPath}' to local storage: ` + `size quota being exceeded is a possible cause of this failure: ` + `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` + `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` + `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);
            }
        }
    }
    /**
     * Load a model from local storage.
     *
     * See the documentation to `browserLocalStorage` for details on the saved
     * artifacts.
     *
     * @returns The loaded model (if loading succeeds).
     */ async load() {
        const info = JSON.parse(this.LS.getItem(this.keys.info));
        if (info == null) throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
        if (info.modelTopologyType !== "JSON") throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
        const out = {};
        // Load topology.
        const topology = JSON.parse(this.LS.getItem(this.keys.topology));
        if (topology == null) throw new Error(`In local storage, the topology of model '${this.modelPath}' ` + `is missing.`);
        out.modelTopology = topology;
        // Load weight specs.
        const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
        if (weightSpecs == null) throw new Error(`In local storage, the weight specs of model '${this.modelPath}' ` + `are missing.`);
        out.weightSpecs = weightSpecs;
        // Load meta-data fields.
        const metadataString = this.LS.getItem(this.keys.modelMetadata);
        if (metadataString != null) {
            const metadata = JSON.parse(metadataString);
            out.format = metadata.format;
            out.generatedBy = metadata.generatedBy;
            out.convertedBy = metadata.convertedBy;
            if (metadata.signature != null) out.signature = metadata.signature;
            if (metadata.userDefinedMetadata != null) out.userDefinedMetadata = metadata.userDefinedMetadata;
            if (metadata.modelInitializer != null) out.modelInitializer = metadata.modelInitializer;
            if (metadata.initializerSignature != null) out.initializerSignature = metadata.initializerSignature;
            if (metadata.trainingConfig != null) out.trainingConfig = metadata.trainingConfig;
        }
        // Load weight data.
        const weightDataBase64 = this.LS.getItem(this.keys.weightData);
        if (weightDataBase64 == null) throw new Error(`In local storage, the binary weight values of model ` + `'${this.modelPath}' are missing.`);
        out.weightData = (0, _ioUtils.base64StringToArrayBuffer)(weightDataBase64);
        return out;
    }
}
BrowserLocalStorage.URL_SCHEME = "localstorage://";
const localStorageRouter = (url)=>{
    if (!(0, _environment.env)().getBool("IS_BROWSER")) return null;
    else {
        if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
        else return null;
    }
};
(0, _routerRegistry.IORouterRegistry).registerSaveRouter(localStorageRouter);
(0, _routerRegistry.IORouterRegistry).registerLoadRouter(localStorageRouter);
function browserLocalStorage(modelPath) {
    return new BrowserLocalStorage(modelPath);
}
class BrowserLocalStorageManager {
    constructor(){
        (0, _util.assert)((0, _environment.env)().getBool("IS_BROWSER"), ()=>"Current environment is not a web browser");
        (0, _util.assert)(typeof window === "undefined" || typeof window.localStorage !== "undefined", ()=>"Current browser does not appear to support localStorage");
        this.LS = window.localStorage;
    }
    async listModels() {
        const out = {};
        const prefix = PATH_PREFIX + PATH_SEPARATOR;
        const suffix = PATH_SEPARATOR + INFO_SUFFIX;
        for(let i = 0; i < this.LS.length; ++i){
            const key = this.LS.key(i);
            if (key.startsWith(prefix) && key.endsWith(suffix)) {
                const modelPath = getModelPathFromKey(key);
                out[modelPath] = JSON.parse(this.LS.getItem(key));
            }
        }
        return out;
    }
    async removeModel(path) {
        path = maybeStripScheme(path);
        const keys = getModelKeys(path);
        if (this.LS.getItem(keys.info) == null) throw new Error(`Cannot find model at path '${path}'`);
        const info = JSON.parse(this.LS.getItem(keys.info));
        removeItems(keys);
        return info;
    }
}

},{"../flags":"5yJGC","../environment":"dXmHm","../util":"ctavo","./io_utils":"hW5iQ","./composite_array_buffer":"gHH7C","./router_registry":"btpsw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iB0Mw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Classes and functions for model management across multiple storage mediums.
 *
 * Supported client actions:
 * - Listing models on all registered storage mediums.
 * - Remove model by URL from any registered storage mediums, by using URL
 *   string.
 * - Moving or copying model from one path to another in the same medium or from
 *   one medium to another, by using URL strings.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ModelStoreManagerRegistry", ()=>ModelStoreManagerRegistry);
parcelHelpers.export(exports, "moveModel", ()=>moveModel);
parcelHelpers.export(exports, "copyModel", ()=>copyModel);
parcelHelpers.export(exports, "removeModel", ()=>removeModel);
parcelHelpers.export(exports, "listModels", ()=>listModels);
var _util = require("../util");
var _routerRegistry = require("./router_registry");
const URL_SCHEME_SUFFIX = "://";
class ModelStoreManagerRegistry {
    constructor(){
        this.managers = {};
    }
    static getInstance() {
        if (ModelStoreManagerRegistry.instance == null) ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
        return ModelStoreManagerRegistry.instance;
    }
    /**
     * Register a save-handler router.
     *
     * @param saveRouter A function that maps a URL-like string onto an instance
     * of `IOHandler` with the `save` method defined or `null`.
     */ static registerManager(scheme, manager) {
        (0, _util.assert)(scheme != null, ()=>"scheme must not be undefined or null.");
        if (scheme.endsWith(URL_SCHEME_SUFFIX)) scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
        (0, _util.assert)(scheme.length > 0, ()=>"scheme must not be an empty string.");
        const registry = ModelStoreManagerRegistry.getInstance();
        (0, _util.assert)(registry.managers[scheme] == null, ()=>`A model store manager is already registered for scheme '${scheme}'.`);
        registry.managers[scheme] = manager;
    }
    static getManager(scheme) {
        const manager = ModelStoreManagerRegistry.getInstance().managers[scheme];
        if (manager == null) throw new Error(`Cannot find model manager for scheme '${scheme}'`);
        return manager;
    }
    static getSchemes() {
        return Object.keys(ModelStoreManagerRegistry.getInstance().managers);
    }
}
/**
 * Helper method for parsing a URL string into a scheme and a path.
 *
 * @param url E.g., 'localstorage://my-model'
 * @returns A dictionary with two fields: scheme and path.
 *   Scheme: e.g., 'localstorage' in the example above.
 *   Path: e.g., 'my-model' in the example above.
 */ function parseURL(url) {
    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) throw new Error(`The url string provided does not contain a scheme. ` + `Supported schemes are: ` + `${ModelStoreManagerRegistry.getSchemes().join(",")}`);
    return {
        scheme: url.split(URL_SCHEME_SUFFIX)[0],
        path: url.split(URL_SCHEME_SUFFIX)[1]
    };
}
async function cloneModelInternal(sourceURL, destURL, deleteSource = false) {
    (0, _util.assert)(sourceURL !== destURL, ()=>`Old path and new path are the same: '${sourceURL}'`);
    const loadHandlers = (0, _routerRegistry.IORouterRegistry).getLoadHandlers(sourceURL);
    (0, _util.assert)(loadHandlers.length > 0, ()=>`Copying failed because no load handler is found for source URL ${sourceURL}.`);
    (0, _util.assert)(loadHandlers.length < 2, ()=>`Copying failed because more than one (${loadHandlers.length}) ` + `load handlers for source URL ${sourceURL}.`);
    const loadHandler = loadHandlers[0];
    const saveHandlers = (0, _routerRegistry.IORouterRegistry).getSaveHandlers(destURL);
    (0, _util.assert)(saveHandlers.length > 0, ()=>`Copying failed because no save handler is found for destination ` + `URL ${destURL}.`);
    (0, _util.assert)(saveHandlers.length < 2, ()=>`Copying failed because more than one (${loadHandlers.length}) ` + `save handlers for destination URL ${destURL}.`);
    const saveHandler = saveHandlers[0];
    const sourceScheme = parseURL(sourceURL).scheme;
    const sourcePath = parseURL(sourceURL).path;
    const sameMedium = sourceScheme === parseURL(sourceURL).scheme;
    const modelArtifacts = await loadHandler.load();
    // If moving within the same storage medium, remove the old model as soon as
    // the loading is done. Without doing this, it is possible that the combined
    // size of the two models will cause the cloning to fail.
    if (deleteSource && sameMedium) await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
    const saveResult = await saveHandler.save(modelArtifacts);
    // If moving between mediums, the deletion is done after the save succeeds.
    // This guards against the case in which saving to the destination medium
    // fails.
    if (deleteSource && !sameMedium) await ModelStoreManagerRegistry.getManager(sourceScheme).removeModel(sourcePath);
    return saveResult.modelArtifactsInfo;
}
/**
 * List all models stored in registered storage mediums.
 *
 * For a web browser environment, the registered mediums are Local Storage and
 * IndexedDB.
 *
 * ```js
 * // First create and save a model.
 * const model = tf.sequential();
 * model.add(tf.layers.dense(
 *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
 * await model.save('localstorage://demo/management/model1');
 *
 * // Then list existing models.
 * console.log(JSON.stringify(await tf.io.listModels()));
 *
 * // Delete the model.
 * await tf.io.removeModel('localstorage://demo/management/model1');
 *
 * // List models again.
 * console.log(JSON.stringify(await tf.io.listModels()));
 * ```
 *
 * @returns A `Promise` of a dictionary mapping URLs of existing models to
 * their model artifacts info. URLs include medium-specific schemes, e.g.,
 *   'indexeddb://my/model/1'. Model artifacts info include type of the
 * model's topology, byte sizes of the topology, weights, etc.
 *
 * @doc {
 *   heading: 'Models',
 *   subheading: 'Management',
 *   namespace: 'io',
 *   ignoreCI: true
 * }
 */ async function listModels() {
    const schemes = ModelStoreManagerRegistry.getSchemes();
    const out = {};
    for (const scheme of schemes){
        const schemeOut = await ModelStoreManagerRegistry.getManager(scheme).listModels();
        for(const path in schemeOut){
            const url = scheme + URL_SCHEME_SUFFIX + path;
            out[url] = schemeOut[path];
        }
    }
    return out;
}
/**
 * Remove a model specified by URL from a registered storage medium.
 *
 * ```js
 * // First create and save a model.
 * const model = tf.sequential();
 * model.add(tf.layers.dense(
 *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
 * await model.save('localstorage://demo/management/model1');
 *
 * // Then list existing models.
 * console.log(JSON.stringify(await tf.io.listModels()));
 *
 * // Delete the model.
 * await tf.io.removeModel('localstorage://demo/management/model1');
 *
 * // List models again.
 * console.log(JSON.stringify(await tf.io.listModels()));
 * ```
 *
 * @param url A URL to a stored model, with a scheme prefix, e.g.,
 *   'localstorage://my-model-1', 'indexeddb://my/model/2'.
 * @returns ModelArtifactsInfo of the deleted model (if and only if deletion
 *   is successful).
 * @throws Error if deletion fails, e.g., if no model exists at `path`.
 *
 * @doc {
 *   heading: 'Models',
 *   subheading: 'Management',
 *   namespace: 'io',
 *   ignoreCI: true
 * }
 */ async function removeModel(url) {
    const schemeAndPath = parseURL(url);
    const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
    return manager.removeModel(schemeAndPath.path);
}
/**
 * Copy a model from one URL to another.
 *
 * This function supports:
 *
 * 1. Copying within a storage medium, e.g.,
 *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`
 * 2. Copying between two storage mediums, e.g.,
 *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`
 *
 * ```js
 * // First create and save a model.
 * const model = tf.sequential();
 * model.add(tf.layers.dense(
 *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
 * await model.save('localstorage://demo/management/model1');
 *
 * // Then list existing models.
 * console.log(JSON.stringify(await tf.io.listModels()));
 *
 * // Copy the model, from Local Storage to IndexedDB.
 * await tf.io.copyModel(
 *     'localstorage://demo/management/model1',
 *     'indexeddb://demo/management/model1');
 *
 * // List models again.
 * console.log(JSON.stringify(await tf.io.listModels()));
 *
 * // Remove both models.
 * await tf.io.removeModel('localstorage://demo/management/model1');
 * await tf.io.removeModel('indexeddb://demo/management/model1');
 * ```
 *
 * @param sourceURL Source URL of copying.
 * @param destURL Destination URL of copying.
 * @returns ModelArtifactsInfo of the copied model (if and only if copying
 *   is successful).
 * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or
 *   if `oldPath` and `newPath` are identical.
 *
 * @doc {
 *   heading: 'Models',
 *   subheading: 'Management',
 *   namespace: 'io',
 *   ignoreCI: true
 * }
 */ async function copyModel(sourceURL, destURL) {
    const deleteSource = false;
    return cloneModelInternal(sourceURL, destURL, deleteSource);
}
/**
 * Move a model from one URL to another.
 *
 * This function supports:
 *
 * 1. Moving within a storage medium, e.g.,
 *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`
 * 2. Moving between two storage mediums, e.g.,
 *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`
 *
 * ```js
 * // First create and save a model.
 * const model = tf.sequential();
 * model.add(tf.layers.dense(
 *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
 * await model.save('localstorage://demo/management/model1');
 *
 * // Then list existing models.
 * console.log(JSON.stringify(await tf.io.listModels()));
 *
 * // Move the model, from Local Storage to IndexedDB.
 * await tf.io.moveModel(
 *     'localstorage://demo/management/model1',
 *     'indexeddb://demo/management/model1');
 *
 * // List models again.
 * console.log(JSON.stringify(await tf.io.listModels()));
 *
 * // Remove the moved model.
 * await tf.io.removeModel('indexeddb://demo/management/model1');
 * ```
 *
 * @param sourceURL Source URL of moving.
 * @param destURL Destination URL of moving.
 * @returns ModelArtifactsInfo of the copied model (if and only if copying
 *   is successful).
 * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or
 *   if `oldPath` and `newPath` are identical.
 *
 * @doc {
 *   heading: 'Models',
 *   subheading: 'Management',
 *   namespace: 'io',
 *   ignoreCI: true
 * }
 */ async function moveModel(sourceURL, destURL) {
    const deleteSource = true;
    return cloneModelInternal(sourceURL, destURL, deleteSource);
}

},{"../util":"ctavo","./router_registry":"btpsw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2Z7zs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getNodeFetch", ()=>getNodeFetch);
// These getters and setters are for testing so we don't export a mutable
// variable.
parcelHelpers.export(exports, "resetSystemFetch", ()=>resetSystemFetch);
parcelHelpers.export(exports, "setSystemFetch", ()=>setSystemFetch);
parcelHelpers.export(exports, "getSystemFetch", ()=>getSystemFetch);
parcelHelpers.export(exports, "PlatformNode", ()=>PlatformNode);
var _environment = require("../environment");
var process = require("9eae209353a3d608");
const getNodeFetch = {
    // tslint:disable-next-line:no-require-imports
    importFetch: ()=>require("dfcdb939bb56fbb8")
};
let systemFetch;
function resetSystemFetch() {
    systemFetch = null;
}
function setSystemFetch(fetchFn) {
    systemFetch = fetchFn;
}
function getSystemFetch() {
    return systemFetch;
}
class PlatformNode {
    constructor(){
        // tslint:disable-next-line:no-require-imports
        this.util = require("3818a9dc927c4605");
        // According to the spec, the built-in encoder can do only UTF-8 encoding.
        // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder
        this.textEncoder = new this.util.TextEncoder();
    }
    fetch(path, requestInits) {
        if ((0, _environment.env)().global.fetch != null) return (0, _environment.env)().global.fetch(path, requestInits);
        if (systemFetch == null) systemFetch = getNodeFetch.importFetch();
        return systemFetch(path, requestInits);
    }
    now() {
        const time = process.hrtime();
        return time[0] * 1000 + time[1] / 1000000;
    }
    encode(text, encoding) {
        if (encoding !== "utf-8" && encoding !== "utf8") throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);
        return this.textEncoder.encode(text);
    }
    decode(bytes, encoding) {
        if (bytes.length === 0) return "";
        return new this.util.TextDecoder(encoding).decode(bytes);
    }
    isTypedArray(a) {
        return this.util.types.isFloat32Array(a) || this.util.types.isInt32Array(a) || this.util.types.isUint8Array(a) || this.util.types.isUint8ClampedArray(a);
    }
}
if ((0, _environment.env)().get("IS_NODE") && !(0, _environment.env)().get("IS_BROWSER")) (0, _environment.env)().setPlatform("node", new PlatformNode());

},{"9eae209353a3d608":"h0aBX","../environment":"dXmHm","dfcdb939bb56fbb8":"1kJBy","3818a9dc927c4605":"1kJBy","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1kJBy":[function(require,module,exports) {
"use strict";

},{}],"dYyY7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.
 *
 * The values are stored in CPU as `TypedArray`. Fill the buffer using
 * `buffer.set()`, or by modifying directly `buffer.values`.
 *
 * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with
 * those values.
 *
 * ```js
 * // Create a buffer and set values at particular indices.
 * const buffer = tf.buffer([2, 2]);
 * buffer.set(3, 0, 0);
 * buffer.set(5, 1, 0);
 *
 * // Convert the buffer back to a tensor.
 * buffer.toTensor().print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param dtype The dtype of the buffer. Defaults to 'float32'.
 * @param values The values of the buffer as `TypedArray`. Defaults to
 * zeros.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "buffer", ()=>buffer);
var _tensor = require("../tensor");
var _util = require("../util");
function buffer(shape, dtype = "float32", values) {
    dtype = dtype || "float32";
    _util.assertNonNegativeIntegerDimensions(shape);
    return new (0, _tensor.TensorBuffer)(shape, dtype, values);
}

},{"../tensor":"cJNoh","../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b4VDA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cast", ()=>cast);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Casts a `tf.Tensor` to a new dtype.
 *
 * ```js
 * const x = tf.tensor1d([1.5, 2.5, 3]);
 * tf.cast(x, 'int32').print();
 * ```
 * @param x The input tensor to be casted.
 * @param dtype The dtype to cast the input tensor to.
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function cast_(x, dtype) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "cast");
    // Sanity checks.
    if (!_util.isValidDtype(dtype)) throw new Error(`Failed to cast to unknown dtype ${dtype}`);
    if (dtype === "string" && $x.dtype !== "string" || dtype !== "string" && $x.dtype === "string") throw new Error("Only strings can be casted to strings");
    const inputs = {
        x: $x
    };
    const attrs = {
        dtype
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Cast), inputs, attrs);
}
const cast = /* @__PURE__ */ (0, _operation.op)({
    cast_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jmQ7S":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "clone", ()=>clone);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Creates a new tensor with the same values and shape as the specified
 * tensor.
 *
 * ```js
 * const x = tf.tensor([1, 2]);
 *
 * x.clone().print();
 * ```
 *
 * @param x The tensor to clone.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function clone_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "clone", "string_or_numeric");
    const inputs = {
        x: $x
    };
    // Note this op is called tf.identity in python. Hence the kernel name used
    // here.
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Identity), inputs);
}
const clone = /* @__PURE__ */ (0, _operation.op)({
    clone_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5RyfW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Prints information about the `tf.Tensor` including its data.
 *
 * ```js
 * const verbose = true;
 * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);
 * ```
 * @param x The tensor to be printed.
 * @param verbose Whether to print verbose information about the ` Tensor`,
 * including dtype and size.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "print", ()=>print);
function print(x, verbose = false) {
    console.log(x.toString(verbose));
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"k7xLP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "registerOptimizers", ()=>registerOptimizers);
var _adadeltaOptimizer = require("./adadelta_optimizer");
var _adagradOptimizer = require("./adagrad_optimizer");
var _adamOptimizer = require("./adam_optimizer");
var _adamaxOptimizer = require("./adamax_optimizer");
var _momentumOptimizer = require("./momentum_optimizer");
var _rmspropOptimizer = require("./rmsprop_optimizer");
var _sgdOptimizer = require("./sgd_optimizer");
var _serialization = require("../serialization");
const OPTIMIZERS = [
    (0, _adadeltaOptimizer.AdadeltaOptimizer),
    (0, _adagradOptimizer.AdagradOptimizer),
    (0, _adamOptimizer.AdamOptimizer),
    (0, _adamaxOptimizer.AdamaxOptimizer),
    (0, _momentumOptimizer.MomentumOptimizer),
    (0, _rmspropOptimizer.RMSPropOptimizer),
    (0, _sgdOptimizer.SGDOptimizer)
];
function registerOptimizers() {
    for (const optimizer of OPTIMIZERS)(0, _serialization.registerClass)(optimizer);
}

},{"./adadelta_optimizer":"a2jsK","./adagrad_optimizer":"ddRDd","./adam_optimizer":"5uefH","./adamax_optimizer":"72DSu","./momentum_optimizer":"bKhX1","./rmsprop_optimizer":"66Wvy","./sgd_optimizer":"4dxnr","../serialization":"dv6JX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a2jsK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/** @doclink Optimizer */ parcelHelpers.export(exports, "AdadeltaOptimizer", ()=>AdadeltaOptimizer);
var _engine = require("../engine");
var _globals = require("../globals");
var _add = require("../ops/add");
var _div = require("../ops/div");
var _mul = require("../ops/mul");
var _ops = require("../ops/ops");
var _square = require("../ops/square");
var _zerosLike = require("../ops/zeros_like");
var _optimizer = require("./optimizer");
class AdadeltaOptimizer extends (0, _optimizer.Optimizer) {
    /** @nocollapse */ static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return "Adadelta";
    }
    constructor(learningRate, rho, epsilon = null){
        super();
        this.learningRate = learningRate;
        this.rho = rho;
        this.epsilon = epsilon;
        this.accumulatedGrads = [];
        this.accumulatedUpdates = [];
        if (epsilon == null) this.epsilon = (0, _engine.ENGINE).backend.epsilon();
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item)=>item.name) : Object.keys(variableGradients);
        variableNames.forEach((name, i)=>{
            const value = (0, _engine.ENGINE).registeredVariables[name];
            const trainable = false;
            if (this.accumulatedGrads[i] == null) this.accumulatedGrads[i] = {
                originalName: `${name}/accum_grad`,
                variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
            };
            if (this.accumulatedUpdates[i] == null) this.accumulatedUpdates[i] = {
                originalName: `${name}/accum_var`,
                variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
            };
            const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
            if (gradient == null) return;
            const accumulatedGrad = this.accumulatedGrads[i].variable;
            const accumulatedUpdate = this.accumulatedUpdates[i].variable;
            (0, _globals.tidy)(()=>{
                const newAccumulatedGrad = (0, _add.add)((0, _mul.mul)(accumulatedGrad, this.rho), (0, _mul.mul)((0, _square.square)(gradient), 1 - this.rho));
                const updates = (0, _mul.mul)((0, _div.div)((0, _ops.sqrt)((0, _add.add)(accumulatedUpdate, this.epsilon)), (0, _ops.sqrt)((0, _add.add)(accumulatedGrad, this.epsilon))), gradient);
                const newAccumulatedUpdate = (0, _add.add)((0, _mul.mul)(accumulatedUpdate, this.rho), (0, _mul.mul)((0, _square.square)(updates), 1 - this.rho));
                accumulatedGrad.assign(newAccumulatedGrad);
                accumulatedUpdate.assign(newAccumulatedUpdate);
                const newValue = (0, _add.add)((0, _mul.mul)(updates, -this.learningRate), value);
                value.assign(newValue);
            });
        });
        this.incrementIterations();
    }
    dispose() {
        if (this.accumulatedUpdates != null) {
            (0, _globals.dispose)(this.accumulatedGrads.map((v)=>v.variable));
            (0, _globals.dispose)(this.accumulatedUpdates.map((v)=>v.variable));
        }
    }
    async getWeights() {
        // Order matters for Python compatibility.
        const variables = [
            ...this.accumulatedGrads,
            ...this.accumulatedUpdates
        ];
        return [
            await this.saveIterations()
        ].concat(variables.map((v)=>({
                name: v.originalName,
                tensor: v.variable
            })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        const variableCount = weightValues.length / 2;
        const trainable = false;
        this.accumulatedGrads = weightValues.slice(0, variableCount).map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        this.accumulatedUpdates = weightValues.slice(variableCount, variableCount * 2).map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
    }
    getConfig() {
        return {
            "learningRate": this.learningRate,
            "rho": this.rho,
            "epsilon": this.epsilon
        };
    }
    /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config["learningRate"], config["rho"], config["epsilon"]);
    }
}

},{"../engine":"1g2qo","../globals":"eO1Ja","../ops/add":"aynh7","../ops/div":"5ZT1h","../ops/mul":"fOWrC","../ops/ops":"13k1e","../ops/square":"cOosW","../ops/zeros_like":"1Xjct","./optimizer":"1b69z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aynh7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "add", ()=>add);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
 *
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3, 4]);
 * const b = tf.tensor1d([10, 20, 30, 40]);
 *
 * a.add(b).print();  // or tf.add(a, b)
 * ```
 *
 * ```js
 * // Broadcast add a with b.
 * const a = tf.scalar(5);
 * const b = tf.tensor1d([10, 20, 30, 40]);
 *
 * a.add(b).print();  // or tf.add(a, b)
 * ```
 * @param a The first `tf.Tensor` to add.
 * @param b The second `tf.Tensor` to add. Must have the same type as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function add_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "add");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "add");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Add), inputs);
}
const add = /* @__PURE__ */ (0, _operation.op)({
    add_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5ZT1h":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "div", ()=>div);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _floorDiv = require("./floorDiv");
var _operation = require("./operation");
/**
 * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 9, 16]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 *
 * a.div(b).print();  // or tf.div(a, b)
 * ```
 *
 * ```js
 * // Broadcast div a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(2);
 *
 * a.div(b).print();  // or tf.div(a, b)
 * ```
 *
 * @param a The first tensor as the numerator.
 * @param b The second tensor as the denominator. Must have the same dtype as
 * `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function div_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "div");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "div");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    if ($a.dtype === "int32" && $b.dtype === "int32") return (0, _floorDiv.floorDiv)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    const attrs = {};
    // tslint:disable-next-line: no-unnecessary-type-assertion
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.RealDiv), inputs, attrs);
}
const div = /* @__PURE__ */ (0, _operation.op)({
    div_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./floorDiv":"jboDF","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jboDF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "floorDiv", ()=>floorDiv);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
 * The result is rounded with floor function.
 *
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 9, 16]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 *
 * a.floorDiv(b).print();  // or tf.div(a, b)
 * ```
 *
 * ```js
 * // Broadcast div a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(2);
 *
 * a.floorDiv(b).print();  // or tf.floorDiv(a, b)
 * ```
 *
 * @param a The first tensor as the numerator.
 * @param b The second tensor as the denominator. Must have the same dtype as
 * `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function floorDiv_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "floorDiv");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "floorDiv");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.FloorDiv), inputs);
}
const floorDiv = /* @__PURE__ */ (0, _operation.op)({
    floorDiv_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fOWrC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mul", ()=>mul);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
 *
 * We also expose `tf.mulStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3, 4]);
 * const b = tf.tensor1d([2, 3, 4, 5]);
 *
 * a.mul(b).print();  // or tf.mul(a, b)
 * ```
 *
 * ```js
 * // Broadcast mul a with b.
 * const a = tf.tensor1d([1, 2, 3, 4]);
 * const b = tf.scalar(5);
 *
 * a.mul(b).print();  // or tf.mul(a, b)
 * ```
 * @param a The first tensor to multiply.
 * @param b The second tensor to multiply. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function mul_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "mul");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "mul");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Multiply), inputs);
}
const mul = /* @__PURE__ */ (0, _operation.op)({
    mul_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"13k1e":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Modularized ops.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "abs", ()=>(0, _abs.abs));
parcelHelpers.export(exports, "acos", ()=>(0, _acos.acos));
parcelHelpers.export(exports, "acosh", ()=>(0, _acosh.acosh));
parcelHelpers.export(exports, "add", ()=>(0, _add.add));
parcelHelpers.export(exports, "addN", ()=>(0, _addN.addN));
parcelHelpers.export(exports, "all", ()=>(0, _all.all));
parcelHelpers.export(exports, "any", ()=>(0, _any.any));
parcelHelpers.export(exports, "argMax", ()=>(0, _argMax.argMax));
parcelHelpers.export(exports, "argMin", ()=>(0, _argMin.argMin));
parcelHelpers.export(exports, "asin", ()=>(0, _asin.asin));
parcelHelpers.export(exports, "asinh", ()=>(0, _asinh.asinh));
parcelHelpers.export(exports, "atan", ()=>(0, _atan.atan));
parcelHelpers.export(exports, "atan2", ()=>(0, _atan2.atan2));
parcelHelpers.export(exports, "atanh", ()=>(0, _atanh.atanh));
parcelHelpers.export(exports, "avgPool", ()=>(0, _avgPool.avgPool));
parcelHelpers.export(exports, "avgPool3d", ()=>(0, _avgPool3D.avgPool3d));
parcelHelpers.export(exports, "basicLSTMCell", ()=>(0, _basicLstmCell.basicLSTMCell));
parcelHelpers.export(exports, "batchToSpaceND", ()=>(0, _batchToSpaceNd.batchToSpaceND));
parcelHelpers.export(exports, "batchNorm", ()=>(0, _batchnorm.batchNorm));
parcelHelpers.export(exports, "batchNorm2d", ()=>(0, _batchnorm2D.batchNorm2d));
parcelHelpers.export(exports, "batchNorm3d", ()=>(0, _batchnorm3D.batchNorm3d));
parcelHelpers.export(exports, "batchNorm4d", ()=>(0, _batchnorm4D.batchNorm4d));
parcelHelpers.export(exports, "bincount", ()=>(0, _bincount.bincount));
parcelHelpers.export(exports, "bitwiseAnd", ()=>(0, _bitwiseAnd.bitwiseAnd));
parcelHelpers.export(exports, "broadcastArgs", ()=>(0, _broadcastArgs.broadcastArgs));
parcelHelpers.export(exports, "broadcastTo", ()=>(0, _broadcastTo.broadcastTo));
parcelHelpers.export(exports, "buffer", ()=>(0, _buffer.buffer));
parcelHelpers.export(exports, "cast", ()=>(0, _cast.cast));
parcelHelpers.export(exports, "ceil", ()=>(0, _ceil.ceil));
parcelHelpers.export(exports, "clipByValue", ()=>(0, _clipByValue.clipByValue));
parcelHelpers.export(exports, "clone", ()=>(0, _clone.clone));
parcelHelpers.export(exports, "complex", ()=>(0, _complex.complex));
parcelHelpers.export(exports, "concat", ()=>(0, _concat.concat));
parcelHelpers.export(exports, "concat1d", ()=>(0, _concat1D.concat1d));
parcelHelpers.export(exports, "concat2d", ()=>(0, _concat2D.concat2d));
parcelHelpers.export(exports, "concat3d", ()=>(0, _concat3D.concat3d));
parcelHelpers.export(exports, "concat4d", ()=>(0, _concat4D.concat4d));
parcelHelpers.export(exports, "conv1d", ()=>(0, _conv1D.conv1d));
parcelHelpers.export(exports, "conv2d", ()=>(0, _conv2D.conv2d));
parcelHelpers.export(exports, "conv2dTranspose", ()=>(0, _conv2DTranspose.conv2dTranspose));
parcelHelpers.export(exports, "conv3d", ()=>(0, _conv3D.conv3d));
parcelHelpers.export(exports, "conv3dTranspose", ()=>(0, _conv3DTranspose.conv3dTranspose));
parcelHelpers.export(exports, "cos", ()=>(0, _cos.cos));
parcelHelpers.export(exports, "cosh", ()=>(0, _cosh.cosh));
parcelHelpers.export(exports, "cumprod", ()=>(0, _cumprod.cumprod));
parcelHelpers.export(exports, "cumsum", ()=>(0, _cumsum.cumsum));
parcelHelpers.export(exports, "denseBincount", ()=>(0, _denseBincount.denseBincount));
parcelHelpers.export(exports, "depthToSpace", ()=>(0, _depthToSpace.depthToSpace));
parcelHelpers.export(exports, "depthwiseConv2d", ()=>(0, _depthwiseConv2D.depthwiseConv2d));
parcelHelpers.export(exports, "diag", ()=>(0, _diag.diag));
parcelHelpers.export(exports, "dilation2d", ()=>(0, _dilation2D.dilation2d));
parcelHelpers.export(exports, "div", ()=>(0, _div.div));
parcelHelpers.export(exports, "divNoNan", ()=>(0, _divNoNan.divNoNan));
parcelHelpers.export(exports, "dot", ()=>(0, _dot.dot));
parcelHelpers.export(exports, "einsum", ()=>(0, _einsum.einsum));
parcelHelpers.export(exports, "elu", ()=>(0, _elu.elu));
parcelHelpers.export(exports, "ensureShape", ()=>(0, _ensureShape.ensureShape));
parcelHelpers.export(exports, "equal", ()=>(0, _equal.equal));
parcelHelpers.export(exports, "erf", ()=>(0, _erf.erf));
parcelHelpers.export(exports, "euclideanNorm", ()=>(0, _euclideanNorm.euclideanNorm));
parcelHelpers.export(exports, "exp", ()=>(0, _exp.exp));
parcelHelpers.export(exports, "expandDims", ()=>(0, _expandDims.expandDims));
parcelHelpers.export(exports, "expm1", ()=>(0, _expm1.expm1));
parcelHelpers.export(exports, "eye", ()=>(0, _eye.eye));
parcelHelpers.export(exports, "fill", ()=>(0, _fill.fill));
parcelHelpers.export(exports, "floor", ()=>(0, _floor.floor));
parcelHelpers.export(exports, "floorDiv", ()=>(0, _floorDiv.floorDiv));
parcelHelpers.export(exports, "gather", ()=>(0, _gather.gather));
parcelHelpers.export(exports, "greater", ()=>(0, _greater.greater));
parcelHelpers.export(exports, "greaterEqual", ()=>(0, _greaterEqual.greaterEqual));
parcelHelpers.export(exports, "imag", ()=>(0, _imag.imag));
parcelHelpers.export(exports, "isFinite", ()=>(0, _isFinite.isFinite));
parcelHelpers.export(exports, "isInf", ()=>(0, _isInf.isInf));
parcelHelpers.export(exports, "isNaN", ()=>(0, _isNan.isNaN));
parcelHelpers.export(exports, "leakyRelu", ()=>(0, _leakyRelu.leakyRelu));
parcelHelpers.export(exports, "less", ()=>(0, _less.less));
parcelHelpers.export(exports, "lessEqual", ()=>(0, _lessEqual.lessEqual));
parcelHelpers.export(exports, "linspace", ()=>(0, _linspace.linspace));
parcelHelpers.export(exports, "localResponseNormalization", ()=>(0, _localResponseNormalization.localResponseNormalization));
parcelHelpers.export(exports, "log", ()=>(0, _log.log));
parcelHelpers.export(exports, "log1p", ()=>(0, _log1P.log1p));
parcelHelpers.export(exports, "logSigmoid", ()=>(0, _logSigmoid.logSigmoid));
parcelHelpers.export(exports, "logSoftmax", ()=>(0, _logSoftmax.logSoftmax));
parcelHelpers.export(exports, "logSumExp", ()=>(0, _logSumExp.logSumExp));
parcelHelpers.export(exports, "logicalAnd", ()=>(0, _logicalAnd.logicalAnd));
parcelHelpers.export(exports, "logicalNot", ()=>(0, _logicalNot.logicalNot));
parcelHelpers.export(exports, "logicalOr", ()=>(0, _logicalOr.logicalOr));
parcelHelpers.export(exports, "logicalXor", ()=>(0, _logicalXor.logicalXor));
parcelHelpers.export(exports, "lowerBound", ()=>(0, _lowerBound.lowerBound));
parcelHelpers.export(exports, "matMul", ()=>(0, _matMul.matMul));
parcelHelpers.export(exports, "max", ()=>(0, _max.max));
parcelHelpers.export(exports, "maxPool", ()=>(0, _maxPool.maxPool));
parcelHelpers.export(exports, "maxPool3d", ()=>(0, _maxPool3D.maxPool3d));
parcelHelpers.export(exports, "maxPoolWithArgmax", ()=>(0, _maxPoolWithArgmax.maxPoolWithArgmax));
parcelHelpers.export(exports, "maximum", ()=>(0, _maximum.maximum));
parcelHelpers.export(exports, "mean", ()=>(0, _mean.mean));
parcelHelpers.export(exports, "meshgrid", ()=>(0, _meshgrid.meshgrid));
parcelHelpers.export(exports, "min", ()=>(0, _min.min));
parcelHelpers.export(exports, "minimum", ()=>(0, _minimum.minimum));
parcelHelpers.export(exports, "mirrorPad", ()=>(0, _mirrorPad.mirrorPad));
parcelHelpers.export(exports, "mod", ()=>(0, _mod.mod));
parcelHelpers.export(exports, "moments", ()=>(0, _moments.moments));
parcelHelpers.export(exports, "mul", ()=>(0, _mul.mul));
parcelHelpers.export(exports, "multiRNNCell", ()=>(0, _multiRnnCell.multiRNNCell));
parcelHelpers.export(exports, "multinomial", ()=>(0, _multinomial.multinomial));
parcelHelpers.export(exports, "neg", ()=>(0, _neg.neg));
parcelHelpers.export(exports, "notEqual", ()=>(0, _notEqual.notEqual));
parcelHelpers.export(exports, "oneHot", ()=>(0, _oneHot.oneHot));
parcelHelpers.export(exports, "ones", ()=>(0, _ones.ones));
parcelHelpers.export(exports, "onesLike", ()=>(0, _onesLike.onesLike));
parcelHelpers.export(exports, "outerProduct", ()=>(0, _outerProduct.outerProduct));
parcelHelpers.export(exports, "pad", ()=>(0, _pad.pad));
parcelHelpers.export(exports, "pad1d", ()=>(0, _pad1D.pad1d));
parcelHelpers.export(exports, "pad2d", ()=>(0, _pad2D.pad2d));
parcelHelpers.export(exports, "pad3d", ()=>(0, _pad3D.pad3d));
parcelHelpers.export(exports, "pad4d", ()=>(0, _pad4D.pad4d));
parcelHelpers.export(exports, "pool", ()=>(0, _pool.pool));
parcelHelpers.export(exports, "pow", ()=>(0, _pow.pow));
parcelHelpers.export(exports, "prelu", ()=>(0, _prelu.prelu));
parcelHelpers.export(exports, "print", ()=>(0, _print.print));
parcelHelpers.export(exports, "prod", ()=>(0, _prod.prod));
parcelHelpers.export(exports, "raggedGather", ()=>(0, _raggedGather.raggedGather));
parcelHelpers.export(exports, "raggedRange", ()=>(0, _raggedRange.raggedRange));
parcelHelpers.export(exports, "raggedTensorToTensor", ()=>(0, _raggedTensorToTensor.raggedTensorToTensor));
parcelHelpers.export(exports, "rand", ()=>(0, _rand.rand));
parcelHelpers.export(exports, "randomGamma", ()=>(0, _randomGamma.randomGamma));
parcelHelpers.export(exports, "randomNormal", ()=>(0, _randomNormal.randomNormal));
parcelHelpers.export(exports, "randomStandardNormal", ()=>(0, _randomStandardNormal.randomStandardNormal));
parcelHelpers.export(exports, "randomUniform", ()=>(0, _randomUniform.randomUniform));
parcelHelpers.export(exports, "randomUniformInt", ()=>(0, _randomUniformInt.randomUniformInt));
parcelHelpers.export(exports, "range", ()=>(0, _range.range));
parcelHelpers.export(exports, "real", ()=>(0, _real.real));
parcelHelpers.export(exports, "reciprocal", ()=>(0, _reciprocal.reciprocal));
parcelHelpers.export(exports, "relu", ()=>(0, _relu.relu));
parcelHelpers.export(exports, "relu6", ()=>(0, _relu6.relu6));
parcelHelpers.export(exports, "reshape", ()=>(0, _reshape.reshape));
parcelHelpers.export(exports, "reverse", ()=>(0, _reverse.reverse));
parcelHelpers.export(exports, "reverse1d", ()=>(0, _reverse1D.reverse1d));
parcelHelpers.export(exports, "reverse2d", ()=>(0, _reverse2D.reverse2d));
parcelHelpers.export(exports, "reverse3d", ()=>(0, _reverse3D.reverse3d));
parcelHelpers.export(exports, "reverse4d", ()=>(0, _reverse4D.reverse4d));
parcelHelpers.export(exports, "round", ()=>(0, _round.round));
parcelHelpers.export(exports, "rsqrt", ()=>(0, _rsqrt.rsqrt));
parcelHelpers.export(exports, "scalar", ()=>(0, _scalar.scalar));
parcelHelpers.export(exports, "selu", ()=>(0, _selu.selu));
parcelHelpers.export(exports, "separableConv2d", ()=>(0, _separableConv2D.separableConv2d));
parcelHelpers.export(exports, "setdiff1dAsync", ()=>(0, _setdiff1DAsync.setdiff1dAsync));
parcelHelpers.export(exports, "sigmoid", ()=>(0, _sigmoid.sigmoid));
parcelHelpers.export(exports, "sign", ()=>(0, _sign.sign));
parcelHelpers.export(exports, "sin", ()=>(0, _sin.sin));
parcelHelpers.export(exports, "sinh", ()=>(0, _sinh.sinh));
parcelHelpers.export(exports, "slice", ()=>(0, _slice.slice));
parcelHelpers.export(exports, "slice1d", ()=>(0, _slice1D.slice1d));
parcelHelpers.export(exports, "slice2d", ()=>(0, _slice2D.slice2d));
parcelHelpers.export(exports, "slice3d", ()=>(0, _slice3D.slice3d));
parcelHelpers.export(exports, "slice4d", ()=>(0, _slice4D.slice4d));
parcelHelpers.export(exports, "softmax", ()=>(0, _softmax.softmax));
parcelHelpers.export(exports, "softplus", ()=>(0, _softplus.softplus));
parcelHelpers.export(exports, "spaceToBatchND", ()=>(0, _spaceToBatchNd.spaceToBatchND));
parcelHelpers.export(exports, "fft", ()=>(0, _fft.fft));
parcelHelpers.export(exports, "ifft", ()=>(0, _ifft.ifft));
parcelHelpers.export(exports, "irfft", ()=>(0, _irfft.irfft));
parcelHelpers.export(exports, "rfft", ()=>(0, _rfft.rfft));
parcelHelpers.export(exports, "split", ()=>(0, _split.split));
parcelHelpers.export(exports, "sqrt", ()=>(0, _sqrt.sqrt));
parcelHelpers.export(exports, "square", ()=>(0, _square.square));
parcelHelpers.export(exports, "squaredDifference", ()=>(0, _squaredDifference.squaredDifference));
parcelHelpers.export(exports, "squeeze", ()=>(0, _squeeze.squeeze));
parcelHelpers.export(exports, "stack", ()=>(0, _stack.stack));
parcelHelpers.export(exports, "step", ()=>(0, _step.step));
parcelHelpers.export(exports, "stridedSlice", ()=>(0, _stridedSlice.stridedSlice));
parcelHelpers.export(exports, "sub", ()=>(0, _sub.sub));
parcelHelpers.export(exports, "sum", ()=>(0, _sum.sum));
parcelHelpers.export(exports, "tan", ()=>(0, _tan.tan));
parcelHelpers.export(exports, "tanh", ()=>(0, _tanh.tanh));
parcelHelpers.export(exports, "tensor", ()=>(0, _tensor.tensor));
parcelHelpers.export(exports, "tensor1d", ()=>(0, _tensor1D.tensor1d));
parcelHelpers.export(exports, "tensor2d", ()=>(0, _tensor2D.tensor2d));
parcelHelpers.export(exports, "tensor3d", ()=>(0, _tensor3D.tensor3d));
parcelHelpers.export(exports, "tensor4d", ()=>(0, _tensor4D.tensor4d));
parcelHelpers.export(exports, "tensor5d", ()=>(0, _tensor5D.tensor5d));
parcelHelpers.export(exports, "tensor6d", ()=>(0, _tensor6D.tensor6d));
parcelHelpers.export(exports, "tensorScatterUpdate", ()=>(0, _tensorScatterUpdate.tensorScatterUpdate));
parcelHelpers.export(exports, "tile", ()=>(0, _tile.tile));
parcelHelpers.export(exports, "topk", ()=>(0, _topk.topk));
parcelHelpers.export(exports, "truncatedNormal", ()=>(0, _truncatedNormal.truncatedNormal));
parcelHelpers.export(exports, "unique", ()=>(0, _unique.unique));
parcelHelpers.export(exports, "unsortedSegmentSum", ()=>(0, _unsortedSegmentSum.unsortedSegmentSum));
parcelHelpers.export(exports, "unstack", ()=>(0, _unstack.unstack));
parcelHelpers.export(exports, "upperBound", ()=>(0, _upperBound.upperBound));
parcelHelpers.export(exports, "variable", ()=>(0, _variable.variable));
parcelHelpers.export(exports, "where", ()=>(0, _where.where));
parcelHelpers.export(exports, "whereAsync", ()=>(0, _whereAsync.whereAsync));
parcelHelpers.export(exports, "zeros", ()=>(0, _zeros.zeros));
parcelHelpers.export(exports, "zerosLike", ()=>(0, _zerosLike.zerosLike));
parcelHelpers.export(exports, "op", ()=>(0, _operation.op));
parcelHelpers.export(exports, "OP_SCOPE_SUFFIX", ()=>(0, _operation.OP_SCOPE_SUFFIX));
// Second level exports.
parcelHelpers.export(exports, "image", ()=>image);
parcelHelpers.export(exports, "linalg", ()=>linalg);
parcelHelpers.export(exports, "losses", ()=>losses);
parcelHelpers.export(exports, "spectral", ()=>spectral);
parcelHelpers.export(exports, "fused", ()=>_fusedOps);
parcelHelpers.export(exports, "signal", ()=>signal);
parcelHelpers.export(exports, "sparse", ()=>sparse);
parcelHelpers.export(exports, "string", ()=>string);
var _abs = require("./abs");
var _acos = require("./acos");
var _acosh = require("./acosh");
var _add = require("./add");
var _addN = require("./add_n");
var _all = require("./all");
var _any = require("./any");
var _argMax = require("./arg_max");
var _argMin = require("./arg_min");
var _asin = require("./asin");
var _asinh = require("./asinh");
var _atan = require("./atan");
var _atan2 = require("./atan2");
var _atanh = require("./atanh");
var _avgPool = require("./avg_pool");
var _avgPool3D = require("./avg_pool_3d");
var _basicLstmCell = require("./basic_lstm_cell");
var _batchToSpaceNd = require("./batch_to_space_nd");
var _batchnorm = require("./batchnorm");
var _batchnorm2D = require("./batchnorm2d");
var _batchnorm3D = require("./batchnorm3d");
var _batchnorm4D = require("./batchnorm4d");
var _bincount = require("./bincount");
var _bitwiseAnd = require("./bitwise_and");
var _broadcastArgs = require("./broadcast_args");
var _broadcastTo = require("./broadcast_to");
var _buffer = require("./buffer");
var _cast = require("./cast");
var _ceil = require("./ceil");
var _clipByValue = require("./clip_by_value");
var _clone = require("./clone");
var _complex = require("./complex");
var _concat = require("./concat");
var _concat1D = require("./concat_1d");
var _concat2D = require("./concat_2d");
var _concat3D = require("./concat_3d");
var _concat4D = require("./concat_4d");
var _conv1D = require("./conv1d");
var _conv2D = require("./conv2d");
var _conv2DTranspose = require("./conv2d_transpose");
var _conv3D = require("./conv3d");
var _conv3DTranspose = require("./conv3d_transpose");
var _cos = require("./cos");
var _cosh = require("./cosh");
var _cumprod = require("./cumprod");
var _cumsum = require("./cumsum");
var _denseBincount = require("./dense_bincount");
var _depthToSpace = require("./depth_to_space");
var _depthwiseConv2D = require("./depthwise_conv2d");
var _diag = require("./diag");
var _dilation2D = require("./dilation2d");
var _div = require("./div");
var _divNoNan = require("./div_no_nan");
var _dot = require("./dot");
var _einsum = require("./einsum");
var _elu = require("./elu");
var _ensureShape = require("./ensure_shape");
var _equal = require("./equal");
var _erf = require("./erf");
var _euclideanNorm = require("./euclidean_norm");
var _exp = require("./exp");
var _expandDims = require("./expand_dims");
var _expm1 = require("./expm1");
var _eye = require("./eye");
var _fill = require("./fill");
var _floor = require("./floor");
var _floorDiv = require("./floorDiv");
var _gather = require("./gather");
var _greater = require("./greater");
var _greaterEqual = require("./greater_equal");
var _imag = require("./imag");
var _isFinite = require("./is_finite");
var _isInf = require("./is_inf");
var _isNan = require("./is_nan");
var _leakyRelu = require("./leaky_relu");
var _less = require("./less");
var _lessEqual = require("./less_equal");
var _linspace = require("./linspace");
var _localResponseNormalization = require("./local_response_normalization");
var _log = require("./log");
var _log1P = require("./log1p");
var _logSigmoid = require("./log_sigmoid");
var _logSoftmax = require("./log_softmax");
var _logSumExp = require("./log_sum_exp");
var _logicalAnd = require("./logical_and");
var _logicalNot = require("./logical_not");
var _logicalOr = require("./logical_or");
var _logicalXor = require("./logical_xor");
var _lowerBound = require("./lower_bound");
var _matMul = require("./mat_mul");
var _max = require("./max");
var _maxPool = require("./max_pool");
var _maxPool3D = require("./max_pool_3d");
var _maxPoolWithArgmax = require("./max_pool_with_argmax");
var _maximum = require("./maximum");
var _mean = require("./mean");
var _meshgrid = require("./meshgrid");
var _min = require("./min");
var _minimum = require("./minimum");
var _mirrorPad = require("./mirror_pad");
var _mod = require("./mod");
var _moments = require("./moments");
var _mul = require("./mul");
var _multiRnnCell = require("./multi_rnn_cell");
var _multinomial = require("./multinomial");
var _neg = require("./neg");
var _notEqual = require("./not_equal");
var _oneHot = require("./one_hot");
var _ones = require("./ones");
var _onesLike = require("./ones_like");
var _outerProduct = require("./outer_product");
var _pad = require("./pad");
var _pad1D = require("./pad1d");
var _pad2D = require("./pad2d");
var _pad3D = require("./pad3d");
var _pad4D = require("./pad4d");
var _pool = require("./pool");
var _pow = require("./pow");
var _prelu = require("./prelu");
var _print = require("./print");
var _prod = require("./prod");
var _raggedGather = require("./ragged_gather");
var _raggedRange = require("./ragged_range");
var _raggedTensorToTensor = require("./ragged_tensor_to_tensor");
var _rand = require("./rand");
var _randomGamma = require("./random_gamma");
var _randomNormal = require("./random_normal");
var _randomStandardNormal = require("./random_standard_normal");
var _randomUniform = require("./random_uniform");
var _randomUniformInt = require("./random_uniform_int");
var _range = require("./range");
var _real = require("./real");
var _reciprocal = require("./reciprocal");
var _relu = require("./relu");
var _relu6 = require("./relu6");
var _reshape = require("./reshape");
var _reverse = require("./reverse");
var _reverse1D = require("./reverse_1d");
var _reverse2D = require("./reverse_2d");
var _reverse3D = require("./reverse_3d");
var _reverse4D = require("./reverse_4d");
var _round = require("./round");
var _rsqrt = require("./rsqrt");
var _scalar = require("./scalar");
var _selu = require("./selu");
var _separableConv2D = require("./separable_conv2d");
var _setdiff1DAsync = require("./setdiff1d_async");
var _sigmoid = require("./sigmoid");
var _sign = require("./sign");
var _sin = require("./sin");
var _sinh = require("./sinh");
var _slice = require("./slice");
var _slice1D = require("./slice1d");
var _slice2D = require("./slice2d");
var _slice3D = require("./slice3d");
var _slice4D = require("./slice4d");
var _softmax = require("./softmax");
var _softplus = require("./softplus");
var _spaceToBatchNd = require("./space_to_batch_nd");
var _fft = require("./spectral/fft");
var _ifft = require("./spectral/ifft");
var _irfft = require("./spectral/irfft");
var _rfft = require("./spectral/rfft");
var _split = require("./split");
var _sqrt = require("./sqrt");
var _square = require("./square");
var _squaredDifference = require("./squared_difference");
var _squeeze = require("./squeeze");
var _stack = require("./stack");
var _step = require("./step");
var _stridedSlice = require("./strided_slice");
var _sub = require("./sub");
var _sum = require("./sum");
var _tan = require("./tan");
var _tanh = require("./tanh");
var _tensor = require("./tensor");
var _tensor1D = require("./tensor1d");
var _tensor2D = require("./tensor2d");
var _tensor3D = require("./tensor3d");
var _tensor4D = require("./tensor4d");
var _tensor5D = require("./tensor5d");
var _tensor6D = require("./tensor6d");
var _tensorScatterUpdate = require("./tensor_scatter_update");
var _tile = require("./tile");
var _topk = require("./topk");
var _truncatedNormal = require("./truncated_normal");
var _unique = require("./unique");
var _unsortedSegmentSum = require("./unsorted_segment_sum");
var _unstack = require("./unstack");
var _upperBound = require("./upper_bound");
var _variable = require("./variable");
var _where = require("./where");
var _whereAsync = require("./where_async");
var _zeros = require("./zeros");
var _zerosLike = require("./zeros_like");
var _booleanMask = require("./boolean_mask");
parcelHelpers.exportAll(_booleanMask, exports);
var _transpose = require("./transpose");
parcelHelpers.exportAll(_transpose, exports);
var _norm = require("./norm");
parcelHelpers.exportAll(_norm, exports);
var _movingAverage = require("./moving_average");
parcelHelpers.exportAll(_movingAverage, exports);
var _scatterNd = require("./scatter_nd");
parcelHelpers.exportAll(_scatterNd, exports);
var _searchSorted = require("./search_sorted");
parcelHelpers.exportAll(_searchSorted, exports);
var _sparseToDense = require("./sparse_to_dense");
parcelHelpers.exportAll(_sparseToDense, exports);
var _gatherNd = require("./gather_nd");
parcelHelpers.exportAll(_gatherNd, exports);
var _dropout = require("./dropout");
parcelHelpers.exportAll(_dropout, exports);
var _signalOpsUtil = require("./signal_ops_util");
parcelHelpers.exportAll(_signalOpsUtil, exports);
var _inTopK = require("./in_top_k");
parcelHelpers.exportAll(_inTopK, exports);
var _operation = require("./operation");
var _fusedOps = require("./fused_ops");
var _hammingWindow = require("./signal/hamming_window");
var _hannWindow = require("./signal/hann_window");
var _frame = require("./signal/frame");
var _stft = require("./signal/stft");
// Image Ops namespace
var _cropAndResize = require("./image/crop_and_resize");
var _flipLeftRight = require("./image/flip_left_right");
var _grayscaleToRgb = require("./image/grayscale_to_rgb");
var _rgbToGrayscale = require("./image/rgb_to_grayscale");
var _rotateWithOffset = require("./image/rotate_with_offset");
var _nonMaxSuppression = require("./image/non_max_suppression");
var _nonMaxSuppressionAsync = require("./image/non_max_suppression_async");
var _nonMaxSuppressionWithScore = require("./image/non_max_suppression_with_score");
var _nonMaxSuppressionWithScoreAsync = require("./image/non_max_suppression_with_score_async");
var _nonMaxSuppressionPadded = require("./image/non_max_suppression_padded");
var _nonMaxSuppressionPaddedAsync = require("./image/non_max_suppression_padded_async");
var _resizeBilinear = require("./image/resize_bilinear");
var _resizeNearestNeighbor = require("./image/resize_nearest_neighbor");
var _threshold = require("./image/threshold");
var _transform = require("./image/transform");
// linalg namespace
var _bandPart = require("./linalg/band_part");
var _gramSchmidt = require("./linalg/gram_schmidt");
var _qr = require("./linalg/qr");
// losses namespace;
var _absoluteDifference = require("./losses/absolute_difference");
var _computeWeightedLoss = require("./losses/compute_weighted_loss");
var _cosineDistance = require("./losses/cosine_distance");
var _hingeLoss = require("./losses/hinge_loss");
var _huberLoss = require("./losses/huber_loss");
var _logLoss = require("./losses/log_loss");
var _meanSquaredError = require("./losses/mean_squared_error");
var _sigmoidCrossEntropy = require("./losses/sigmoid_cross_entropy");
var _softmaxCrossEntropy = require("./losses/softmax_cross_entropy");
var _sparseFillEmptyRows = require("./sparse/sparse_fill_empty_rows");
var _sparseReshape = require("./sparse/sparse_reshape");
var _sparseSegmentMean = require("./sparse/sparse_segment_mean");
var _sparseSegmentSum = require("./sparse/sparse_segment_sum");
var _stringNGrams = require("./string/string_n_grams");
var _stringSplit = require("./string/string_split");
var _stringToHashBucketFast = require("./string/string_to_hash_bucket_fast");
var _staticRegexReplace = require("./string/static_regex_replace");
const spectral = {
    fft: (0, _fft.fft),
    ifft: (0, _ifft.ifft),
    rfft: (0, _rfft.rfft),
    irfft: (0, _irfft.irfft)
};
const signal = {
    hammingWindow: (0, _hammingWindow.hammingWindow),
    hannWindow: (0, _hannWindow.hannWindow),
    frame: (0, _frame.frame),
    stft: (0, _stft.stft)
};
const image = {
    flipLeftRight: (0, _flipLeftRight.flipLeftRight),
    grayscaleToRGB: (0, _grayscaleToRgb.grayscaleToRGB),
    resizeNearestNeighbor: (0, _resizeNearestNeighbor.resizeNearestNeighbor),
    resizeBilinear: (0, _resizeBilinear.resizeBilinear),
    rgbToGrayscale: (0, _rgbToGrayscale.rgbToGrayscale),
    rotateWithOffset: (0, _rotateWithOffset.rotateWithOffset),
    cropAndResize: (0, _cropAndResize.cropAndResize),
    nonMaxSuppression: (0, _nonMaxSuppression.nonMaxSuppression),
    nonMaxSuppressionAsync: (0, _nonMaxSuppressionAsync.nonMaxSuppressionAsync),
    nonMaxSuppressionWithScore: (0, _nonMaxSuppressionWithScore.nonMaxSuppressionWithScore),
    nonMaxSuppressionWithScoreAsync: (0, _nonMaxSuppressionWithScoreAsync.nonMaxSuppressionWithScoreAsync),
    nonMaxSuppressionPadded: (0, _nonMaxSuppressionPadded.nonMaxSuppressionPadded),
    nonMaxSuppressionPaddedAsync: (0, _nonMaxSuppressionPaddedAsync.nonMaxSuppressionPaddedAsync),
    threshold: (0, _threshold.threshold),
    transform: (0, _transform.transform)
};
const linalg = {
    bandPart: (0, _bandPart.bandPart),
    gramSchmidt: (0, _gramSchmidt.gramSchmidt),
    qr: (0, _qr.qr)
};
const losses = {
    absoluteDifference: (0, _absoluteDifference.absoluteDifference),
    computeWeightedLoss: (0, _computeWeightedLoss.computeWeightedLoss),
    cosineDistance: (0, _cosineDistance.cosineDistance),
    hingeLoss: (0, _hingeLoss.hingeLoss),
    huberLoss: (0, _huberLoss.huberLoss),
    logLoss: (0, _logLoss.logLoss),
    meanSquaredError: (0, _meanSquaredError.meanSquaredError),
    sigmoidCrossEntropy: (0, _sigmoidCrossEntropy.sigmoidCrossEntropy),
    softmaxCrossEntropy: (0, _softmaxCrossEntropy.softmaxCrossEntropy)
};
const sparse = {
    sparseFillEmptyRows: (0, _sparseFillEmptyRows.sparseFillEmptyRows),
    sparseReshape: (0, _sparseReshape.sparseReshape),
    sparseSegmentMean: (0, _sparseSegmentMean.sparseSegmentMean),
    sparseSegmentSum: (0, _sparseSegmentSum.sparseSegmentSum)
};
// tslint:disable-next-line:variable-name
const string = {
    stringNGrams: (0, _stringNGrams.stringNGrams),
    stringSplit: (0, _stringSplit.stringSplit),
    stringToHashBucketFast: (0, _stringToHashBucketFast.stringToHashBucketFast),
    staticRegexReplace: (0, _staticRegexReplace.staticRegexReplace)
};

},{"./abs":"iZQ4e","./acos":"dZHka","./acosh":"84Yuy","./add":"aynh7","./add_n":"cEtmn","./all":"a1yTA","./any":"9SyE5","./arg_max":"6RjBh","./arg_min":"ljaN9","./asin":"gHBQs","./asinh":"5c3gG","./atan":"a4WSB","./atan2":"3i20s","./atanh":"1G9hg","./avg_pool":"d8nrP","./avg_pool_3d":"bdrlT","./basic_lstm_cell":"581eT","./batch_to_space_nd":"5A2j5","./batchnorm":"5eVFD","./batchnorm2d":"9ULx9","./batchnorm3d":"y3kug","./batchnorm4d":"4l9oM","./bincount":"gW00L","./bitwise_and":"jmT4J","./broadcast_args":"d9ZLh","./broadcast_to":"iMuoF","./buffer":"dYyY7","./cast":"b4VDA","./ceil":"hVmez","./clip_by_value":"cKbfV","./clone":"jmQ7S","./complex":"bPDAx","./concat":"1NOyL","./concat_1d":"jI8Rd","./concat_2d":"2ZT6j","./concat_3d":"g5T4x","./concat_4d":"fsDG1","./conv1d":"2LKBX","./conv2d":"lmJyO","./conv2d_transpose":"kCheD","./conv3d":"3stvE","./conv3d_transpose":"4J2I2","./cos":"aBory","./cosh":"kFKmS","./cumprod":"gorKt","./cumsum":"8KDcI","./dense_bincount":"3PEDs","./depth_to_space":"ipPbw","./depthwise_conv2d":"cZHpb","./diag":"3orNc","./dilation2d":"7hUfK","./div":"5ZT1h","./div_no_nan":"aw8lb","./dot":"b1M9k","./einsum":"adKPG","./elu":"dE197","./ensure_shape":"f2yaA","./equal":"htEqc","./erf":"e0JJC","./euclidean_norm":"8OAGY","./exp":"1CsGu","./expand_dims":"lc7t7","./expm1":"lDAq4","./eye":"hDQ1U","./fill":"i7goe","./floor":"35qCf","./floorDiv":"jboDF","./gather":"5p5vK","./greater":"3Nh8O","./greater_equal":"f7S2c","./imag":"lDORm","./is_finite":"ddlWF","./is_inf":"2QZvv","./is_nan":"58UC5","./leaky_relu":"aRBcm","./less":"hCGN5","./less_equal":"j9Jhy","./linspace":"7Vd0v","./local_response_normalization":"eTOXs","./log":"iex5O","./log1p":"jKhH4","./log_sigmoid":"2X6mM","./log_softmax":"5yBCd","./log_sum_exp":"feRCM","./logical_and":"6zYjJ","./logical_not":"gSuOh","./logical_or":"dH6xG","./logical_xor":"7Y9m5","./lower_bound":"2XtFq","./mat_mul":"fiMp4","./max":"cb6Hb","./max_pool":"hx37d","./max_pool_3d":"z14HA","./max_pool_with_argmax":"hPUnJ","./maximum":"1nFpL","./mean":"hzzLs","./meshgrid":"jFI6G","./min":"fhwke","./minimum":"guwat","./mirror_pad":"1Sy5n","./mod":"g5efu","./moments":"ef1IE","./mul":"fOWrC","./multi_rnn_cell":"lNZ3B","./multinomial":"g1s8L","./neg":"3jQfI","./not_equal":"gUKb0","./one_hot":"91jNM","./ones":"2gdnE","./ones_like":"7TDfu","./outer_product":"emoMO","./pad":"89mWw","./pad1d":"gOSFg","./pad2d":"2QXo6","./pad3d":"6eqEc","./pad4d":"dP5zO","./pool":"sCTfp","./pow":"gezpC","./prelu":"azJEF","./print":"5RyfW","./prod":"hawIk","./ragged_gather":"a5cAV","./ragged_range":"9Z2C7","./ragged_tensor_to_tensor":"93163","./rand":"eyCJc","./random_gamma":"4v8VB","./random_normal":"72nVa","./random_standard_normal":"eOkY1","./random_uniform":"8LDYI","./random_uniform_int":"a0IEB","./range":"3seHd","./real":"8i3Ch","./reciprocal":"bxIjM","./relu":"lcv3E","./relu6":"cvYLK","./reshape":"ew3wZ","./reverse":"9yp4z","./reverse_1d":"1LPYc","./reverse_2d":"7RrO2","./reverse_3d":"jdnrR","./reverse_4d":"7dqzq","./round":"4aCvQ","./rsqrt":"7D6sy","./scalar":"7lhC8","./selu":"kGS0T","./separable_conv2d":"1MPx9","./setdiff1d_async":"8tR1Z","./sigmoid":"81E16","./sign":"3bcO0","./sin":"gSMA7","./sinh":"5VZJu","./slice":"dr7Gq","./slice1d":"4krOi","./slice2d":"2Dy3M","./slice3d":"3DCYE","./slice4d":"bMRWt","./softmax":"5C9bA","./softplus":"4zLJS","./space_to_batch_nd":"dDwiy","./spectral/fft":"2vcIN","./spectral/ifft":"4lzNl","./spectral/irfft":"hzbzS","./spectral/rfft":"hnd5A","./split":"l0HF2","./sqrt":"cOGi9","./square":"cOosW","./squared_difference":"iHBgt","./squeeze":"VbY6Z","./stack":"jeh7M","./step":"bBBT4","./strided_slice":"9Kwrw","./sub":"jBBQ2","./sum":"5awd0","./tan":"c36dQ","./tanh":"jXQuL","./tensor":"jdbb1","./tensor1d":"aEL9e","./tensor2d":"faX5H","./tensor3d":"lVDsd","./tensor4d":"5QFb1","./tensor5d":"7LoZX","./tensor6d":"jFXe2","./tensor_scatter_update":"1L18R","./tile":"gzqG2","./topk":"jUBl9","./truncated_normal":"jsVmm","./unique":"kc2B3","./unsorted_segment_sum":"8ZRDN","./unstack":"90Kbb","./upper_bound":"csQSE","./variable":"c3Bg5","./where":"47cKc","./where_async":"2KVsP","./zeros":"fcPHR","./zeros_like":"1Xjct","./boolean_mask":"jnpjK","./transpose":"2bqbS","./norm":"eOnCA","./moving_average":"hLPKe","./scatter_nd":"2xMWX","./search_sorted":"4gaGh","./sparse_to_dense":"gXN1p","./gather_nd":"9KIwq","./dropout":"6nVzD","./signal_ops_util":"1EuF4","./in_top_k":"9rwSr","./operation":"ip64W","./fused_ops":"jqlvt","./signal/hamming_window":"crMwx","./signal/hann_window":"lwQEl","./signal/frame":"3ntgK","./signal/stft":"gUBf5","./image/crop_and_resize":"kQOfI","./image/flip_left_right":"7xdFd","./image/grayscale_to_rgb":"hbmqN","./image/rgb_to_grayscale":"hrZfi","./image/rotate_with_offset":"df6QK","./image/non_max_suppression":"bQBrw","./image/non_max_suppression_async":"8ps8b","./image/non_max_suppression_with_score":"khN9i","./image/non_max_suppression_with_score_async":"bH9TE","./image/non_max_suppression_padded":"3Hdah","./image/non_max_suppression_padded_async":"dJg2S","./image/resize_bilinear":"h5fgY","./image/resize_nearest_neighbor":"6vytD","./image/threshold":"d58R4","./image/transform":"sijU0","./linalg/band_part":"cLsM5","./linalg/gram_schmidt":"dmmpK","./linalg/qr":"dhxAc","./losses/absolute_difference":"5sJon","./losses/compute_weighted_loss":"1agzr","./losses/cosine_distance":"enn6x","./losses/hinge_loss":"1aaXc","./losses/huber_loss":"1c06w","./losses/log_loss":"UdKNC","./losses/mean_squared_error":"8Mugj","./losses/sigmoid_cross_entropy":"77Q3C","./losses/softmax_cross_entropy":"cGUog","./sparse/sparse_fill_empty_rows":"1MyfF","./sparse/sparse_reshape":"f6fUL","./sparse/sparse_segment_mean":"d0IYG","./sparse/sparse_segment_sum":"7MtJg","./string/string_n_grams":"gELNE","./string/string_split":"3z1cj","./string/string_to_hash_bucket_fast":"8mVop","./string/static_regex_replace":"dLgGV","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iZQ4e":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "abs", ()=>abs);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes absolute value element-wise: `abs(x)`
 *
 * ```js
 * const x = tf.tensor1d([-1, 2, -3, 4]);
 *
 * x.abs().print();  // or tf.abs(x)
 * ```
 * @param x The input `tf.Tensor`.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function abs_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "abs");
    if ($x.dtype === "complex64") {
        const inputs = {
            x: $x
        };
        return (0, _engine.ENGINE).runKernel((0, _kernelNames.ComplexAbs), inputs);
    } else {
        const inputs = {
            x: $x
        };
        return (0, _engine.ENGINE).runKernel((0, _kernelNames.Abs), inputs);
    }
}
const abs = /* @__PURE__ */ (0, _operation.op)({
    abs_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dZHka":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "acos", ()=>acos);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.acos().print();  // or tf.acos(x)
 * ```
 * @param x The input tensor.
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function acos_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "acos");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Acos), inputs);
}
const acos = /* @__PURE__ */ (0, _operation.op)({
    acos_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"84Yuy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "acosh", ()=>acosh);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:
 * `acosh(x)`
 *
 * ```js
 * const x = tf.tensor1d([10, 1, 3, 5.7]);
 *
 * x.acosh().print();  // or tf.acosh(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function acosh_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "acosh");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Acosh), inputs);
}
const acosh = /* @__PURE__ */ (0, _operation.op)({
    acosh_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cEtmn":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "addN", ()=>addN);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 * const b = tf.tensor1d([3, 4]);
 * const c = tf.tensor1d([5, 6]);
 *
 * tf.addN([a, b, c]).print();
 * ```
 * @param tensors A list of tensors with the same shape and dtype.
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function addN_(tensors) {
    _util.assert(Array.isArray(tensors), ()=>"The argument passed to tf.addN() must be a list of tensors");
    _util.assert(tensors.length >= 1, ()=>`Must pass at least one tensor to tf.addN(), but got ` + `${tensors.length}`);
    const $tensors = tensors.map((t, i)=>(0, _tensorUtilEnv.convertToTensor)(t, `tensors${i}`, "addN"));
    const firstTensor = $tensors[0];
    $tensors.forEach((t)=>{
        if (t.dtype !== firstTensor.dtype) throw new Error("All tensors passed to tf.addN() must have the same dtype");
    });
    $tensors.forEach((t)=>{
        if (!_util.arraysEqual(t.shape, firstTensor.shape)) throw new Error("All tensors passed to tf.addN() must have the same shape");
    });
    const inputs = $tensors;
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.AddN), inputs);
}
const addN = /* @__PURE__ */ (0, _operation.op)({
    addN_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a1yTA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "all", ()=>all);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the logical and of elements across dimensions of a `tf.Tensor`.
 *
 * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
 * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
 * `axes`. If `keepDims` is true, the reduced dimensions are retained with
 * length 1. If `axes` has no entries, all dimensions are reduced, and a
 * `tf.Tensor` with a single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 1, 1], 'bool');
 *
 * x.all().print();  // or tf.all(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
 *
 * const axis = 1;
 * x.all(axis).print();  // or tf.all(x, axis)
 * ```
 *
 * @param x The input tensor. Must be of dtype bool.
 * @param axis The dimension(s) to reduce. By default it reduces
 *     all dimensions.
 * @param keepDims If true, retains reduced dimensions with size 1.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function all_(x, axis = null, keepDims = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "all", "bool");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        keepDims
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.All), inputs, attrs);
}
const all = /* @__PURE__ */ (0, _operation.op)({
    all_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9SyE5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "any", ()=>any);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the logical or of elements across dimensions of a `tf.Tensor`.
 *
 * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
 * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
 * `axes`. If `keepDims` is true, the reduced dimensions are retained with
 * length 1. If `axes` has no entries, all dimensions are reduced, and a
 * `tf.Tensor` with a single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 1, 1], 'bool');
 *
 * x.any().print();  // or tf.any(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
 *
 * const axis = 1;
 * x.any(axis).print();  // or tf.any(x, axis)
 * ```
 *
 * @param x The input tensor. Must be of dtype bool.
 * @param axis The dimension(s) to reduce. By default it reduces
 *     all dimensions.
 * @param keepDims If true, retains reduced dimensions with size 1.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function any_(x, axis = null, keepDims = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "any", "bool");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        keepDims
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Any), inputs, attrs);
}
const any = /* @__PURE__ */ (0, _operation.op)({
    any_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6RjBh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "argMax", ()=>argMax);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns the indices of the maximum values along an `axis`.
 *
 * The result has the same shape as `input` with the dimension along `axis`
 * removed.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.argMax().print();  // or tf.argMax(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
 *
 * const axis = 1;
 * x.argMax(axis).print();  // or tf.argMax(x, axis)
 * ```
 *
 * @param x The input tensor.
 * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function argMax_(x, axis = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "argMax");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.ArgMax), inputs, attrs);
}
const argMax = /* @__PURE__ */ (0, _operation.op)({
    argMax_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ljaN9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "argMin", ()=>argMin);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns the indices of the minimum values along an `axis`.
 *
 * The result has the same shape as `input` with the dimension along `axis`
 * removed.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.argMin().print();  // or tf.argMin(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
 *
 * const axis = 1;
 * x.argMin(axis).print();  // or tf.argMin(x, axis)
 * ```
 *
 * @param x The input tensor.
 * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function argMin_(x, axis = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "argMin");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.ArgMin), inputs, attrs);
}
const argMin = /* @__PURE__ */ (0, _operation.op)({
    argMin_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gHBQs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "asin", ()=>asin);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.asin().print();  // or tf.asin(x)
 * ```
 * @param x The input tensor.
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function asin_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "asin");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Asin), inputs);
}
const asin = /* @__PURE__ */ (0, _operation.op)({
    asin_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5c3gG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "asinh", ()=>asinh);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:
 * `asinh(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.asinh().print();  // or tf.asinh(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function asinh_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "asinh");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Asinh), inputs);
}
const asinh = /* @__PURE__ */ (0, _operation.op)({
    asinh_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a4WSB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atan", ()=>atan);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.atan().print();  // or tf.atan(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function atan_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "atan");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Atan), inputs);
}
const atan = /* @__PURE__ */ (0, _operation.op)({
    atan_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3i20s":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atan2", ()=>atan2);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
 * Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
 * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
 *
 * tf.atan2(a, b).print()
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function atan2_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "atan2");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "atan2");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Atan2), inputs);
}
const atan2 = /* @__PURE__ */ (0, _operation.op)({
    atan2_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1G9hg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atanh", ()=>atanh);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:
 * `atanh(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, .1, -.1, .7]);
 *
 * x.atanh().print();  // or tf.atanh(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function atanh_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "atanh");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Atanh), inputs);
}
const atanh = /* @__PURE__ */ (0, _operation.op)({
    atanh_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d8nrP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool", ()=>avgPool);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _cast = require("./cast");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the 2D average pooling of an image.
 *
 * @param x The input tensor, of rank 4 or rank 3 of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
 * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
 *     `filterSize` is a single number, then `filterHeight == filterWidth`.
 * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
 *     `strides` is a single number, then `strideHeight == strideWidth`.
 * @param pad The type of padding algorithm:
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *         https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function avgPool_(x, filterSize, strides, pad, dimRoundingMode) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "avgPool", "float32");
    const dilations = 1;
    _util.assert(_convUtil.eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in avgPool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    _util.assert(x4D.rank === 4, ()=>`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
    _convUtil.checkPadOnDimRoundingMode("avgPool", pad, dimRoundingMode);
    const inputs = {
        x: x4D
    };
    const attrs = {
        filterSize,
        strides,
        pad,
        dimRoundingMode
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    let res = (0, _engine.ENGINE).runKernel((0, _kernelNames.AvgPool), inputs, attrs);
    res = (0, _cast.cast)(res, $x.dtype);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const avgPool = /* @__PURE__ */ (0, _operation.op)({
    avgPool_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./cast":"b4VDA","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bf4MH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 *
 * @param inputShape Input tensor shape is of the following dimensions:
 *     `[batch, height, width, inChannels]`.
 * @param filterShape The filter shape is of the following dimensions:
 *     `[filterHeight, filterWidth, depth]`.
 * @param strides The strides of the sliding window for each dimension of the
 *     input tensor: `[strideHeight, strideWidth]`.
 *     If `strides` is a single number,
 *     then `strideHeight == strideWidth`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1*1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat The data format of the input and output data.
 *     Defaults to 'NHWC'.
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.
 *     Defaults to `[1, 1]`. If `dilations` is a single number, then
 *     `dilationHeight == dilationWidth`.
 */ parcelHelpers.export(exports, "computeDilation2DInfo", ()=>computeDilation2DInfo);
parcelHelpers.export(exports, "computePool2DInfo", ()=>computePool2DInfo);
/**
 * Computes the information for a forward pass of a pooling3D operation.
 */ parcelHelpers.export(exports, "computePool3DInfo", ()=>computePool3DInfo);
/**
 * Computes the information for a forward pass of a convolution/pooling
 * operation.
 */ parcelHelpers.export(exports, "computeConv2DInfo", ()=>computeConv2DInfo);
/**
 * Computes the information for a forward pass of a 3D convolution/pooling
 * operation.
 */ parcelHelpers.export(exports, "computeConv3DInfo", ()=>computeConv3DInfo);
parcelHelpers.export(exports, "computeDefaultPad", ()=>computeDefaultPad);
parcelHelpers.export(exports, "tupleValuesAreOne", ()=>tupleValuesAreOne);
parcelHelpers.export(exports, "eitherStridesOrDilationsAreOne", ()=>eitherStridesOrDilationsAreOne);
parcelHelpers.export(exports, "stridesOrDilationsArePositive", ()=>stridesOrDilationsArePositive);
/**
 * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to
 *    'channelsLast'|'channelsFirst'
 * @param dataFormat in 'NHWC'|'NCHW' mode
 * @return dataFormat in 'channelsLast'|'channelsFirst' mode
 * @throws unknown dataFormat
 */ parcelHelpers.export(exports, "convertConv2DDataFormat", ()=>convertConv2DDataFormat);
/**
 * Check validity of pad when using dimRoundingMode.
 * @param opDesc A string of op description
 * @param pad The type of padding algorithm.
 *   - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *   - `valid` output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 * @throws unknown padding parameter
 */ parcelHelpers.export(exports, "checkPadOnDimRoundingMode", ()=>checkPadOnDimRoundingMode);
var _util = require("../util");
function computeDilation2DInfo(inputShape, filterShape, strides, pad, dataFormat = "NHWC", dilations) {
    // `computerConv2DInfo` require filterShape to be in the dimension of:
    // `[filterHeight, filterWidth, depth, outDepth]`, dilation2d doesn't have
    // outDepth, it should have the same depth as the input.
    // Input shape: [batch, height, width, inChannels]
    const inputChannels = inputShape[3];
    const $filterShape = [
        ...filterShape,
        inputChannels
    ];
    const $dataFormat = convertConv2DDataFormat(dataFormat);
    return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad, null, null, $dataFormat);
}
function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = "channelsLast") {
    const [filterHeight, filterWidth] = parseTupleParam(filterSize);
    let filterShape;
    if (dataFormat === "channelsLast") filterShape = [
        filterHeight,
        filterWidth,
        inShape[3],
        inShape[3]
    ];
    else if (dataFormat === "channelsFirst") filterShape = [
        filterHeight,
        filterWidth,
        inShape[1],
        inShape[1]
    ];
    else throw new Error(`Unknown dataFormat ${dataFormat}`);
    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);
}
function computePool3DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat = "NDHWC") {
    const [filterDepth, filterHeight, filterWidth] = parse3TupleParam(filterSize);
    let filterShape;
    let $dataFormat;
    if (dataFormat === "NDHWC") {
        $dataFormat = "channelsLast";
        filterShape = [
            filterDepth,
            filterHeight,
            filterWidth,
            inShape[4],
            inShape[4]
        ];
    } else if (dataFormat === "NCDHW") {
        $dataFormat = "channelsFirst";
        filterShape = [
            filterDepth,
            filterHeight,
            filterWidth,
            inShape[1],
            inShape[1]
        ];
    } else throw new Error(`Unknown dataFormat ${dataFormat}`);
    return computeConv3DInfo(inShape, filterShape, strides, dilations, pad, false, $dataFormat, roundingMode);
}
function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise = false, dataFormat = "channelsLast") {
    let [batchSize, inHeight, inWidth, inChannels] = [
        -1,
        -1,
        -1,
        -1
    ];
    if (dataFormat === "channelsLast") [batchSize, inHeight, inWidth, inChannels] = inShape;
    else if (dataFormat === "channelsFirst") [batchSize, inChannels, inHeight, inWidth] = inShape;
    else throw new Error(`Unknown dataFormat ${dataFormat}`);
    const [filterHeight, filterWidth, , filterChannels] = filterShape;
    const [strideHeight, strideWidth] = parseTupleParam(strides);
    const [dilationHeight, dilationWidth] = parseTupleParam(dilations);
    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
    const { padInfo, outHeight, outWidth } = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat);
    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
    let outShape;
    if (dataFormat === "channelsFirst") outShape = [
        batchSize,
        outChannels,
        outHeight,
        outWidth
    ];
    else if (dataFormat === "channelsLast") outShape = [
        batchSize,
        outHeight,
        outWidth,
        outChannels
    ];
    return {
        batchSize,
        dataFormat,
        inHeight,
        inWidth,
        inChannels,
        outHeight,
        outWidth,
        outChannels,
        padInfo,
        strideHeight,
        strideWidth,
        filterHeight,
        filterWidth,
        effectiveFilterHeight,
        effectiveFilterWidth,
        dilationHeight,
        dilationWidth,
        inShape,
        outShape,
        filterShape
    };
}
function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise = false, dataFormat = "channelsLast", roundingMode) {
    let [batchSize, inDepth, inHeight, inWidth, inChannels] = [
        -1,
        -1,
        -1,
        -1,
        -1
    ];
    if (dataFormat === "channelsLast") [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;
    else if (dataFormat === "channelsFirst") [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;
    else throw new Error(`Unknown dataFormat ${dataFormat}`);
    const [filterDepth, filterHeight, filterWidth, , filterChannels] = filterShape;
    const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);
    const [dilationDepth, dilationHeight, dilationWidth] = parse3TupleParam(dilations);
    const effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
    const effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
    const effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
    const { padInfo, outDepth, outHeight, outWidth } = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth, roundingMode);
    const outChannels = depthwise ? filterChannels * inChannels : filterChannels;
    let outShape;
    if (dataFormat === "channelsFirst") outShape = [
        batchSize,
        outChannels,
        outDepth,
        outHeight,
        outWidth
    ];
    else if (dataFormat === "channelsLast") outShape = [
        batchSize,
        outDepth,
        outHeight,
        outWidth,
        outChannels
    ];
    return {
        batchSize,
        dataFormat,
        inDepth,
        inHeight,
        inWidth,
        inChannels,
        outDepth,
        outHeight,
        outWidth,
        outChannels,
        padInfo,
        strideDepth,
        strideHeight,
        strideWidth,
        filterDepth,
        filterHeight,
        filterWidth,
        effectiveFilterDepth,
        effectiveFilterHeight,
        effectiveFilterWidth,
        dilationDepth,
        dilationHeight,
        dilationWidth,
        inShape,
        outShape,
        filterShape
    };
}
function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) {
    if (zeroPad == null) zeroPad = computeDefaultPad(inShape, fieldSize, stride);
    const inputRows = inShape[0];
    const inputCols = inShape[1];
    const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
    const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
    return [
        outputRows,
        outputCols
    ];
}
function computeOutputShape4D(inShape, filterShape, outChannels, strides, zeroPad, roundingMode) {
    if (zeroPad == null) zeroPad = computeDefaultPad(inShape, filterShape[0], strides[0]);
    const outShape = [
        0,
        0,
        0,
        outChannels
    ];
    for(let index = 0; index < 3; index++)if (inShape[index] + 2 * zeroPad >= filterShape[index]) outShape[index] = round((inShape[index] - filterShape[index] + 2 * zeroPad) / strides[index] + 1, roundingMode);
    return outShape;
}
function computeDefaultPad(inputShape, fieldSize, stride, dilation = 1) {
    const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
}
function parseTupleParam(param) {
    if (typeof param === "number") return [
        param,
        param,
        param
    ];
    if (param.length === 2) return [
        param[0],
        param[1],
        1
    ];
    return param;
}
function parse3TupleParam(param) {
    return typeof param === "number" ? [
        param,
        param,
        param
    ] : param;
}
/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d
 * Atrous convolution is equivalent to standard convolution with upsampled
 * filters with effective_filter_height =
 * filter_height + (filter_height - 1) * (dilation - 1)
 * and effective_filter_width =
 * filter_width + (filter_width - 1) * (dilation - 1),
 * produced by inserting dilation - 1 zeros along consecutive elements across
 * the filters' spatial dimensions.
 * When there is a dilation, this converts a filter dimension to the
 * effective filter dimension, so it can be used in a standard convolution.
 */ function getEffectiveFilterSize(filterSize, dilation) {
    if (dilation <= 1) return filterSize;
    return filterSize + (filterSize - 1) * (dilation - 1);
}
function getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
    let padInfo;
    let outHeight;
    let outWidth;
    if (typeof pad === "number") {
        const padType = pad === 0 ? "VALID" : "NUMBER";
        padInfo = {
            top: pad,
            bottom: pad,
            left: pad,
            right: pad,
            type: padType
        };
        const outShape = computeOutputShape2D([
            inHeight,
            inWidth
        ], filterHeight, strideHeight, pad, roundingMode);
        outHeight = outShape[0];
        outWidth = outShape[1];
    } else if (pad === "same") {
        outHeight = Math.ceil(inHeight / strideHeight);
        outWidth = Math.ceil(inWidth / strideWidth);
        const padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight);
        const padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth);
        const top = Math.floor(padAlongHeight / 2);
        const bottom = padAlongHeight - top;
        const left = Math.floor(padAlongWidth / 2);
        const right = padAlongWidth - left;
        padInfo = {
            top,
            bottom,
            left,
            right,
            type: "SAME"
        };
    } else if (pad === "valid") {
        padInfo = {
            top: 0,
            bottom: 0,
            left: 0,
            right: 0,
            type: "VALID"
        };
        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
    } else if (typeof pad === "object") {
        const top = dataFormat === "channelsLast" ? pad[1][0] : pad[2][0];
        const bottom = dataFormat === "channelsLast" ? pad[1][1] : pad[2][1];
        const left = dataFormat === "channelsLast" ? pad[2][0] : pad[3][0];
        const right = dataFormat === "channelsLast" ? pad[2][1] : pad[3][1];
        const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
        padInfo = {
            top,
            bottom,
            left,
            right,
            type: padType
        };
        outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
        outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
    } else throw Error(`Unknown padding parameter: ${pad}`);
    return {
        padInfo,
        outHeight,
        outWidth
    };
}
function get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
    let padInfo;
    let outDepth;
    let outHeight;
    let outWidth;
    if (pad === "valid") pad = 0;
    if (typeof pad === "number") {
        const padType = pad === 0 ? "VALID" : "NUMBER";
        padInfo = {
            top: pad,
            bottom: pad,
            left: pad,
            right: pad,
            front: pad,
            back: pad,
            type: padType
        };
        const outShape = computeOutputShape4D([
            inDepth,
            inHeight,
            inWidth,
            1
        ], [
            filterDepth,
            filterHeight,
            filterWidth
        ], 1, [
            strideDepth,
            strideHeight,
            strideWidth
        ], pad, roundingMode);
        outDepth = outShape[0];
        outHeight = outShape[1];
        outWidth = outShape[2];
    } else if (pad === "same") {
        outDepth = Math.ceil(inDepth / strideDepth);
        outHeight = Math.ceil(inHeight / strideHeight);
        outWidth = Math.ceil(inWidth / strideWidth);
        const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
        const padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
        const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
        const front = Math.floor(padAlongDepth / 2);
        const back = padAlongDepth - front;
        const top = Math.floor(padAlongHeight / 2);
        const bottom = padAlongHeight - top;
        const left = Math.floor(padAlongWidth / 2);
        const right = padAlongWidth - left;
        padInfo = {
            top,
            bottom,
            left,
            right,
            front,
            back,
            type: "SAME"
        };
    } else throw Error(`Unknown padding parameter: ${pad}`);
    return {
        padInfo,
        outDepth,
        outHeight,
        outWidth
    };
}
/**
 * Rounds a value depending on the rounding mode
 * @param value
 * @param roundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 */ function round(value, roundingMode) {
    if (!roundingMode) return Math.trunc(value);
    switch(roundingMode){
        case "round":
            // used for Caffe Conv
            return Math.round(value);
        case "ceil":
            // used for Caffe Pool
            return Math.ceil(value);
        case "floor":
            return Math.floor(value);
        default:
            throw new Error(`Unknown roundingMode ${roundingMode}`);
    }
}
function tupleValuesAreOne(param) {
    const [dimA, dimB, dimC] = parseTupleParam(param);
    return dimA === 1 && dimB === 1 && dimC === 1;
}
function eitherStridesOrDilationsAreOne(strides, dilations) {
    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
}
function stridesOrDilationsArePositive(values) {
    return parseTupleParam(values).every((value)=>value > 0);
}
function convertConv2DDataFormat(dataFormat) {
    if (dataFormat === "NHWC") return "channelsLast";
    else if (dataFormat === "NCHW") return "channelsFirst";
    else throw new Error(`Unknown dataFormat ${dataFormat}`);
}
function checkPadOnDimRoundingMode(opDesc, pad, dimRoundingMode) {
    if (dimRoundingMode != null) {
        if (typeof pad === "string") throw Error(`Error in ${opDesc}: pad must be an integer when using ` + `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
        else if (typeof pad === "number") _util.assert(_util.isInt(pad), ()=>`Error in ${opDesc}: pad must be an integer when using ` + `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
        else if (typeof pad === "object") pad.forEach((p)=>{
            p.forEach((v)=>{
                _util.assert(_util.isInt(v), ()=>`Error in ${opDesc}: pad must be an integer when using ` + `dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);
            });
        });
        else throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad}`);
    }
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ew3wZ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reshape", ()=>reshape);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Reshapes a `tf.Tensor` to a given shape.
 *
 * Given an input tensor, returns a new tensor with the same values as the
 * input tensor with shape `shape`.
 *
 * If one component of shape is the special value -1, the size of that
 * dimension is computed so that the total size remains constant. In
 * particular, a shape of [-1] flattens into 1-D. At most one component of
 * shape can be -1.
 *
 * If shape is 1-D or higher, then the operation returns a tensor with shape
 * shape filled with the values of tensor. In this case, the number of
 * elements implied by shape must be the same as the number of elements in
 * tensor.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 * x.reshape([2, 2]).print();
 * ```
 *
 * @param x The input tensor to be reshaped.
 * @param shape An array of integers defining the output tensor shape.
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function reshape_(x, shape) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "reshape", "string_or_numeric");
    const inputs = {
        x: $x
    };
    const attrs = {
        shape
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Reshape), inputs, attrs);
}
const reshape = /* @__PURE__ */ (0, _operation.op)({
    reshape_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bdrlT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool3d", ()=>avgPool3d);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _cast = require("./cast");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the 3D average pooling.
 *
 * ```js
 * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
 * const result = tf.avgPool3d(x, 2, 1, 'valid');
 * result.print();
 * ```
 *
 * @param x The input tensor, of rank 5 or rank 4 of shape
 *     `[batch, depth, height, width, inChannels]`.
 * @param filterSize The filter size:
 *     `[filterDepth, filterHeight, filterWidth]`.
 *     If `filterSize` is a single number,
 *     then `filterDepth == filterHeight == filterWidth`.
 * @param strides The strides of the pooling:
 *     `[strideDepth, strideHeight, strideWidth]`.
 *     If `strides` is a single number,
 *     then `strideDepth == strideHeight == strideWidth`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1*1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
 *     "NDHWC". Specify the data format of the input and output data. With the
 *     default format "NDHWC", the data is stored in the order of: [batch,
 *     depth, height, width, channels]. Only "NDHWC" is currently supported.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = "NDHWC") {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "avgPool3d", "float32");
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
        reshapedTo5D = true;
        x5D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2],
            $x.shape[3]
        ]);
    }
    _util.assert(x5D.rank === 5, ()=>`Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
    _util.assert(dataFormat === "NDHWC", ()=>`Error in avgPool3d: Only NDHWC is currently supported, ` + `but got dataFormat of ${dataFormat}`);
    _util.assert(typeof strides === "number" && strides > 0 || Array.isArray(strides) && strides[0] > 0 && strides[1] > 0 && strides[2] > 0, ()=>`Error in avgPool3d: Stride must be > 0, but got '${strides}'`);
    (0, _convUtil.checkPadOnDimRoundingMode)("avgPool3d", pad, dimRoundingMode);
    const inputs = {
        x: x5D
    };
    const attrs = {
        filterSize,
        strides,
        pad,
        dimRoundingMode,
        dataFormat
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    let res = (0, _engine.ENGINE).runKernel((0, _kernelNames.AvgPool3D), inputs, attrs);
    res = (0, _cast.cast)(res, x5D.dtype);
    if (reshapedTo5D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3],
        res.shape[4]
    ]);
    return res;
}
const avgPool3d = /* @__PURE__ */ (0, _operation.op)({
    avgPool3d_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./cast":"b4VDA","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"581eT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "basicLSTMCell", ()=>basicLSTMCell);
var _tensorUtilEnv = require("../tensor_util_env");
var _add = require("./add");
var _concat = require("./concat");
var _matMul = require("./mat_mul");
var _mul = require("./mul");
var _operation = require("./operation");
var _sigmoid = require("./sigmoid");
var _slice = require("./slice");
var _tanh = require("./tanh");
/**
 * Computes the next state and output of a BasicLSTMCell.
 *
 * Returns `[newC, newH]`.
 *
 * Derived from tf.contrib.rnn.BasicLSTMCell.
 *
 * @param forgetBias Forget bias for the cell.
 * @param lstmKernel The weights for the cell.
 * @param lstmBias The bias for the cell.
 * @param data The input to the cell.
 * @param c Previous cell state.
 * @param h Previous cell output.
 *
 * @doc {heading: 'Operations', subheading: 'RNN'}
 */ function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
    const $forgetBias = (0, _tensorUtilEnv.convertToTensor)(forgetBias, "forgetBias", "basicLSTMCell");
    const $lstmKernel = (0, _tensorUtilEnv.convertToTensor)(lstmKernel, "lstmKernel", "basicLSTMCell");
    const $lstmBias = (0, _tensorUtilEnv.convertToTensor)(lstmBias, "lstmBias", "basicLSTMCell");
    const $data = (0, _tensorUtilEnv.convertToTensor)(data, "data", "basicLSTMCell");
    const $c = (0, _tensorUtilEnv.convertToTensor)(c, "c", "basicLSTMCell");
    const $h = (0, _tensorUtilEnv.convertToTensor)(h, "h", "basicLSTMCell");
    const combined = (0, _concat.concat)([
        $data,
        $h
    ], 1);
    const weighted = (0, _matMul.matMul)(combined, $lstmKernel);
    const res = (0, _add.add)(weighted, $lstmBias);
    // i = input_gate, j = new_input, f = forget_gate, o = output_gate
    const batchSize = res.shape[0];
    const sliceCols = res.shape[1] / 4;
    const sliceSize = [
        batchSize,
        sliceCols
    ];
    const i = (0, _slice.slice)(res, [
        0,
        0
    ], sliceSize);
    const j = (0, _slice.slice)(res, [
        0,
        sliceCols
    ], sliceSize);
    const f = (0, _slice.slice)(res, [
        0,
        sliceCols * 2
    ], sliceSize);
    const o = (0, _slice.slice)(res, [
        0,
        sliceCols * 3
    ], sliceSize);
    const newC = (0, _add.add)((0, _mul.mul)((0, _sigmoid.sigmoid)(i), (0, _tanh.tanh)(j)), (0, _mul.mul)($c, (0, _sigmoid.sigmoid)((0, _add.add)($forgetBias, f))));
    const newH = (0, _mul.mul)((0, _tanh.tanh)(newC), (0, _sigmoid.sigmoid)(o));
    return [
        newC,
        newH
    ];
}
const basicLSTMCell = /* @__PURE__ */ (0, _operation.op)({
    basicLSTMCell_
});

},{"../tensor_util_env":"6vfHI","./add":"aynh7","./concat":"1NOyL","./mat_mul":"fiMp4","./mul":"fOWrC","./operation":"ip64W","./sigmoid":"81E16","./slice":"dr7Gq","./tanh":"jXQuL","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1NOyL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concat", ()=>concat);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _clone = require("./clone");
var _operation = require("./operation");
/**
 * Concatenates a list of `tf.Tensor`s along a given axis.
 *
 * The tensors ranks and types must match, and their sizes must match in all
 * dimensions except `axis`.
 *
 * Also available are stricter rank-specific methods that assert that
 * `tensors` are of the given rank:
 *   - `tf.concat1d`
 *   - `tf.concat2d`
 *   - `tf.concat3d`
 *   - `tf.concat4d`
 *
 * Except `tf.concat1d` (which does not have axis param), all methods have
 * same signature as this method.
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 * const b = tf.tensor1d([3, 4]);
 * a.concat(b).print();  // or a.concat(b)
 * ```
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 * const b = tf.tensor1d([3, 4]);
 * const c = tf.tensor1d([5, 6]);
 * tf.concat([a, b, c]).print();
 * ```
 *
 * ```js
 * const a = tf.tensor2d([[1, 2], [10, 20]]);
 * const b = tf.tensor2d([[3, 4], [30, 40]]);
 * const axis = 1;
 * tf.concat([a, b], axis).print();
 * ```
 * @param tensors A list of tensors to concatenate.
 * @param axis The axis to concatenate along. Defaults to 0 (the first dim).
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function concat_(tensors, axis = 0) {
    (0, _util.assert)(tensors.length >= 1, ()=>"Pass at least one tensor to concat");
    const $tensors = (0, _tensorUtilEnv.convertToTensorArray)(tensors, "tensors", "concat", "string_or_numeric");
    if ($tensors[0].dtype === "complex64") $tensors.forEach((tensor)=>{
        if (tensor.dtype !== "complex64") throw new Error(`Cannot concatenate complex64 tensors with a tensor
          with dtype ${tensor.dtype}. `);
    });
    if ($tensors.length === 1) return (0, _clone.clone)($tensors[0]);
    const inputs = $tensors;
    const attr = {
        axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Concat), inputs, attr);
}
const concat = /* @__PURE__ */ (0, _operation.op)({
    concat_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./clone":"jmQ7S","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fiMp4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "matMul", ()=>matMul);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the dot product of two matrices, A * B. These must be matrices.
 *
 * ```js
 * const a = tf.tensor2d([1, 2], [1, 2]);
 * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * a.matMul(b).print();  // or tf.matMul(a, b)
 * ```
 * @param a First matrix in dot product operation.
 * @param b Second matrix in dot product operation.
 * @param transposeA If true, `a` is transposed before multiplication.
 * @param transposeB If true, `b` is transposed before multiplication.
 *
 * @doc {heading: 'Operations', subheading: 'Matrices'}
 */ function matMul_(a, b, transposeA = false, transposeB = false) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "matMul");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "matMul");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    const attrs = {
        transposeA,
        transposeB
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.BatchMatMul), inputs, attrs);
}
const matMul = /* @__PURE__ */ (0, _operation.op)({
    matMul_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"81E16":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sigmoid", ()=>sigmoid);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes sigmoid element-wise, `1 / (1 + exp(-x))`
 *
 * ```js
 * const x = tf.tensor1d([0, -1, 2, -3]);
 *
 * x.sigmoid().print();  // or tf.sigmoid(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function sigmoid_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "sigmoid", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Sigmoid), inputs);
}
const sigmoid = /* @__PURE__ */ (0, _operation.op)({
    sigmoid_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dr7Gq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "slice", ()=>slice);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`
 * and is of size `size`.
 *
 * Also available are stricter rank-specific methods with the same signature
 * as this method that assert that `x` is of the given rank:
 *   - `tf.slice1d`
 *   - `tf.slice2d`
 *   - `tf.slice3d`
 *   - `tf.slice4d`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 *
 * x.slice([1], [2]).print();
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * x.slice([1, 0], [1, 2]).print();
 * ```
 * @param x The input `tf.Tensor` to slice from.
 * @param begin The coordinates to start the slice from. The length can be
 *     less than the rank of x - the rest of the axes will have implicit 0 as
 *     start. Can also be a single number, in which case it specifies the
 *     first axis.
 * @param size The size of the slice. The length can be less than the rank of
 *     x - the rest of the axes will have implicit -1. A value of -1 requests
 *     the rest of the dimensions in the axis. Can also be a single number,
 *     in which case it specifies the size of the first axis.
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function slice_(x, begin, size) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "slice", "string_or_numeric");
    if ($x.rank === 0) throw new Error("Slicing scalar is not possible");
    const inputs = {
        x: $x
    };
    const attrs = {
        begin,
        size
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Slice), inputs, attrs);
}
const slice = /* @__PURE__ */ (0, _operation.op)({
    slice_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jXQuL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tanh", ()=>tanh);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, 70]);
 *
 * x.tanh().print();  // or tf.tanh(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function tanh_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "tanh", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Tanh), inputs);
}
const tanh = /* @__PURE__ */ (0, _operation.op)({
    tanh_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5A2j5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchToSpaceND", ()=>batchToSpaceND);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of
 * shape `blockShape + [batch]`, interleaves these blocks back into the grid
 * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with
 * the same rank as the input. The spatial dimensions of this intermediate
 * result are then optionally cropped according to `crops` to produce the
 * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise
 * description.
 *
 * ```js
 * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
 * const blockShape = [2, 2];
 * const crops = [[0, 0], [0, 0]];
 *
 * x.batchToSpaceND(blockShape, crops).print();
 * ```
 *
 * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
 * remainingShape`, where spatialShape has `M` dimensions.
 * @param blockShape A 1-D array. Must have shape `[M]`, all values must
 * be >= 1.
 * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.
 * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input
 * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required
 * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`
 *
 * This operation is equivalent to the following steps:
 *
 * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,
 * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,
 * x.shape[N-1]]`
 *
 * 2. Permute dimensions of `reshaped` to produce `permuted` of shape `[batch /
 * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],
 * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
 *
 * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /
 * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *
 * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
 *
 * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`
 * according to `crops` to produce the output of shape: `[batch /
 * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],
 * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -
 * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function batchToSpaceND_(x, blockShape, crops) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "batchToSpaceND");
    const prod = blockShape.reduce((a, b)=>a * b);
    _util.assert($x.rank >= 1 + blockShape.length, ()=>`input rank is ${$x.rank} but should be > than blockShape.length ${blockShape.length}`);
    _util.assert(crops.length === blockShape.length, ()=>`crops.length is ${crops.length} but should be equal to blockShape.length  ${blockShape.length}`);
    _util.assert($x.shape[0] % prod === 0, ()=>`input tensor batch is ${$x.shape[0]} but is not divisible by the product of ` + `the elements of blockShape ${blockShape.join(" * ")} === ${prod}`);
    const inputs = {
        x: $x
    };
    const attrs = {
        blockShape,
        crops
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.BatchToSpaceND), inputs, attrs);
}
const batchToSpaceND = /* @__PURE__ */ (0, _operation.op)({
    batchToSpaceND_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5eVFD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchNorm", ()=>batchNorm);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _batchnormUtil = require("./batchnorm_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Batch normalization.
 *
 * As described in
 * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).
 *
 * Mean, variance, scale, and offset can be of two shapes:
 *   - The same shape as the input.
 *   - In the common case, the depth dimension is the last dimension of x, so
 *     the values would be a `tf.Tensor1D` of shape [depth].
 *
 * Also available are stricter rank-specific methods with the same signature
 * as this method that assert that parameters passed are of given rank
 *   - `tf.batchNorm2d`
 *   - `tf.batchNorm3d`
 *   - `tf.batchNorm4d`
 *
 * @param x The input Tensor.
 * @param mean A mean Tensor.
 * @param variance A variance Tensor.
 * @param offset An offset Tensor.
 * @param scale A scale Tensor.
 * @param varianceEpsilon A small float number to avoid dividing by 0.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */ function batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) {
    if (varianceEpsilon == null) varianceEpsilon = 0.001;
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "batchNorm");
    const $mean = (0, _tensorUtilEnv.convertToTensor)(mean, "mean", "batchNorm");
    const $variance = (0, _tensorUtilEnv.convertToTensor)(variance, "variance", "batchNorm");
    let $scale;
    if (scale != null) $scale = (0, _tensorUtilEnv.convertToTensor)(scale, "scale", "batchNorm");
    let $offset;
    if (offset != null) $offset = (0, _tensorUtilEnv.convertToTensor)(offset, "offset", "batchNorm");
    _util.assert($mean.rank === $variance.rank, ()=>"Batch normalization gradient requires mean and variance to have equal ranks.");
    _util.assert($offset == null || $mean.rank === $offset.rank, ()=>"Batch normalization gradient requires mean and offset to have equal ranks.");
    _util.assert($scale == null || $mean.rank === $scale.rank, ()=>"Batch normalization gradient requires mean and scale to have equal ranks.");
    const x4D = (0, _batchnormUtil.xAs4D)($x);
    const inputs = {
        x: x4D,
        scale: $scale,
        offset: $offset,
        mean: $mean,
        variance: $variance
    };
    const attrs = {
        varianceEpsilon
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.FusedBatchNorm), inputs, attrs);
    return (0, _reshape.reshape)(res, $x.shape);
}
const batchNorm = /* @__PURE__ */ (0, _operation.op)({
    batchNorm_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./batchnorm_util":"bTZAD","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bTZAD":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "xAs4D", ()=>xAs4D);
var _reshape = require("./reshape");
function xAs4D(x) {
    let x4D;
    if (x.rank === 0 || x.rank === 1) x4D = (0, _reshape.reshape)(x, [
        1,
        1,
        1,
        x.size
    ]);
    else if (x.rank === 2) x4D = (0, _reshape.reshape)(x, [
        1,
        1,
        x.shape[0],
        x.shape[1]
    ]);
    else if (x.rank === 3) x4D = (0, _reshape.reshape)(x, [
        1,
        x.shape[0],
        x.shape[1],
        x.shape[2]
    ]);
    else x4D = x;
    return x4D;
}

},{"./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9ULx9":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchNorm2d", ()=>batchNorm2d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _batchnorm = require("./batchnorm");
var _operation = require("./operation");
/**
 * Batch normalization, strictly for 2D. For the more relaxed version, see
 * `tf.batchNorm`.
 *
 * @param x The input Tensor.
 * @param mean A mean Tensor.
 * @param variance A variance Tensor.
 * @param offset An offset Tensor.
 * @param scale A scale Tensor.
 * @param varianceEpsilon A small float number to avoid dividing by 0.
 */ function batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "batchNorm");
    const $mean = (0, _tensorUtilEnv.convertToTensor)(mean, "mean", "batchNorm");
    const $variance = (0, _tensorUtilEnv.convertToTensor)(variance, "variance", "batchNorm");
    let $scale;
    if (scale != null) $scale = (0, _tensorUtilEnv.convertToTensor)(scale, "scale", "batchNorm");
    let $offset;
    if (offset != null) $offset = (0, _tensorUtilEnv.convertToTensor)(offset, "offset", "batchNorm");
    _util.assert($x.rank === 2, ()=>`Error in batchNorm2D: x must be rank 2 but got rank ` + `${$x.rank}.`);
    _util.assert($mean.rank === 2 || $mean.rank === 1, ()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but ` + `got rank ${$mean.rank}.`);
    _util.assert($variance.rank === 2 || $variance.rank === 1, ()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 ` + `but got rank ${$variance.rank}.`);
    if ($scale != null) _util.assert($scale.rank === 2 || $scale.rank === 1, ()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 ` + `but got rank ${$scale.rank}.`);
    if ($offset != null) _util.assert($offset.rank === 2 || $offset.rank === 1, ()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 ` + `but got rank ${$offset.rank}.`);
    return (0, _batchnorm.batchNorm)($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
const batchNorm2d = /* @__PURE__ */ (0, _operation.op)({
    batchNorm2d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./batchnorm":"5eVFD","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"y3kug":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchNorm3d", ()=>batchNorm3d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _batchnorm = require("./batchnorm");
var _operation = require("./operation");
/**
 * Batch normalization, strictly for 3D. For the more relaxed version, see
 * `tf.batchNorm`.
 *
 * @param x The input Tensor.
 * @param mean A mean Tensor.
 * @param variance A variance Tensor.
 * @param offset An offset Tensor.
 * @param scale A scale Tensor.
 * @param varianceEpsilon A small float number to avoid dividing by 0.
 */ function batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "batchNorm");
    const $mean = (0, _tensorUtilEnv.convertToTensor)(mean, "mean", "batchNorm");
    const $variance = (0, _tensorUtilEnv.convertToTensor)(variance, "variance", "batchNorm");
    let $scale;
    if (scale != null) $scale = (0, _tensorUtilEnv.convertToTensor)(scale, "scale", "batchNorm");
    let $offset;
    if (offset != null) $offset = (0, _tensorUtilEnv.convertToTensor)(offset, "offset", "batchNorm");
    _util.assert($x.rank === 3, ()=>`Error in batchNorm3D: x must be rank 3 but got rank ` + `${$x.rank}.`);
    _util.assert($mean.rank === 3 || $mean.rank === 1, ()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but ` + `got rank ${$mean.rank}.`);
    _util.assert($variance.rank === 3 || $variance.rank === 1, ()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 ` + `but got rank ${$variance.rank}.`);
    if ($scale != null) _util.assert($scale.rank === 3 || $scale.rank === 1, ()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 ` + `but got rank ${$scale.rank}.`);
    if ($offset != null) _util.assert($offset.rank === 3 || $offset.rank === 1, ()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 ` + `but got rank ${$offset.rank}.`);
    return (0, _batchnorm.batchNorm)($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
const batchNorm3d = /* @__PURE__ */ (0, _operation.op)({
    batchNorm3d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./batchnorm":"5eVFD","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4l9oM":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchNorm4d", ()=>batchNorm4d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _batchnorm = require("./batchnorm");
var _operation = require("./operation");
/**
 * Batch normalization, strictly for 4D. For the more relaxed version, see
 * `tf.batchNorm`.
 *
 * @param x The input Tensor.
 * @param mean A mean Tensor.
 * @param variance A variance Tensor.
 * @param offset An offset Tensor.
 * @param scale A scale Tensor.
 * @param varianceEpsilon A small float number to avoid dividing by 0.
 */ function batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "batchNorm");
    const $mean = (0, _tensorUtilEnv.convertToTensor)(mean, "mean", "batchNorm");
    const $variance = (0, _tensorUtilEnv.convertToTensor)(variance, "variance", "batchNorm");
    let $scale;
    if (scale != null) $scale = (0, _tensorUtilEnv.convertToTensor)(scale, "scale", "batchNorm");
    let $offset;
    if (offset != null) $offset = (0, _tensorUtilEnv.convertToTensor)(offset, "offset", "batchNorm");
    _util.assert($x.rank === 4, ()=>`Error in batchNorm4D: x must be rank 4 but got rank ` + `${$x.rank}.`);
    _util.assert($mean.rank === 4 || $mean.rank === 1, ()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but ` + `got rank ${$mean.rank}.`);
    _util.assert($variance.rank === 4 || $variance.rank === 1, ()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 ` + `but got rank ${$variance.rank}.`);
    if ($scale != null) _util.assert($scale.rank === 4 || $scale.rank === 1, ()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 ` + `but got rank ${$scale.rank}.`);
    if ($offset != null) _util.assert($offset.rank === 4 || $offset.rank === 1, ()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 ` + `but got rank ${$offset.rank}.`);
    return (0, _batchnorm.batchNorm)($x, $mean, $variance, $offset, $scale, varianceEpsilon);
}
const batchNorm4d = /* @__PURE__ */ (0, _operation.op)({
    batchNorm4d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./batchnorm":"5eVFD","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gW00L":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "bincount", ()=>bincount);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Outputs a vector with length `size` and the same dtype as `weights`.
 *
 * If `weights` are empty, then index `i` stores the number of times the value
 * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the
 * sum of the value in `weights` at each index where the corresponding value in
 * `x` is `i`.
 *
 * Values in `x` outside of the range [0, size) are ignored.
 *
 * @param x The input int tensor, rank 1.
 * @param weights The weights tensor, must have the same shape as x, or a
 *     length-0 Tensor, in which case it acts as all weights equal to 1.
 * @param size Non-negative integer.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function bincount_(x, weights, size) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "bincount");
    const $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "bincount");
    _util.assert($x.dtype === "int32", ()=>`Error in bincount: input ` + `dtype must be int32, but got ${$x.dtype}`);
    _util.assert(size >= 0, ()=>`size must be non-negative, but got ${size}.`);
    _util.assert($weights.size === $x.size || $weights.size === 0, ()=>`Error in bincount: weights must have the same size as input or` + `0-length, but got input shape: ${$x.shape}, weights shape: ` + `${$weights.shape}.`);
    const inputs = {
        x: $x,
        weights: $weights
    };
    const attrs = {
        size
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Bincount), inputs, attrs);
}
const bincount = /* @__PURE__ */ (0, _operation.op)({
    bincount_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jmT4J":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "bitwiseAnd", ()=>bitwiseAnd);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _utilBase = require("../util_base");
var _operation = require("./operation");
/**
 * Bitwise `AND` operation for input tensors.
 *
 * Given two input tensors, returns a new tensor
 * with the `AND` calculated values.
 *
 * The method supports int32 values
 *
 *
 * ```js
 * const x = tf.tensor1d([0, 5, 3, 14], 'int32');
 * const y = tf.tensor1d([5, 0, 7, 11], 'int32');
 * tf.bitwiseAnd(x, y).print();
 * ```
 *
 * @param x The input tensor to be calculated.
 * @param y The input tensor to be calculated.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function bitwiseAnd_(x, y) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "bitwiseAnd");
    const $y = (0, _tensorUtilEnv.convertToTensor)(y, "y", "bitwiseAnd");
    if (!(0, _utilBase.arraysEqual)($x.shape, $y.shape)) throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${$x.shape}, y: ${$y.shape}`);
    if ($x.dtype !== "int32" || $y.dtype !== "int32") throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${$x.dtype} and type of y: ${$y.dtype}`);
    const inputs = {
        a: $x,
        b: $y
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.BitwiseAnd), inputs);
}
const bitwiseAnd = /* @__PURE__ */ (0, _operation.op)({
    bitwiseAnd_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util_base":"4Vo4a","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d9ZLh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "broadcastArgs", ()=>broadcastArgs);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Return the shape of s0 op s1 with broadcast.
 *
 * compute r0, the broadcasted shape as a tensor.
 * s0, s1 and r0 are all integer vectors.
 *
 * This function returns the shape of the result of an operation between
 * two tensors of size s0 and s1 performed with broadcast.
 *
 * @param s0 A tensor representing a shape
 * @param s1 A tensor representing a shape
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function broadcastArgs_(s0, s1) {
    const shape1Input = (0, _tensorUtilEnv.convertToTensor)(s0, "s0", "broadcastArgs", "int32");
    const shape2Input = (0, _tensorUtilEnv.convertToTensor)(s1, "s1", "broadcastArgs", "int32");
    if (shape1Input.rank !== 1) throw new Error("broadcastArgs(): first input must be a vector (rank=1). " + `Has rank ${shape1Input.rank}`);
    if (shape2Input.rank !== 1) throw new Error("broadcastArgs(): second input must be a vector (rank=1). " + `Has rank ${shape2Input.rank}`);
    const inputs = {
        s0: shape1Input,
        s1: shape2Input
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.BroadcastArgs), inputs);
}
const broadcastArgs = /* @__PURE__ */ (0, _operation.op)({
    broadcastArgs_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iMuoF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "broadcastTo", ()=>broadcastTo);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _utilBase = require("../util_base");
var _clone = require("./clone");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Broadcast an array to a compatible shape NumPy-style.
 *
 * The tensor's shape is compared to the broadcast shape from end to beginning.
 * Ones are prepended to the tensor's shape until it has the same length as
 * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is
 * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then
 * the input tensor is tiled N times along that axis (using tf.tile).
 *
 * @param input The tensor that is to be broadcasted.
 * @param shape The input is to be broadcast to this shape.
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function broadcastTo_(x, shape) {
    let input = (0, _tensorUtilEnv.convertToTensor)(x, "broadcastTo", "x");
    const xShape = input.shape;
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    if (shape.length < input.rank) throw new Error(`broadcastTo(): shape.length=${shape.length} < input.rank=${input.rank}.`);
    if (shape.length > input.rank) {
        const newShape = input.shape.slice();
        while(newShape.length < shape.length)newShape.unshift(1);
        input = (0, _reshape.reshape)(input, newShape);
    }
    const inputShape = input.shape;
    const reps = Array.from(shape);
    for(let i = shape.length - 1; i >= 0; i--){
        if (inputShape[i] === shape[i]) reps[i] = 1;
        else if (input.shape[i] !== 1) throw new Error(`broadcastTo(): [${xShape}] cannot be broadcast to [${shape}].`);
    }
    const axes = reps.map((n, i)=>n > 1 ? i : -1).filter((i)=>i >= 0);
    if (axes.length === 0) return (0, _clone.clone)(input);
    // TODO call broadcastTo kernel directly once backends implement broadcstTo
    const inputs = {
        x: input
    };
    const attrs = {
        reps
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Tile), inputs, attrs);
}
const broadcastTo = /* @__PURE__ */ (0, _operation.op)({
    broadcastTo_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util_base":"4Vo4a","./clone":"jmQ7S","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hVmez":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ceil", ()=>ceil);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`
 *
 * ```js
 * const x = tf.tensor1d([.6, 1.1, -3.3]);
 *
 * x.ceil().print();  // or tf.ceil(x)
 * ```
 * @param x The input Tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function ceil_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "ceil", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Ceil), inputs);
}
const ceil = /* @__PURE__ */ (0, _operation.op)({
    ceil_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cKbfV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "clipByValue", ()=>clipByValue);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _fill = require("./fill");
var _operation = require("./operation");
/**
 * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`
 *
 * ```js
 * const x = tf.tensor1d([-1, 2, -3, 4]);
 *
 * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
 * ```
 * @param x The input tensor.
 * @param clipValueMin Lower bound of range to be clipped to.
 * @param clipValueMax Upper bound of range to be clipped to.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function clipByValue_(x, clipValueMin, clipValueMax) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "clipByValue");
    _util.assert(clipValueMin <= clipValueMax, ()=>`Error in clip: min (${clipValueMin}) must be ` + `less than or equal to max (${clipValueMax}).`);
    if (clipValueMin === clipValueMax) return (0, _fill.fill)($x.shape, clipValueMin, $x.dtype);
    const inputs = {
        x: $x
    };
    const attrs = {
        clipValueMin,
        clipValueMax
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.ClipByValue), inputs, attrs);
}
const clipByValue = /* @__PURE__ */ (0, _operation.op)({
    clipByValue_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./fill":"i7goe","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"i7goe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fill", ()=>fill);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _util = require("../util");
var _utilBase = require("../util_base");
/**
 * Creates a `tf.Tensor` filled with a scalar value.
 *
 * ```js
 * tf.fill([2, 2], 4).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param value The scalar value to fill the tensor with.
 * @param dtype The type of an element in the resulting tensor. Defaults to
 *     'float32' if the given param value is a number, otherwise 'string'.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function fill(shape, value, dtype) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    dtype = dtype || (0, _util.inferDtype)(value);
    const attrs = {
        shape,
        value,
        dtype
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Fill), {}, attrs);
}

},{"../engine":"1g2qo","../kernel_names":"hniYO","../util":"ctavo","../util_base":"4Vo4a","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jI8Rd":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concat1d", ()=>concat1d);
var _concat = require("./concat");
var _operation = require("./operation");
/**
 * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.
 *
 * For example, if:
 * A: shape(3) = |r1, g1, b1|
 * B: shape(2) = |r2, g2|
 * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|
 *
 * @param tensors A list of`tf.Tensor`s to concatenate.
 * @return The concatenated array.
 */ function concat1d_(tensors) {
    return (0, _concat.concat)(tensors, 0 /* axis */ );
}
const concat1d = /* @__PURE__ */ (0, _operation.op)({
    concat1d_
});

},{"./concat":"1NOyL","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2ZT6j":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concat2d", ()=>concat2d);
var _concat = require("./concat");
var _operation = require("./operation");
/**
 * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.
 *
 * For example, if:
 * A: shape(2, 3) = | r1, g1, b1 |
 *                  | r2, g2, b2 |
 *
 * B: shape(2, 3) = | r3, g3, b3 |
 *                  | r4, g4, b4 |
 *
 * C = tf.concat2d([A, B], axis)
 *
 * if axis = 0:
 * C: shape(4, 3) = | r1, g1, b1 |
 *                  | r2, g2, b2 |
 *                  | r3, g3, b3 |
 *                  | r4, g4, b4 |
 *
 * if axis = 1:
 * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |
 *                   | r2, g2, b2, r4, g4, b4 |
 *
 *
 * @param tensors A list of `tf.Tensor`s to concatenate.
 * @param axis The axis to concatenate along.
 * @return The concatenated array.
 */ function concat2d_(tensors, axis) {
    return (0, _concat.concat)(tensors, axis);
}
const concat2d = /* @__PURE__ */ (0, _operation.op)({
    concat2d_
});

},{"./concat":"1NOyL","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g5T4x":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concat3d", ()=>concat3d);
var _concat = require("./concat");
var _operation = require("./operation");
/**
 * Concatenates a list of `tf.Tensor3D`s along an axis.
 * See `concat` for details.
 *
 * For example, if:
 * A: shape(2, 1, 3) = | r1, g1, b1 |
 *                     | r2, g2, b2 |
 *
 * B: shape(2, 1, 3) = | r3, g3, b3 |
 *                     | r4, g4, b4 |
 *
 * C = tf.concat3d([A, B], axis)
 *
 * if axis = 0:
 * C: shape(4, 1, 3) = | r1, g1, b1 |
 *                     | r2, g2, b2 |
 *                     | r3, g3, b3 |
 *                     | r4, g4, b4 |
 *
 * if axis = 1:
 * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |
 *                     | r2, g2, b2, r4, g4, b4 |
 *
 * if axis = 2:
 * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |
 *                      | r2, g2, b2, r4, g4, b4 |
 *
 * @param tensors A list of`tf.Tensor`s to concatenate.
 * @param axis The axis to concate along.
 * @return The concatenated array.
 */ function concat3d_(tensors, axis) {
    return (0, _concat.concat)(tensors, axis);
}
const concat3d = /* @__PURE__ */ (0, _operation.op)({
    concat3d_
});

},{"./concat":"1NOyL","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fsDG1":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concat4d", ()=>concat4d);
var _concat = require("./concat");
var _operation = require("./operation");
/**
 * Concatenates a list of `tf.Tensor4D`s along an axis.
 * See `concat` for details.
 *
 * @param tensors A list of `tf.Tensor`s to concatenate.
 * @param axis The axis to concate along.
 * @return The concatenated array.
 */ function concat4d_(tensors, axis) {
    return (0, _concat.concat)(tensors, axis);
}
const concat4d = /* @__PURE__ */ (0, _operation.op)({
    concat4d_
});

},{"./concat":"1NOyL","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2LKBX":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv1d", ()=>conv1d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _conv2D = require("./conv2d");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes a 1D convolution over the input x.
 *
 * @param x The input tensor, of rank 3 or rank 2, of shape
 *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.
 * @param filter The filter, rank 3, of shape
 *     `[filterWidth, inDepth, outDepth]`.
 * @param stride The number of entries by which the filter is moved right at
 *     each step.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC",
 *     the data is stored in the order of [batch, in_width, in_channels]. Only
 *     "NWC" is currently supported.
 * @param dilation The dilation rate in which we sample input values in
 *     atrous convolution. Defaults to `1`. If it is greater than 1, then
 *     stride must be `1`.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function conv1d_(x, filter, stride, pad, dataFormat = "NWC", dilation = 1, dimRoundingMode) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "conv1d");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "conv1d");
    let x3D = $x;
    let reshapedTo3D = false;
    if ($x.rank === 2) {
        reshapedTo3D = true;
        x3D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1]
        ]);
    }
    _util.assert(x3D.rank === 3, ()=>`Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
    _util.assert($filter.rank === 3, ()=>`Error in conv1d: filter must be rank 3, but got rank ` + `${$filter.rank}.`);
    _convUtil.checkPadOnDimRoundingMode("conv1d", pad, dimRoundingMode);
    _util.assert(x3D.shape[2] === $filter.shape[1], ()=>`Error in conv1d: depth of input (${x3D.shape[2]}) must match ` + `input depth for filter ${$filter.shape[1]}.`);
    _util.assert(_convUtil.eitherStridesOrDilationsAreOne(stride, dilation), ()=>"Error in conv1D: Either stride or dilation must be 1. " + `Got stride ${stride} and dilation '${dilation}'`);
    _util.assert(_convUtil.stridesOrDilationsArePositive(dilation), ()=>"Error in conv1D: Dilated rates should be larger than 0.");
    _util.assert(_convUtil.stridesOrDilationsArePositive(stride), ()=>"Error in conv1D: Stride should be larger than 0.");
    _util.assert(dataFormat === "NWC", ()=>`Error in conv1d: got dataFormat of ${dataFormat} but only NWC is currently supported.`);
    const filter4D = (0, _reshape.reshape)($filter, [
        1,
        $filter.shape[0],
        $filter.shape[1],
        $filter.shape[2]
    ]);
    const input4D = (0, _reshape.reshape)(x3D, [
        x3D.shape[0],
        1,
        x3D.shape[1],
        x3D.shape[2]
    ]);
    const strides = [
        1,
        stride
    ];
    const dilations = [
        1,
        dilation
    ];
    const conv2dDataFormat = "NHWC";
    const res = (0, _conv2D.conv2d)(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);
    if (reshapedTo3D) return (0, _reshape.reshape)(res, [
        res.shape[2],
        res.shape[3]
    ]);
    return (0, _reshape.reshape)(res, [
        res.shape[0],
        res.shape[2],
        res.shape[3]
    ]);
}
const conv1d = /* @__PURE__ */ (0, _operation.op)({
    conv1d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./conv2d":"lmJyO","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lmJyO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2d", ()=>conv2d);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes a 2D convolution over the input x.
 *
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter, rank 4, of shape
 *     `[filterHeight, filterWidth, inDepth, outDepth]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels].
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function conv2d_(x, filter, strides, pad, dataFormat = "NHWC", dilations = [
    1,
    1
], dimRoundingMode) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "conv2d", "float32");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "conv2d", "float32");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    _util.assert(x4D.rank === 4, ()=>`Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);
    _util.assert($filter.rank === 4, ()=>`Error in conv2d: filter must be rank 4, but got rank ` + `${$filter.rank}.`);
    _convUtil.checkPadOnDimRoundingMode("conv2d", pad, dimRoundingMode);
    const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
    _util.assert(inDepth === $filter.shape[2], ()=>`Error in conv2d: depth of input (${inDepth}) must match ` + `input depth for filter ${$filter.shape[2]}.`);
    _util.assert(_convUtil.eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in conv2D: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    _util.assert(_convUtil.stridesOrDilationsArePositive(dilations), ()=>"Error in conv2D: Dilated rates should be larger than 0.");
    _util.assert(_convUtil.stridesOrDilationsArePositive(strides), ()=>"Error in conv2D: Strides should be larger than 0.");
    const inputs = {
        x: x4D,
        filter: $filter
    };
    const attrs = {
        strides,
        pad,
        dataFormat,
        dilations,
        dimRoundingMode
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.Conv2D), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const conv2d = /* @__PURE__ */ (0, _operation.op)({
    conv2d_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kCheD":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2dTranspose", ()=>conv2dTranspose);
var _tensorUtilEnv = require("../tensor_util_env");
var _conv2DBackpropInput = require("./conv2d_backprop_input");
var _operation = require("./operation");
/**
 * Computes the transposed 2D convolution of an image, also known as a
 * deconvolution.
 *
 * @param x The input image, of rank 4 or rank 3, of shape
 *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.
 * @param filter The filter, rank 4, of shape
 *     `[filterHeight, filterWidth, outDepth, inDepth]`.
 *     `inDepth` must match `inDepth` in `x`.
 * @param outputShape Output shape, of rank 4 or rank 3:
 *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.
 * @param strides The strides of the original convolution:
 *     `[strideHeight, strideWidth]`.
 * @param pad  The type of padding algorithm used in the non-transpose version
 *    of the op.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "conv2dTranspose");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "conv2dTranspose");
    return (0, _conv2DBackpropInput.conv2DBackpropInput)(outputShape, $x, $filter, strides, pad, "NHWC", dimRoundingMode);
}
const conv2dTranspose = /* @__PURE__ */ (0, _operation.op)({
    conv2dTranspose_
});

},{"../tensor_util_env":"6vfHI","./conv2d_backprop_input":"l0Ec6","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l0Ec6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2DBackpropInput", ()=>conv2DBackpropInput);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _util = require("../util");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the derivative of the input of a 2D convolution.
 *
 * @param xShape The shape of the input: [batch, height, width, inDepth].
 * If length of 3, batch of 1 is assumed.
 * @param dy The derivative of the output, of rank 4 or rank 3 of shape
 *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter, rank 4, of shape
 *     `[filterHeight, filterWidth, inDepth, outDepth]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`.
 * @param pad The type of padding algorithm used:
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels].
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 */ function conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = "NHWC", dimRoundingMode) {
    _util.assert(xShape.length === dy.rank, ()=>`Length of inShape ` + `(${xShape.length}) and rank of dy (${dy.rank}) must match`);
    let xShape4D = xShape;
    let dy4D = dy;
    let reshapedTo4D = false;
    if (dy.rank === 3) {
        reshapedTo4D = true;
        dy4D = (0, _reshape.reshape)(dy, [
            1,
            dy.shape[0],
            dy.shape[1],
            dy.shape[2]
        ]);
        xShape4D = [
            1,
            xShape[0],
            xShape[1],
            xShape[2]
        ];
    }
    _util.assert(xShape4D.length === 4, ()=>`Error in conv2dDerInput: inShape must be length 4, but got length ` + `${xShape4D.length}.`);
    _util.assert(dy4D.rank === 4, ()=>`Error in conv2dDerInput: dy must be rank 4, but got ` + `rank ${dy4D.rank}`);
    _util.assert(filter.rank === 4, ()=>`Error in conv2dDerInput: filter must be rank 4, but got ` + `rank ${filter.rank}`);
    const inDepth = dataFormat === "NHWC" ? xShape4D[3] : xShape4D[1];
    const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
    _util.assert(inDepth === filter.shape[2], ()=>`Error in conv2dDerInput: depth of input (${inDepth}) must ` + `match input depth for filter ${filter.shape[2]}.`);
    _util.assert(outDepth === filter.shape[3], ()=>`Error in conv2dDerInput: depth of output (${outDepth}) must ` + `match output depth for filter ${filter.shape[3]}.`);
    _convUtil.checkPadOnDimRoundingMode("conv2dDerInput", pad, dimRoundingMode);
    const inputs = {
        dy: dy4D,
        filter
    };
    const attrs = {
        strides,
        pad,
        dataFormat,
        dimRoundingMode,
        inputShape: xShape4D
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.Conv2DBackpropInput), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const conv2DBackpropInput = /* @__PURE__ */ (0, _operation.op)({
    conv2DBackpropInput_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../util":"ctavo","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3stvE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3d", ()=>conv3d);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes a 3D convolution over the input x.
 *
 * @param x The input tensor, of rank 5 or rank 4, of shape
 *     `[batch, depth, height, width, channels]`. If rank 4,
 * batch of 1 is assumed.
 * @param filter The filter, rank 5, of shape
 *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.
 *      inChannels must match between input and filter.
 * @param strides The strides of the convolution: `[strideDepth, strideHeight,
 * strideWidth]`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to
 *     "NDHWC". Specify the data format of the input and output data. With the
 *     default format "NDHWC", the data is stored in the order of: [batch,
 *     depth, height, width, channels]. Only "NDHWC" is currently supported.
 * @param dilations The dilation rates: `[dilationDepth, dilationHeight,
 *     dilationWidth]` in which we sample input values across the height
 *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.
 *     If `dilations` is a single number, then
 *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater
 *     than 1, then all values of `strides` must be 1.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function conv3d_(x, filter, strides, pad, dataFormat = "NDHWC", dilations = [
    1,
    1,
    1
]) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "conv3d");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "conv3d");
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
        reshapedTo5D = true;
        x5D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2],
            $x.shape[3]
        ]);
    }
    _util.assert(x5D.rank === 5, ()=>`Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);
    _util.assert($filter.rank === 5, ()=>`Error in conv3d: filter must be rank 5, but got rank ` + `${$filter.rank}.`);
    _util.assert(x5D.shape[4] === $filter.shape[3], ()=>`Error in conv3d: depth of input (${x5D.shape[4]}) must match ` + `input depth for filter ${$filter.shape[3]}.`);
    _util.assert((0, _convUtil.eitherStridesOrDilationsAreOne)(strides, dilations), ()=>"Error in conv3D: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    _util.assert(dataFormat === "NDHWC", ()=>`Error in conv3d: got dataFormat of ${dataFormat} but only NDHWC is currently supported.`);
    _util.assert((0, _convUtil.stridesOrDilationsArePositive)(dilations), ()=>"Error in conv3D: Dilated rates should be larger than 0.");
    _util.assert((0, _convUtil.stridesOrDilationsArePositive)(strides), ()=>"Error in conv3D: Strides should be larger than 0.");
    const inputs = {
        x: x5D,
        filter: $filter
    };
    const attrs = {
        strides,
        pad,
        dataFormat,
        dilations
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.Conv3D), inputs, attrs);
    if (reshapedTo5D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3],
        res.shape[4]
    ]);
    return res;
}
const conv3d = /* @__PURE__ */ (0, _operation.op)({
    conv3d_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4J2I2":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3dTranspose", ()=>conv3dTranspose);
var _tensorUtilEnv = require("../tensor_util_env");
var _conv3DBackpropInput = require("./conv3d_backprop_input");
var _operation = require("./operation");
/**
 * Computes the transposed 3D convolution of a volume, also known as a
 * deconvolution.
 *
 * @param x The input image, of rank 5 or rank 4, of shape
 *   `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.
 * @param filter The filter, rank 4, of shape
 *     `[depth, filterHeight, filterWidth, outDepth, inDepth]`.
 *     `inDepth` must match `inDepth` in `x`.
 * @param outputShape Output shape, of rank 5 or rank 4:
 *     `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is
 *    assumed.
 * @param strides The strides of the original convolution:
 *     `[strideDepth, strideHeight, strideWidth]`.
 * @param pad  The type of padding algorithm used in the non-transpose version
 *    of the op.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function conv3dTranspose_(x, filter, outputShape, strides, pad) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "conv3dTranspose");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "conv3dTranspose");
    return (0, _conv3DBackpropInput.conv3DBackpropInput)(outputShape, $x, $filter, strides, pad);
}
const conv3dTranspose = /* @__PURE__ */ (0, _operation.op)({
    conv3dTranspose_
});

},{"../tensor_util_env":"6vfHI","./conv3d_backprop_input":"fyxX7","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fyxX7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3DBackpropInput", ()=>conv3DBackpropInput);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _util = require("../util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the derivative of the input of a 3D convolution.
 *
 * @param xShape The shape of the input: [batch, depth, height, width,
 * in_channels]. If length of 4, batch of 1 is assumed.
 * @param dy The derivative of the output, of rank 5 or rank 4 of shape
 *   `[batch, outDepth, outHeight, outWidth, in_channels]`.
 * If rank 4, batch of 1 is assumed.
 * @param filter The filter, rank 5, of shape
 *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.
 * @param strides The strides of the convolution: `[strideDepth, strideHeight,
 * strideWidth]`.
 * @param pad The type of padding algorithm used:
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 */ function conv3DBackpropInput_(xShape, dy, filter, strides, pad) {
    _util.assert(xShape.length === dy.rank, ()=>`Length of inShape ` + `(${xShape.length}) and rank of dy (${dy.rank}) must match`);
    let xShape5D = xShape;
    let dy5D = dy;
    let reshapedTo5D = false;
    if (dy.rank === 4) {
        reshapedTo5D = true;
        dy5D = (0, _reshape.reshape)(dy, [
            1,
            dy.shape[0],
            dy.shape[1],
            dy.shape[2],
            dy.shape[3]
        ]);
        xShape5D = [
            1,
            xShape[0],
            xShape[1],
            xShape[2],
            xShape[3]
        ];
    }
    const inDepth = xShape5D[4];
    const outDepth = dy5D.shape[4];
    _util.assert(xShape5D.length === 5, ()=>`Error in conv3dDerInput: inShape must be length 5, but got length ` + `${xShape5D.length}.`);
    _util.assert(dy5D.rank === 5, ()=>`Error in conv3dDerInput: dy must be rank 5, but got ` + `rank ${dy5D.rank}`);
    _util.assert(filter.rank === 5, ()=>`Error in conv3dDerInput: filter must be rank 5, but got ` + `rank ${filter.rank}`);
    _util.assert(inDepth === filter.shape[3], ()=>`Error in conv3dDerInput: depth of input (${inDepth}) must ` + `match input depth for filter ${filter.shape[3]}.`);
    _util.assert(outDepth === filter.shape[4], ()=>`Error in conv3dDerInput: depth of output (${outDepth}) must ` + `match output depth for filter ${filter.shape[4]}.`);
    const inputs = {
        dy: dy5D,
        filter
    };
    const attrs = {
        pad,
        strides,
        inputShape: xShape5D
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.Conv3DBackpropInputV2), inputs, attrs);
    if (reshapedTo5D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3],
        res.shape[4]
    ]);
    return res;
}
const conv3DBackpropInput = /* @__PURE__ */ (0, _operation.op)({
    conv3DBackpropInput_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../util":"ctavo","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aBory":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cos", ()=>cos);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
 *
 * x.cos().print();  // or tf.cos(x)
 * ```
 * @param x The input tensor. Must be float32 type.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function cos_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "cos", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Cos), inputs);
}
const cos = /* @__PURE__ */ (0, _operation.op)({
    cos_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kFKmS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cosh", ()=>cosh);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.cosh().print();  // or tf.cosh(x)
 * ```
 * @param x The input tensor. Must be float32 type.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function cosh_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "cosh", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Cosh), inputs);
}
const cosh = /* @__PURE__ */ (0, _operation.op)({
    cosh_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gorKt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the 'License');
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an 'AS IS' BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cumprod", ()=>cumprod);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the cumulative product of a `tf.Tensor` along `axis`.
 *
 * ```js
 * const x = tf.tensor([1, 2, 3, 4]);
 * x.cumprod().print();
 * ```
 * ```js
 * const x = tf.tensor([[1, 2], [3, 4]]);
 * x.cumprod().print();
 * ```
 *
 * @param x The input tensor to cumulatively multiply.
 * @param axis The axis along which to multiply. Optional. Defaults to 0.
 * @param exclusive Whether to perform exclusive cumulative product. Optional.
 *     Defaults to false. If set to true then the product of each tensor entry
 *     does not include its own value, but only the values previous to it
 *     along the specified axis.
 * @param reverse Whether to multiply in the opposite direction. Optional.
 *     Defaults to false.
 *
 * @doc {heading: 'Operations', subheading: 'Scan'}
 */ function cumprod_(x, axis = 0, exclusive = false, reverse = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "cumprod");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        exclusive,
        reverse
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Cumprod), inputs, attrs);
}
const cumprod = /* @__PURE__ */ (0, _operation.op)({
    cumprod_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8KDcI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cumsum", ()=>cumsum);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the cumulative sum of a `tf.Tensor` along `axis`.
 *
 * ```js
 * const x = tf.tensor([1, 2, 3, 4]);
 * x.cumsum().print();
 * ```
 * ```js
 * const x = tf.tensor([[1, 2], [3, 4]]);
 * x.cumsum().print();
 * ```
 *
 * @param x The input tensor to be summed.
 * @param axis The axis along which to sum. Optional. Defaults to 0.
 * @param exclusive Whether to perform exclusive cumulative sum. Optional.
 *     Defaults to false. If set to true then the sum of each tensor entry
 *     does not include its own value, but only the values previous to it
 *     along the specified axis.
 * @param reverse Whether to sum in the opposite direction. Optional.
 *     Defaults to false.
 *
 * @doc {heading: 'Operations', subheading: 'Scan'}
 */ function cumsum_(x, axis = 0, exclusive = false, reverse = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "cumsum");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        exclusive,
        reverse
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Cumsum), inputs, attrs);
}
const cumsum = /* @__PURE__ */ (0, _operation.op)({
    cumsum_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3PEDs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "denseBincount", ()=>denseBincount);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Outputs a vector with length `size` and the same dtype as `weights`.
 *
 * If `weights` are empty, then index `i` stores the number of times the value
 * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the
 * sum of the value in `weights` at each index where the corresponding value in
 * `x` is `i`.
 *
 * Values in `x` outside of the range [0, size) are ignored.
 *
 * @param x The input int tensor, rank 1 or rank 2.
 * @param weights The weights tensor, must have the same shape as x, or a
 *     length-0 Tensor, in which case it acts as all weights equal to 1.
 * @param size Non-negative integer.
 * @param binaryOutput Optional. Whether the kernel should count the appearance
 *     or number of occurrences. Defaults to False.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function denseBincount_(x, weights, size, binaryOutput = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "denseBincount");
    const $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "denseBincount");
    _util.assert($x.dtype === "int32", ()=>`Error in denseBincount: input ` + `dtype must be int32, but got ${$x.dtype}`);
    _util.assert($x.rank <= 2, ()=>`Error in denseBincount: input must be at most rank 2, but got ` + `rank ${$x.rank}.`);
    _util.assert(size >= 0, ()=>`size must be non-negative, but got ${size}.`);
    _util.assert($weights.size === $x.size || $weights.size === 0, ()=>`Error in denseBincount: weights must have the same shape as x or ` + `0-length, but got x shape: ${$x.shape}, weights shape: ` + `${$weights.shape}.`);
    const inputs = {
        x: $x,
        weights: $weights
    };
    const attrs = {
        size,
        binaryOutput
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.DenseBincount), inputs, attrs);
}
const denseBincount = /* @__PURE__ */ (0, _operation.op)({
    denseBincount_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ipPbw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthToSpace", ()=>depthToSpace);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Rearranges data from depth into blocks of spatial data. More specifically,
 * this op outputs a copy of the input tensor where values from the `depth`
 * dimension are moved in spatial blocks to the `height` and `width` dimensions.
 * The attr `blockSize` indicates the input block size and how the data is
 * moved.
 *
 *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged
 * into non-overlapping blocks of size `blockSize x blockSize`
 *
 *  - The width the output tensor is `inputWidth * blockSize`, whereas the
 * height is `inputHeight * blockSize`
 *
 *  - The Y, X coordinates within each block of the output image are determined
 * by the high order component of the input channel index
 *
 *  - The depth of the input tensor must be divisible by `blockSize *
 * blockSize`
 *
 * The `dataFormat` attr specifies the layout of the input and output tensors
 * with the following options: "NHWC": [ `batch, height, width, channels` ]
 * "NCHW": [ `batch, channels, height, width` ]
 *
 * ```js
 * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
 * const blockSize = 2;
 * const dataFormat = "NHWC";
 *
 * tf.depthToSpace(x, blockSize, dataFormat).print();
 * ```
 *
 * @param x The input tensor of rank 4
 * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block
 * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to "NHWC"
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "depthToSpace", "float32");
    const inputHeight = dataFormat === "NHWC" ? $x.shape[1] : $x.shape[2];
    const inputWidth = dataFormat === "NHWC" ? $x.shape[2] : $x.shape[3];
    const inputDepth = dataFormat === "NHWC" ? $x.shape[3] : $x.shape[1];
    _util.assert(blockSize > 1, ()=>`blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
    _util.assert(inputHeight * blockSize >= 0, ()=>`Negative dimension size caused by overflow when multiplying
    ${inputHeight} and ${blockSize}  for depthToSpace with input shape
    ${$x.shape}`);
    _util.assert(inputWidth * blockSize >= 0, ()=>`Negative dimension size caused by overflow when multiplying
    ${inputWidth} and ${blockSize} for depthToSpace with input shape
        ${$x.shape}`);
    _util.assert(inputDepth % (blockSize * blockSize) === 0, ()=>`Dimension size must be evenly divisible by ${blockSize * blockSize} but is ${inputDepth} for depthToSpace with input shape ${$x.shape}`);
    const inputs = {
        x: $x
    };
    const attrs = {
        blockSize,
        dataFormat
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.DepthToSpace), inputs, attrs);
}
const depthToSpace = /* @__PURE__ */ (0, _operation.op)({
    depthToSpace_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cZHpb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2d", ()=>depthwiseConv2d);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Depthwise 2D convolution.
 *
 * Given a 4D `input` array and a `filter` array of shape
 * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
 * `inChannels` convolutional filters of depth 1, this op applies a
 * different filter to each input channel (expanding from 1 channel to
 * `channelMultiplier` channels for each), then concatenates the results
 * together. The output has `inChannels * channelMultiplier` channels.
 *
 * See
 * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
 *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
 * for more details.
 *
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter tensor, rank 4, of shape
 *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`. If strides is a single number, then `strideHeight ==
 * strideWidth`.
 * @param pad The type of padding algorithm.
 *   - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *   - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels]. Only "NHWC" is currently supported.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function depthwiseConv2d_(x, filter, strides, pad, dataFormat = "NHWC", dilations = [
    1,
    1
], dimRoundingMode) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "depthwiseConv2d", "float32");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "depthwiseConv2d", "float32");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    _util.assert(x4D.rank === 4, ()=>`Error in depthwiseConv2d: input must be rank 4, but got ` + `rank ${x4D.rank}.`);
    _util.assert($filter.rank === 4, ()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ` + `${$filter.rank}.`);
    const inChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
    _util.assert(inChannels === $filter.shape[2], ()=>`Error in depthwiseConv2d: number of input channels ` + `(${inChannels}) must match the inChannels dimension in ` + `filter ${$filter.shape[2]}.`);
    _convUtil.checkPadOnDimRoundingMode("depthwiseConv2d", pad, dimRoundingMode);
    const inputs = {
        x: x4D,
        filter: $filter
    };
    const attrs = {
        strides,
        pad,
        dataFormat,
        dilations,
        dimRoundingMode
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.DepthwiseConv2dNative), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const depthwiseConv2d = /* @__PURE__ */ (0, _operation.op)({
    depthwiseConv2d_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3orNc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "diag", ()=>diag);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns a diagonal tensor with given diagonal values.
 *
 * Given a diagonal, this operation returns a tensor with the diagonal and
 * everything else padded with zeros.
 *
 * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor
 * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 *
 * tf.diag(x).print()
 * ```
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [4, 2])
 *
 * tf.diag(x).print()
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function diag_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "diag");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Diag), inputs);
}
const diag = /* @__PURE__ */ (0, _operation.op)({
    diag_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7hUfK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "dilation2d", ()=>dilation2d);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the grayscale dilation over the input `x`.
 *
 * @param x The input tensor, rank 3 or rank 4 of shape
 *     `[batch, height, width, depth]`. If rank 3, batch of 1 is assumed.
 * @param filter The filter tensor, rank 3, of shape
 *     `[filterHeight, filterWidth, depth]`.
 * @param strides The strides of the sliding window for each dimension of the
 *     input tensor: `[strideHeight, strideWidth]`.
 *     If `strides` is a single number,
 *     then `strideHeight == strideWidth`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1*1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat Specify the data format of the input and output data.
 *      Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the
 *      default format "NHWC", the data is stored in the order of: [batch,
 *      height, width, channels].
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`
 *     is a single number, then `dilationHeight == dilationWidth`. If it is
 *     greater than 1, then all values of `strides` must be 1.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function dilation2d_(x, filter, strides, pad, dilations = [
    1,
    1
], dataFormat = "NHWC") {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "dilation2d");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "dilation2d");
    _util.assert($x.rank === 3 || $x.rank === 4, ()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ` + `${$x.rank}.`);
    _util.assert($filter.rank === 3, ()=>`Error in dilation2d: filter must be rank 3, but got rank ` + `${$filter.rank}.`);
    _util.assert(dataFormat === "NHWC", ()=>`Error in dilation2d: Only NHWC is currently supported, ` + `but got dataFormat of ${dataFormat}`);
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
        reshapedTo4D = true;
    }
    _util.assert(x4D.shape[3] === $filter.shape[2], ()=>`Error in dilation2d:  input and filter must have the same depth: ${x4D.shape[3]} vs ${$filter.shape[2]}`);
    const inputs = {
        x: x4D,
        filter: $filter
    };
    const attrs = {
        strides,
        pad,
        dilations
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.Dilation2D), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const dilation2d = /* @__PURE__ */ (0, _operation.op)({
    dilation2d_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aw8lb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "divNoNan", ()=>divNoNan);
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _div = require("./div");
var _equal = require("./equal");
var _operation = require("./operation");
var _where = require("./where");
var _zerosLike = require("./zeros_like");
/**
 * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
 * if denominator is 0.
 *
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 9, 16]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 * const c = tf.tensor1d([0, 0, 0, 0]);
 *
 * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
 * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
 * ```
 *
 * ```js
 * // Broadcast div a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(2);
 * const c = tf.scalar(0);
 *
 * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
 * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
 * ```
 *
 * @param a The first tensor as the numerator.
 * @param b The second tensor as the denominator. Must have the same dtype as
 * `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function divNoNan_(a, b) {
    // TODO: Make this into its own kernel.
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "div");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "div");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const divResult = (0, _div.div)($a, $b);
    const zeros = (0, _zerosLike.zerosLike)(divResult);
    const bEqualsZero = (0, _equal.equal)($b, zeros);
    return (0, _where.where)(bEqualsZero, zeros, divResult);
}
const divNoNan = /* @__PURE__ */ (0, _operation.op)({
    divNoNan_
});

},{"../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./div":"5ZT1h","./equal":"htEqc","./operation":"ip64W","./where":"47cKc","./zeros_like":"1Xjct","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"htEqc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "equal", ()=>equal);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of (a == b) element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 * const b = tf.tensor1d([2, 2, 2]);
 *
 * a.equal(b).print();
 * ```
 *
 * @param a The first input tensor.
 * @param b The second input tensor. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function equal_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "equal", "string_or_numeric");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "equal", "string_or_numeric");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Equal), inputs);
}
const equal = /* @__PURE__ */ (0, _operation.op)({
    equal_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eLo4J":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Returns the dimensions in the input shape that are broadcasted to
 * produce the provided output shape.
 *
 * The returned dimensions are 0-indexed and sorted. An example:
 * inShape = [4, 1, 3]
 * outShape = [5, 4, 3, 3]
 * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getBroadcastDims", ()=>getBroadcastDims);
/**
 * Returns the axes in the output space that should be reduced to produce
 * the input space.
 */ parcelHelpers.export(exports, "getReductionAxes", ()=>getReductionAxes);
parcelHelpers.export(exports, "assertAndGetBroadcastShape", ()=>assertAndGetBroadcastShape);
function getBroadcastDims(inShape, outShape) {
    const inRank = inShape.length;
    const dims = [];
    for(let i = 0; i < inRank; i++){
        const dim = inRank - 1 - i;
        const a = inShape[dim] || 1;
        const b = outShape[outShape.length - 1 - i] || 1;
        if (b > 1 && a === 1) dims.unshift(dim);
    }
    return dims;
}
function getReductionAxes(inShape, outShape) {
    const result = [];
    for(let i = 0; i < outShape.length; i++){
        const inDim = inShape[inShape.length - i - 1];
        const outAxis = outShape.length - i - 1;
        const outDim = outShape[outAxis];
        if (inDim == null || inDim === 1 && outDim > 1) result.unshift(outAxis);
    }
    return result;
}
function assertAndGetBroadcastShape(shapeA, shapeB) {
    const l = Math.max(shapeA.length, shapeB.length);
    const result = new Array(l);
    for(let i = 0; i < l; i++){
        let a = shapeA[shapeA.length - i - 1];
        if (a == null) a = 1;
        let b = shapeB[shapeB.length - i - 1];
        if (b == null) b = 1;
        if (a === 1) result[l - i - 1] = b;
        else if (b === 1) result[l - i - 1] = a;
        else if (a !== b) {
            const errMsg = `Operands could not be broadcast together with shapes ` + `${shapeA} and ${shapeB}.`;
            throw Error(errMsg);
        } else result[l - i - 1] = a;
    }
    return result;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"47cKc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "where", ()=>where);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastTo = require("./broadcast_to");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the elements, either `a` or `b` depending on the `condition`.
 *
 * If the condition is true, select from `a`, otherwise select from `b`.
 *
 * ```js
 * const cond = tf.tensor1d([false, false, true], 'bool');
 * const a = tf.tensor1d([1 , 2, 3]);
 * const b = tf.tensor1d([-1, -2, -3]);
 *
 * a.where(cond, b).print();
 * ```
 *
 * @param condition The input condition. Must be of dtype bool.
 * @param a If `condition` is rank 1, `a` may have a higher rank but
 *     its first dimension must match the size of `condition`.
 * @param b A tensor with the same dtype as `a` and with shape that is
 *     compatible with `a`.
 * @return A tensor with same dtype as `a` and `b`, and shape that is
 *     broadcastable from `a` and `b`.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function where_(condition, a, b) {
    const $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "where");
    const $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "where");
    const $condition = (0, _tensorUtilEnv.convertToTensor)(condition, "condition", "where", "bool");
    // TODO: move this logic to forward function when the broadcastTo op is
    // implemented in WASM.
    // Find the broadcastable shape for $condition, $a, and $b.
    const broadcastShape = (0, _broadcastUtil.assertAndGetBroadcastShape)((0, _broadcastUtil.assertAndGetBroadcastShape)($condition.shape, $a.shape), $b.shape);
    const $broadcastedCondition = (0, _broadcastTo.broadcastTo)($condition, broadcastShape);
    const $broadcastedA = (0, _broadcastTo.broadcastTo)($a, broadcastShape);
    const $broadcastedB = (0, _broadcastTo.broadcastTo)($b, broadcastShape);
    const inputs = {
        condition: $broadcastedCondition,
        t: $broadcastedA,
        e: $broadcastedB
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Select), inputs);
}
const where = /* @__PURE__ */ (0, _operation.op)({
    where_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./broadcast_to":"iMuoF","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1Xjct":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "zerosLike", ()=>zerosLike);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the
 * given tensor.
 *
 * ```js
 * const x = tf.tensor([1, 2]);
 * tf.zerosLike(x).print();
 * ```
 *
 * @param x The tensor of required shape.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function zerosLike_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "zerosLike");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.ZerosLike), inputs);
}
const zerosLike = /* @__PURE__ */ (0, _operation.op)({
    zerosLike_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b1M9k":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "dot", ()=>dot);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _matMul = require("./mat_mul");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 * const b = tf.tensor2d([[1, 2], [3, 4]]);
 * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
 *
 * a.dot(b).print();  // or tf.dot(a, b)
 * b.dot(a).print();
 * b.dot(c).print();
 * ```
 * @param t1 The first tensor in the dot operation.
 * @param t2 The second tensor in the dot operation.
 *
 * @doc {heading: 'Operations', subheading: 'Matrices'}
 */ function dot_(t1, t2) {
    const $t1 = (0, _tensorUtilEnv.convertToTensor)(t1, "t1", "dot");
    const $t2 = (0, _tensorUtilEnv.convertToTensor)(t2, "t2", "dot");
    _util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), ()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ` + `${$t1.rank} and ${$t2.rank}.`);
    const t1Inner = $t1.rank === 1 ? $t1.size : $t1.shape[1];
    const t2Inner = $t2.rank === 1 ? $t2.size : $t2.shape[0];
    _util.assert(t1Inner === t2Inner, ()=>`Error in dot: inner dimensions of inputs must match, but got ` + `${t1Inner} and ${t2Inner}.`);
    if ($t1.rank === 1 && $t2.rank === 1) {
        const t12D = (0, _reshape.reshape)($t1, [
            1,
            -1
        ]);
        const t22D = (0, _reshape.reshape)($t2, [
            -1,
            1
        ]);
        const t1t2 = (0, _matMul.matMul)(t12D, t22D);
        return (0, _reshape.reshape)(t1t2, []);
    } else if ($t1.rank === 1 && $t2.rank === 2) {
        const t12D = (0, _reshape.reshape)($t1, [
            1,
            -1
        ]);
        const t22D = (0, _reshape.reshape)($t2, [
            $t2.shape[0],
            $t2.shape[1]
        ]);
        const t1t2 = (0, _matMul.matMul)(t12D, t22D);
        return (0, _reshape.reshape)(t1t2, [
            t1t2.size
        ]);
    } else if ($t1.rank === 2 && $t2.rank === 1) {
        const t22D = (0, _reshape.reshape)($t2, [
            -1,
            1
        ]);
        const t1t2 = (0, _matMul.matMul)($t1, t22D);
        return (0, _reshape.reshape)(t1t2, [
            t1t2.size
        ]);
    } else {
        const t22D = (0, _reshape.reshape)($t2, [
            $t2.shape[0],
            $t2.shape[1]
        ]);
        const t1t2 = (0, _matMul.matMul)($t1, t22D);
        return t1t2;
    }
}
const dot = /* @__PURE__ */ (0, _operation.op)({
    dot_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./mat_mul":"fiMp4","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"adKPG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Tensor contraction over specified indices and outer product.
 *
 * `einsum` allows defining Tensors by defining their element-wise computation.
 * This computation is based on
 * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation).
 *
 * Some special cases include:
 *
 * Matrix multiplication:
 * ```js
 * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
 * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]);
 * x.print();
 * y.print();
 * tf.einsum('ij,jk->ik', x, y).print();
 * ```
 *
 * Dot product:
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 * const y = tf.tensor1d([0, 1, 2]);
 * x.print();
 * y.print();
 * tf.einsum('i,i->', x, y).print();
 * ```
 *
 * Batch dot product:
 * ```js
 * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
 * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]);
 * x.print();
 * y.print();
 * tf.einsum('bi,bi->b', x, y).print();
 * ```
 *
 * Outer prouduct:
 * ```js
 * const x = tf.tensor1d([1, 3, 5]);
 * const y = tf.tensor1d([2, 4, 6]);
 * x.print();
 * y.print();
 * tf.einsum('i,j->ij', x, y).print();
 * ```
 *
 * Matrix transpose:
 * ```js
 * const x = tf.tensor2d([[1, 2], [3, 4]]);
 * x.print();
 * tf.einsum('ij->ji', x).print();
 * ```
 *
 * Batch matrix transpose:
 * ```js
 * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]);
 * x.print();
 * tf.einsum('bij->bji', x).print();
 * ```
 *
 * Limitations:
 *
 * This implementation of einsum has the following limitations:
 *
 * - Does not support >2 input tensors.
 * - Does not support duplicate axes for any given input tensor. E.g., equation
 *   'ii->' is not supported.
 * - The `...` notation is not supported.
 *
 * @param equation a string describing the contraction, in the same format as
 * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html).
 * @param tensors the input(s) to contract (each one a Tensor), whose shapes
 *     should be consistent with equation.
 * @returns The output tensor.
 *
 * @doc {heading: 'Tensors', subheading: 'Matrices'}
 */ parcelHelpers.export(exports, "einsum_", ()=>einsum_);
parcelHelpers.export(exports, "einsum", ()=>einsum);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
function einsum_(equation, ...tensors) {
    const $tensors = tensors.map((t, i)=>(0, _tensorUtilEnv.convertToTensor)(t, `tensors${i}`, "einsum"));
    const attrs = {
        equation
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Einsum), $tensors, attrs);
}
const einsum = /* @__PURE__ */ (0, _operation.op)({
    einsum_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dE197":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "elu", ()=>elu);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes exponential linear element-wise: `x > 0 ? x : (e ^ x) - 1`.
 *
 * ```js
 * const x = tf.tensor1d([-1, 1, -3, 2]);
 *
 * x.elu().print();  // or tf.elu(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function elu_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "elu", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Elu), inputs);
}
const elu = /* @__PURE__ */ (0, _operation.op)({
    elu_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"f2yaA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ensureShape", ()=>ensureShape);
var _tensorUtilEnv = require("../tensor_util_env");
var _utilBase = require("../util_base");
var _operation = require("./operation");
/**
 * Checks the input tensor mathes the given shape.
 *
 * Given an input tensor, returns a new tensor with the same values as the
 * input tensor with shape `shape`.
 *
 * The method supports the null value in tensor. It will still check the shapes,
 * and null is a placeholder.
 *
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 * const y = tf.tensor1d([1, null, 3, 4]);
 * const z = tf.tensor2d([1, 2, 3, 4], [2,2]);
 * tf.ensureShape(x, [4]).print();
 * tf.ensureShape(y, [4]).print();
 * tf.ensureShape(z, [null, 2]).print();
 * ```
 *
 * @param x The input tensor to be ensured.
 * @param shape A TensorShape representing the shape of this tensor, an array
 *     or null.
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function ensureShape_(x, shape) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "ensureShape", "string_or_numeric");
    if (!(0, _utilBase.arraysEqualWithNull)($x.shape, shape)) throw new Error(`EnsureShape: Shape of tensor ${$x.shape} is not compatible with expected shape ${shape}`);
    return x;
}
const ensureShape = /* @__PURE__ */ (0, _operation.op)({
    ensureShape_
});

},{"../tensor_util_env":"6vfHI","../util_base":"4Vo4a","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"e0JJC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "erf", ()=>erf);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _cast = require("./cast");
var _operation = require("./operation");
/**
 * Computes Gauss error function of the input `tf.Tensor` element-wise:
 * `erf(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, .1, -.1, .7]);
 *
 * x.erf().print(); // or tf.erf(x);
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function erf_(x) {
    let $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "erf");
    _util.assert($x.dtype === "int32" || $x.dtype === "float32", ()=>"Input dtype must be `int32` or `float32`.");
    if ($x.dtype === "int32") $x = (0, _cast.cast)($x, "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Erf), inputs);
}
const erf = /* @__PURE__ */ (0, _operation.op)({
    erf_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./cast":"b4VDA","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8OAGY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "euclideanNorm", ()=>euclideanNorm);
var _norm = require("./norm");
var _operation = require("./operation");
/**
 * Computes the Euclidean norm of scalar, vectors, and matrices.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 *
 * x.euclideanNorm().print();  // or tf.euclideanNorm(x)
 * ```
 *
 * @param x The input array.
 * @param axis Optional. If axis is null (the default), the input is
 * considered a vector and a single vector norm is computed over the entire
 * set of values in the Tensor, i.e. euclideanNorm(x) is equivalent
 * to euclideanNorm(x.reshape([-1])). If axis is an integer, the input
 * is considered a batch of vectors, and axis determines the axis in x
 * over which to compute vector norms. If axis is a 2-tuple of integer it is
 * considered a batch of matrices and axis determines the axes in NDArray
 * over which to compute a matrix norm.
 * @param keepDims Optional. If true, the norm has the same dimensionality
 * as the input.
 *
 * @doc {heading: 'Operations', subheading: 'Matrices'}
 */ function euclideanNorm_(x, axis = null, keepDims = false) {
    return (0, _norm.norm)(x, "euclidean", axis, keepDims);
}
const euclideanNorm = /* @__PURE__ */ (0, _operation.op)({
    euclideanNorm_
});

},{"./norm":"eOnCA","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eOnCA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "norm", ()=>norm);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _abs = require("./abs");
var _axisUtil = require("./axis_util");
var _max = require("./max");
var _min = require("./min");
var _operation = require("./operation");
var _pow = require("./pow");
var _reshape = require("./reshape");
var _scalar = require("./scalar");
var _sqrt = require("./sqrt");
var _square = require("./square");
var _sum = require("./sum");
/**
 * Computes the norm of scalar, vectors, and matrices.
 * This function can compute several different vector norms (the 1-norm, the
 * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)
 * and matrix norms (Frobenius, 1-norm, and inf-norm).
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 *
 * x.norm().print();  // or tf.norm(x)
 * ```
 *
 * @param x The input array.
 * @param ord Optional. Order of the norm. Supported norm types are
 * following:
 *
 *  | ord        | norm for matrices         | norm for vectors
 *  |------------|---------------------------|---------------------
 *  |'euclidean' |Frobenius norm             |2-norm
 *  |'fro'       |Frobenius norm	           |
 *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))
 *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))
 *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))
 *  |2           |                           |sum(abs(x)^2)^(1/2)
 *
 * @param axis Optional. If axis is null (the default), the input is
 * considered a vector and a single vector norm is computed over the entire
 * set of values in the Tensor, i.e. norm(x, ord) is equivalent
 * to norm(x.reshape([-1]), ord). If axis is an integer, the input
 * is considered a batch of vectors, and axis determines the axis in x
 * over which to compute vector norms. If axis is a 2-tuple of integer it is
 * considered a batch of matrices and axis determines the axes in NDArray
 * over which to compute a matrix norm.
 * @param keepDims Optional. If true, the norm has the same dimensionality
 * as the input.
 *
 * @doc {heading: 'Operations', subheading: 'Matrices'}
 */ function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
    x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "norm");
    const norm = normImpl(x, ord, axis);
    let keepDimsShape = norm.shape;
    if (keepDims) {
        const axes = (0, _util.parseAxisParam)(axis, x.shape);
        keepDimsShape = _axisUtil.expandShapeToKeepDim(norm.shape, axes);
    }
    return (0, _reshape.reshape)(norm, keepDimsShape);
}
function normImpl(x, p, axis = null) {
    if (x.rank === 0) return (0, _abs.abs)(x);
    // consider vector when no axis is specified
    if (x.rank !== 1 && axis === null) return normImpl((0, _reshape.reshape)(x, [
        -1
    ]), p, axis);
    // vector
    if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
        if (p === 1) return (0, _sum.sum)((0, _abs.abs)(x), axis);
        if (p === Infinity) return (0, _max.max)((0, _abs.abs)(x), axis);
        if (p === -Infinity) return (0, _min.min)((0, _abs.abs)(x), axis);
        if (p === "euclidean" || p === 2) // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2
        return (0, _sqrt.sqrt)((0, _sum.sum)((0, _pow.pow)((0, _abs.abs)(x), (0, _scalar.scalar)(2, "int32")), axis));
        throw new Error(`Error in norm: invalid ord value: ${p}`);
    }
    // matrix (assumption axis[0] < axis[1])
    if (Array.isArray(axis) && axis.length === 2) {
        if (p === 1) return (0, _max.max)((0, _sum.sum)((0, _abs.abs)(x), axis[0]), axis[1] - 1);
        if (p === Infinity) return (0, _max.max)((0, _sum.sum)((0, _abs.abs)(x), axis[1]), axis[0]);
        if (p === -Infinity) return (0, _min.min)((0, _sum.sum)((0, _abs.abs)(x), axis[1]), axis[0]);
        if (p === "fro" || p === "euclidean") // norm(x) = sqrt(sum(pow(x, 2)))
        return (0, _sqrt.sqrt)((0, _sum.sum)((0, _square.square)(x), axis));
        throw new Error(`Error in norm: invalid ord value: ${p}`);
    }
    throw new Error(`Error in norm: invalid axis: ${axis}`);
}
const norm = /* @__PURE__ */ (0, _operation.op)({
    norm_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./abs":"iZQ4e","./axis_util":"hzS4B","./max":"cb6Hb","./min":"fhwke","./operation":"ip64W","./pow":"gezpC","./reshape":"ew3wZ","./scalar":"7lhC8","./sqrt":"cOGi9","./square":"cOosW","./sum":"5awd0","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hzS4B":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Returns true if the axis specifies the inner most dimensions of the
 * array.
 */ parcelHelpers.export(exports, "axesAreInnerMostDims", ()=>axesAreInnerMostDims);
parcelHelpers.export(exports, "combineLocations", ()=>combineLocations);
parcelHelpers.export(exports, "computeOutAndReduceShapes", ()=>computeOutAndReduceShapes);
parcelHelpers.export(exports, "expandShapeToKeepDim", ()=>expandShapeToKeepDim);
parcelHelpers.export(exports, "assertAxesAreInnerMostDims", ()=>assertAxesAreInnerMostDims);
/**
 * Returns the axes permutation to be used with `tf.transpose`, if such
 * permutation is necessary. Otherwise it returns null. This method is used by
 * operations that operate only on inner-most axes.
 */ parcelHelpers.export(exports, "getAxesPermutation", ()=>getAxesPermutation);
/** Returns the axes permutation that undoes the original permutation. */ parcelHelpers.export(exports, "getUndoAxesPermutation", ()=>getUndoAxesPermutation);
parcelHelpers.export(exports, "getInnerMostAxes", ()=>getInnerMostAxes);
var _util = require("../util");
function axesAreInnerMostDims(axes, rank) {
    for(let i = 0; i < axes.length; ++i){
        if (axes[axes.length - i - 1] !== rank - 1 - i) return false;
    }
    return true;
}
function combineLocations(outputLoc, reduceLoc, axes) {
    const rank = outputLoc.length + reduceLoc.length;
    const loc = [];
    let outIdx = 0;
    let reduceIdx = 0;
    for(let dim = 0; dim < rank; dim++)if (axes.indexOf(dim) === -1) loc.push(outputLoc[outIdx++]);
    else loc.push(reduceLoc[reduceIdx++]);
    return loc;
}
function computeOutAndReduceShapes(aShape, axes) {
    const outShape = [];
    const rank = aShape.length;
    for(let dim = 0; dim < rank; dim++)if (axes.indexOf(dim) === -1) outShape.push(aShape[dim]);
    const reduceShape = axes.map((dim)=>aShape[dim]);
    return [
        outShape,
        reduceShape
    ];
}
function expandShapeToKeepDim(shape, axes) {
    const reduceSubShape = axes.map((x)=>1);
    return combineLocations(shape, reduceSubShape, axes);
}
function assertAxesAreInnerMostDims(msg, axes, rank) {
    _util.assert(axesAreInnerMostDims(axes, rank), ()=>`${msg} supports only inner-most axes for now. ` + `Got axes ${axes} and rank-${rank} input.`);
}
function getAxesPermutation(axes, rank) {
    if (axesAreInnerMostDims(axes, rank)) return null;
    const result = [];
    for(let i = 0; i < rank; ++i)if (axes.indexOf(i) === -1) result.push(i);
    axes.forEach((axis)=>result.push(axis));
    return result;
}
function getUndoAxesPermutation(axes) {
    return axes.map((axis, i)=>[
            i,
            axis
        ]).sort((a, b)=>a[1] - b[1]).map((x)=>x[0]);
}
function getInnerMostAxes(numAxes, rank) {
    const res = [];
    for(let i = rank - numAxes; i < rank; ++i)res.push(i);
    return res;
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cb6Hb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "max", ()=>max);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the maximum of elements across dimensions of a `tf.Tensor`.
 *
 * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
 * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
 * `axes`. If `keepDims` is true, the reduced dimensions are retained with
 * length 1. If `axes` has no entries, all dimensions are reduced, and a
 * `tf.Tensor` with a single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.max().print();  // or tf.max(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * const axis = 1;
 * x.max(axis).print();  // or tf.max(x, axis)
 * ```
 *
 * @param x The input tensor.
 * @param axis The dimension(s) to reduce. By default it reduces
 *     all dimensions.
 * @param keepDims If true, retains reduced dimensions with size 1.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function max_(x, axis = null, keepDims = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "max");
    const inputs = {
        x: $x
    };
    const attrs = {
        reductionIndices: axis,
        keepDims
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Max), inputs, attrs);
}
const max = /* @__PURE__ */ (0, _operation.op)({
    max_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fhwke":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "min", ()=>min);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the minimum value from the input.
 *
 * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
 * is true, the rank of the array is reduced by 1 for each entry in `axes`.
 * If `keepDims` is true, the reduced dimensions are retained with length 1.
 * If `axes` has no entries, all dimensions are reduced, and an array with a
 * single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.min().print();  // or tf.min(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * const axis = 1;
 * x.min(axis).print();  // or tf.min(x, axis)
 * ```
 *
 * @param x The input Tensor.
 * @param axis The dimension(s) to reduce. By default it reduces
 *     all dimensions.
 * @param keepDims If true, retains reduced dimensions with size 1.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function min_(x, axis = null, keepDims = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "min");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        keepDims
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Min), inputs, attrs);
}
const min = /* @__PURE__ */ (0, _operation.op)({
    min_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gezpC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pow", ()=>pow);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the power of one `tf.Tensor` to another. Supports broadcasting.
 *
 * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
 * corresponding elements in x and y. The result's dtype will be the upcasted
 * type of the `base` and `exp` dtypes.
 *
 * ```js
 * const a = tf.tensor([[2, 3], [4, 5]])
 * const b = tf.tensor([[1, 2], [3, 0]]).toInt();
 *
 * a.pow(b).print();  // or tf.pow(a, b)
 * ```
 *
 * ```js
 * const a = tf.tensor([[1, 2], [3, 4]])
 * const b = tf.tensor(2).toInt();
 *
 * a.pow(b).print();  // or tf.pow(a, b)
 * ```
 * We also expose `powStrict` which has the same signature as this op and
 * asserts that `base` and `exp` are the same shape (does not broadcast).
 *
 * @param base The base `tf.Tensor` to pow element-wise.
 * @param exp The exponent `tf.Tensor` to pow element-wise.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function pow_(base, exp) {
    let $base = (0, _tensorUtilEnv.convertToTensor)(base, "base", "pow");
    let $exp = (0, _tensorUtilEnv.convertToTensor)(exp, "exp", "pow");
    [$base, $exp] = (0, _tensorUtil.makeTypesMatch)($base, $exp);
    const inputs = {
        a: $base,
        b: $exp
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Pow), inputs);
}
const pow = /* @__PURE__ */ (0, _operation.op)({
    pow_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7lhC8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.
 *
 * The same functionality can be achieved with `tf.tensor`, but in general
 * we recommend using `tf.scalar` as it makes the code more readable.
 *
 * ```js
 * tf.scalar(3.14).print();
 * ```
 *
 * @param value The value of the scalar.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "scalar", ()=>scalar);
var _util = require("../util");
var _tensorOpsUtil = require("./tensor_ops_util");
function scalar(value, dtype) {
    if (((0, _util.isTypedArray)(value) && dtype !== "string" || Array.isArray(value)) && dtype !== "complex64") throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");
    if (dtype === "string" && (0, _util.isTypedArray)(value) && !(value instanceof Uint8Array)) throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
    const shape = [];
    const inferredShape = [];
    return (0, _tensorOpsUtil.makeTensor)(value, shape, inferredShape, dtype);
}

},{"../util":"ctavo","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cOGi9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sqrt", ()=>sqrt);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 4, -1]);
 *
 * x.sqrt().print();  // or tf.sqrt(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function sqrt_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "sqrt", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Sqrt), inputs);
}
const sqrt = /* @__PURE__ */ (0, _operation.op)({
    sqrt_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cOosW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "square", ()=>square);
var _engine = require("../engine");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes square of `x` element-wise: `x ^ 2`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);
 *
 * x.square().print();  // or tf.square(x)
 * ```
 * @param x The input Tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function square_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "square");
    const attrs = {};
    return (0, _engine.ENGINE).runKernel("Square", {
        x: $x
    }, attrs);
}
const square = /* @__PURE__ */ (0, _operation.op)({
    square_
});

},{"../engine":"1g2qo","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5awd0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sum", ()=>sum);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _cast = require("./cast");
var _operation = require("./operation");
/**
 * Computes the sum of elements across dimensions of a `tf.Tensor`.
 *
 * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
 * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
 * `axes`. If `keepDims` is true, the reduced dimensions are retained with
 * length 1. If axes has no entries, all dimensions are reduced, and a
 * `tf.Tensor` with a single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.sum().print();  // or tf.sum(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * const axis = 1;
 * x.sum(axis).print();  // or tf.sum(x, axis)
 * ```
 *
 * @param x The input tensor to compute the sum over. If the dtype is `bool`
 *   it will be converted to `int32` and the output dtype will be `int32`.
 * @param axis The dimension(s) to reduce. By default it reduces
 *     all dimensions.
 * @param keepDims If true, retains reduced dimensions with size 1.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function sum_(x, axis = null, keepDims = false) {
    let $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "sum");
    if ($x.dtype === "bool") $x = (0, _cast.cast)($x, "int32");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        keepDims
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Sum), inputs, attrs);
}
const sum = /* @__PURE__ */ (0, _operation.op)({
    sum_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./cast":"b4VDA","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1CsGu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "exp", ()=>exp);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, -3]);
 *
 * x.exp().print();  // or tf.exp(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function exp_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "exp");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Exp), inputs);
}
const exp = /* @__PURE__ */ (0, _operation.op)({
    exp_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lc7t7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "expandDims", ()=>expandDims);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension
 * into the tensor's shape.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 * const axis = 1;
 * x.expandDims(axis).print();
 * ```
 *
 * @param x The input tensor whose dimensions are to be expanded.
 * @param axis The dimension index at which to insert shape of `1`. Defaults
 *     to 0 (the first dimension).
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function expandDims_(x, axis = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "expandDims", "string_or_numeric");
    _util.assert(axis <= $x.rank, ()=>"Axis must be <= rank of the tensor");
    const inputs = {
        input: $x
    };
    const attrs = {
        dim: axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.ExpandDims), inputs, attrs);
}
const expandDims = /* @__PURE__ */ (0, _operation.op)({
    expandDims_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lDAq4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "expm1", ()=>expm1);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes exponential of the input `tf.Tensor` minus one element-wise.
 * `e ^ x - 1`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, -3]);
 *
 * x.expm1().print();  // or tf.expm1(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function expm1_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "expm1");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Expm1), inputs);
}
const expm1 = /* @__PURE__ */ (0, _operation.op)({
    expm1_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hDQ1U":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "eye", ()=>eye);
var _buffer = require("./buffer");
var _expandDims = require("./expand_dims");
var _operation = require("./operation");
var _reshape = require("./reshape");
var _tile = require("./tile");
/**
 * Create an identity matrix.
 *
 * @param numRows Number of rows.
 * @param numColumns Number of columns. Defaults to `numRows`.
 * @param batchShape If provided, will add the batch shape to the beginning
 *   of the shape of the returned `tf.Tensor` by repeating the identity
 *   matrix.
 * @param dtype Data type.
 * @returns Identity matrix of the specified size and data type, possibly
 *   with batch repetition if `batchShape` is specified.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function eye_(numRows, numColumns, batchShape, dtype = "float32") {
    if (numColumns == null) numColumns = numRows;
    const buff = (0, _buffer.buffer)([
        numRows,
        numColumns
    ], dtype);
    const n = numRows <= numColumns ? numRows : numColumns;
    for(let i = 0; i < n; ++i)buff.set(1, i, i);
    const out = (0, _reshape.reshape)(buff.toTensor(), [
        numRows,
        numColumns
    ]);
    if (batchShape == null) return out;
    else {
        if (batchShape.length === 1) return (0, _tile.tile)((0, _expandDims.expandDims)(out, 0), [
            batchShape[0],
            1,
            1
        ]);
        else if (batchShape.length === 2) // tslint:disable-next-line:no-unnecessary-type-assertion
        return (0, _tile.tile)((0, _expandDims.expandDims)((0, _expandDims.expandDims)(out, 0), 0), [
            batchShape[0],
            batchShape[1],
            1,
            1
        ]);
        else if (batchShape.length === 3) // tslint:disable-next-line:no-unnecessary-type-assertion
        return (0, _tile.tile)((0, _expandDims.expandDims)((0, _expandDims.expandDims)((0, _expandDims.expandDims)(out, 0), 0), 0), [
            batchShape[0],
            batchShape[1],
            batchShape[2],
            1,
            1
        ]);
        else throw new Error(`eye() currently supports only 1D and 2D ` + // tslint:disable-next-line:no-any
        `batchShapes, but received ${batchShape.length}D.`);
    }
}
const eye = /* @__PURE__ */ (0, _operation.op)({
    eye_
});

},{"./buffer":"dYyY7","./expand_dims":"lc7t7","./operation":"ip64W","./reshape":"ew3wZ","./tile":"gzqG2","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gzqG2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tile", ()=>tile);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Construct a tensor by repeating it the number of times given by reps.
 *
 * This operation creates a new tensor by replicating `input` `reps`
 * times. The output tensor's `i`th dimension has `input.shape[i] *
 * reps[i]` elements, and the values of `input` are replicated
 * `reps[i]` times along the `i`th dimension. For example, tiling
 * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 *
 * a.tile([2]).print();    // or tf.tile(a, [2])
 * ```
 *
 * ```js
 * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * a.tile([1, 2]).print();  // or tf.tile(a, [1,2])
 * ```
 * @param x The tensor to tile.
 * @param reps Determines the number of replications per dimension.
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function tile_(x, reps) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "tile", "string_or_numeric");
    _util.assert($x.rank === reps.length, ()=>`Error in transpose: rank of input ${$x.rank} ` + `must match length of reps ${reps}.`);
    const inputs = {
        x: $x
    };
    const attrs = {
        reps
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Tile), inputs, attrs);
}
const tile = /* @__PURE__ */ (0, _operation.op)({
    tile_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"35qCf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "floor", ()=>floor);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.
 *
 * ```js
 * const x = tf.tensor1d([.6, 1.1, -3.3]);
 *
 * x.floor().print();  // or tf.floor(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function floor_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "floor", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Floor), inputs);
}
const floor = /* @__PURE__ */ (0, _operation.op)({
    floor_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5p5vK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gather", ()=>gather);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Gather slices from tensor `x`'s axis `axis` according to `indices`.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 * const indices = tf.tensor1d([1, 3, 3], 'int32');
 *
 * x.gather(indices).print();
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 * const indices = tf.tensor1d([1, 1, 0], 'int32');
 *
 * x.gather(indices).print();
 * ```
 * @param x The input tensor whose slices are to be gathered.
 * @param indices The indices of the values to extract.
 * @param axis The axis over which to select values. Defaults to 0.
 * @param batchDims Optional. The number of batch dimensions. It must be less
 *     than or equal to rank(indices). Defaults to 0.
 *     The output tensor will have shape of
 *     `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]`
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function gather_(x, indices, axis = 0, batchDims = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "gather");
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "gather", "int32");
    const inputs = {
        x: $x,
        indices: $indices
    };
    const attrs = {
        axis,
        batchDims
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.GatherV2), inputs, attrs);
}
const gather = /* @__PURE__ */ (0, _operation.op)({
    gather_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3Nh8O":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "greater", ()=>greater);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of (a > b) element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 * const b = tf.tensor1d([2, 2, 2]);
 *
 * a.greater(b).print();
 * ```
 *
 * @param a The first input tensor.
 * @param b The second input tensor. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function greater_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "greater", "string_or_numeric");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "greater", "string_or_numeric");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Greater), inputs);
}
const greater = /* @__PURE__ */ (0, _operation.op)({
    greater_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"f7S2c":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "greaterEqual", ()=>greaterEqual);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of (a >= b) element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 * const b = tf.tensor1d([2, 2, 2]);
 *
 * a.greaterEqual(b).print();
 * ```
 *
 * @param a The first input tensor.
 * @param b The second input tensor. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function greaterEqual_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "greaterEqual", "string_or_numeric");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "greaterEqual", "string_or_numeric");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.GreaterEqual), inputs);
}
const greaterEqual = /* @__PURE__ */ (0, _operation.op)({
    greaterEqual_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lDORm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "imag", ()=>imag);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns the imaginary part of a complex (or real) tensor.
 *
 * Given a tensor input, this operation returns a tensor of type float that is
 * the imaginary part of each element in input considered as a complex number.
 * If input is real, a tensor of all zeros is returned.
 *
 * ```js
 * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
 * tf.imag(x).print();
 * ```
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function imag_(input) {
    const $input = (0, _tensorUtilEnv.convertToTensor)(input, "input", "imag");
    const inputs = {
        input: $input
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Imag), inputs);
}
const imag = /* @__PURE__ */ (0, _operation.op)({
    imag_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ddlWF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isFinite", ()=>isFinite);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns which elements of x are finite.
 *
 * ```js
 * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
 *
 * x.isFinite().print();  // or tf.isNaN(x)
 * ```
 * @param x The input Tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function isFinite_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "isFinite");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.IsFinite), inputs);
}
const isFinite = /* @__PURE__ */ (0, _operation.op)({
    isFinite_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2QZvv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isInf", ()=>isInf);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns which elements of x are Infinity or -Infinity.
 *
 * ```js
 * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
 *
 * x.isInf().print();  // or tf.isNaN(x)
 * ```
 * @param x The input Tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function isInf_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "isInf");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.IsInf), inputs);
}
const isInf = /* @__PURE__ */ (0, _operation.op)({
    isInf_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"58UC5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isNaN", ()=>isNaN);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns which elements of x are NaN.
 *
 * ```js
 * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);
 *
 * x.isNaN().print();  // or tf.isNaN(x)
 * ```
 * @param x The input Tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function isNaN_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "isNaN");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.IsNan), inputs);
}
const isNaN = /* @__PURE__ */ (0, _operation.op)({
    isNaN_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aRBcm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "leakyRelu", ()=>leakyRelu);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes leaky rectified linear element-wise.
 *
 * See
 * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](
 *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)
 *
 * ```js
 * const x = tf.tensor1d([-1, 2, -3, 4]);
 *
 * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)
 * ```
 * @param x The input tensor.
 * @param alpha The scaling factor for negative values, defaults to 0.2.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function leakyRelu_(x, alpha = 0.2) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "leakyRelu");
    const inputs = {
        x: $x
    };
    const attrs = {
        alpha
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.LeakyRelu), inputs, attrs);
}
const leakyRelu = /* @__PURE__ */ (0, _operation.op)({
    leakyRelu_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hCGN5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "less", ()=>less);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of (a < b) element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 * const b = tf.tensor1d([2, 2, 2]);
 *
 * a.less(b).print();
 * ```
 * @param a The first input tensor.
 * @param b The second input tensor. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function less_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "less", "string_or_numeric");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "less", "string_or_numeric");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Less), inputs);
}
const less = /* @__PURE__ */ (0, _operation.op)({
    less_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"j9Jhy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "lessEqual", ()=>lessEqual);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of (a <= b) element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 * const b = tf.tensor1d([2, 2, 2]);
 *
 * a.lessEqual(b).print();
 * ```
 *
 * @param a The first input tensor.
 * @param b The second input tensor. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function lessEqual_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "lessEqual", "string_or_numeric");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "lessEqual", "string_or_numeric");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.LessEqual), inputs);
}
const lessEqual = /* @__PURE__ */ (0, _operation.op)({
    lessEqual_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7Vd0v":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Return an evenly spaced sequence of numbers over the given interval.
 *
 * ```js
 * tf.linspace(0, 9, 10).print();
 * ```
 * @param start The start value of the sequence.
 * @param stop The end value of the sequence.
 * @param num The number of values to generate.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "linspace", ()=>linspace);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
function linspace(start, stop, num) {
    if (num <= 0) throw new Error("The number of values should be positive.");
    const attrs = {
        start,
        stop,
        num
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.LinSpace), {}, attrs);
}

},{"../engine":"1g2qo","../kernel_names":"hniYO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eTOXs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "localResponseNormalization", ()=>localResponseNormalization);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Normalizes the activation of a local neighborhood across or within
 * channels.
 *
 * @param x The input tensor. The 4-D input tensor is treated as a 3-D array
 *     of 1D vectors (along the last dimension), and each vector is
 *     normalized independently.
 * @param depthRadius The number of adjacent channels in the 1D normalization
 *     window.
 * @param bias A constant bias term for the basis.
 * @param alpha A scale factor, usually positive.
 * @param beta An exponent.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */ function localResponseNormalization_(x, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "localResponseNormalization");
    _util.assert($x.rank === 4 || $x.rank === 3, ()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${$x.rank}.`);
    _util.assert(_util.isInt(depthRadius), ()=>`Error in localResponseNormalization: depthRadius must be an ` + `integer but got depthRadius ${depthRadius}.`);
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    const inputs = {
        x: x4D
    };
    const attrs = {
        depthRadius,
        bias,
        alpha,
        beta
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.LRN), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    else return res;
}
const localResponseNormalization = /* @__PURE__ */ (0, _operation.op)({
    localResponseNormalization_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iex5O":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "log", ()=>log);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, Math.E]);
 *
 * x.log().print();  // or tf.log(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function log_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "log", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Log), inputs);
}
const log = /* @__PURE__ */ (0, _operation.op)({
    log_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jKhH4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "log1p", ()=>log1p);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes natural logarithm of the input `tf.Tensor` plus one
 * element-wise: `ln(1 + x)`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, Math.E - 1]);
 *
 * x.log1p().print();  // or tf.log1p(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function log1p_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "log1p");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Log1p), inputs);
}
const log1p = /* @__PURE__ */ (0, _operation.op)({
    log1p_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2X6mM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logSigmoid", ()=>logSigmoid);
var _gradients = require("../gradients");
var _tensorUtilEnv = require("../tensor_util_env");
var _mul = require("./mul");
var _neg = require("./neg");
var _operation = require("./operation");
var _sigmoid = require("./sigmoid");
var _softplus = require("./softplus");
/**
 * Computes log sigmoid of the input `tf.Tensor` element-wise:
 * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.logSigmoid().print();  // or tf.logSigmoid(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function logSigmoid_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "logSigmoid");
    // Use a custom gradient to maintain previous implementation.
    // There is no LogSigmoid kernel in TF so we can't use engine.runKernel
    // directly
    const customOp = (0, _gradients.customGrad)((x)=>{
        // TODO(yassogba) we can remove the chained softplus call here only
        // after backends have modualrized softplus at which point we can call
        // engine runKernel(..., Sotfplus, ...) directly.
        const value = (0, _neg.neg)((0, _softplus.softplus)((0, _neg.neg)(x)));
        const gradFunc = (dy)=>{
            const derX = (0, _mul.mul)(dy, (0, _sigmoid.sigmoid)((0, _neg.neg)(x)));
            return derX;
        };
        return {
            value,
            gradFunc
        };
    });
    return customOp($x);
}
const logSigmoid = /* @__PURE__ */ (0, _operation.op)({
    logSigmoid_
});

},{"../gradients":"dDHpg","../tensor_util_env":"6vfHI","./mul":"fOWrC","./neg":"3jQfI","./operation":"ip64W","./sigmoid":"81E16","./softplus":"4zLJS","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dDHpg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "customGrad", ()=>customGrad);
parcelHelpers.export(exports, "variableGrads", ()=>variableGrads);
parcelHelpers.export(exports, "valueAndGrad", ()=>valueAndGrad);
parcelHelpers.export(exports, "valueAndGrads", ()=>valueAndGrads);
parcelHelpers.export(exports, "grad", ()=>grad);
parcelHelpers.export(exports, "grads", ()=>grads);
var _engine = require("./engine");
var _tensor = require("./tensor");
var _tensorUtilEnv = require("./tensor_util_env");
var _util = require("./util");
/**
 * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the
 * gradient of `f(x)` with respect to `x`.
 *
 * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to
 * `x` is computed instead. `f(x)` must take a single tensor `x` and return a
 * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.
 *
 * ```js
 * // f(x) = x ^ 2
 * const f = x => x.square();
 * // f'(x) = 2x
 * const g = tf.grad(f);
 *
 * const x = tf.tensor1d([2, 3]);
 * g(x).print();
 * ```
 *
 * ```js
 * // f(x) = x ^ 3
 * const f = x => x.pow(tf.scalar(3, 'int32'));
 * // f'(x) = 3x ^ 2
 * const g = tf.grad(f);
 * // f''(x) = 6x
 * const gg = tf.grad(g);
 *
 * const x = tf.tensor1d([2, 3]);
 * gg(x).print();
 * ```
 *
 * @param f The function f(x), to compute gradient for.
 *
 * @doc {heading: 'Training', subheading: 'Gradients'}
 */ function grad(f) {
    _util.assert(_util.isFunction(f), ()=>"The f passed in grad(f) must be a function");
    return (x, dy)=>{
        // x can be of any dtype, thus null as the last argument.
        const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "tf.grad", "string_or_numeric");
        const $dy = dy != null ? (0, _tensorUtilEnv.convertToTensor)(dy, "dy", "tf.grad") : null;
        return (0, _engine.ENGINE).tidy(()=>{
            const { value, grads } = (0, _engine.ENGINE).gradients(()=>f($x), [
                $x
            ], $dy);
            if ($dy != null) _util.assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)");
            checkGrads(grads);
            return grads[0];
        });
    };
}
/**
 * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,
 * which gives an array of gradients of `f()` with respect to each input
 * [`x1`,`x2`,...].
 *
 * If `dy` is passed when calling `g()`, the gradient of
 * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.
 * The provided `f` must take one or more tensors and return a single tensor
 * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.
 *
 * ```js
 * // f(a, b) = a * b
 * const f = (a, b) => a.mul(b);
 * // df / da = b, df / db = a
 * const g = tf.grads(f);
 *
 * const a = tf.tensor1d([2, 3]);
 * const b = tf.tensor1d([-2, -3]);
 * const [da, db] = g([a, b]);
 * console.log('da');
 * da.print();
 * console.log('db');
 * db.print();
 * ```
 *
 * @param f The function `f(x1, x2,...)` to compute gradients for.
 *
 * @doc {heading: 'Training', subheading: 'Gradients'}
 */ function grads(f) {
    _util.assert(_util.isFunction(f), ()=>"The f passed in grads(f) must be a function");
    return (args, dy)=>{
        _util.assert(Array.isArray(args), ()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
        // args can be of any dtype, thus null as the last argument.
        const $args = (0, _tensorUtilEnv.convertToTensorArray)(args, "args", "tf.grads", "string_or_numeric");
        const $dy = dy != null ? (0, _tensorUtilEnv.convertToTensor)(dy, "dy", "tf.grads") : null;
        return (0, _engine.ENGINE).tidy(()=>{
            const { value, grads } = (0, _engine.ENGINE).gradients(()=>f(...$args), $args, $dy);
            if ($dy != null) _util.assertShapesMatch(value.shape, $dy.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
            checkGrads(grads);
            return grads;
        });
    };
}
/**
 * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`
 * returns a metric you want to show.
 *
 * The result is a rich object with the following properties:
 * - grad: The gradient of `f(x)` w.r.t. `x` (result of `tf.grad`).
 * - value: The value returned by `f(x)`.
 *
 * ```js
 * // f(x) = x ^ 2
 * const f = x => x.square();
 * // f'(x) = 2x
 * const g = tf.valueAndGrad(f);
 *
 * const x = tf.tensor1d([2, 3]);
 * const {value, grad} = g(x);
 *
 * console.log('value');
 * value.print();
 * console.log('grad');
 * grad.print();
 * ```
 *
 * @doc {heading: 'Training', subheading: 'Gradients'}
 */ function valueAndGrad(f) {
    _util.assert(_util.isFunction(f), ()=>"The f passed in valueAndGrad(f) must be a function");
    return (x, dy)=>{
        _util.assert(x instanceof (0, _tensor.Tensor), ()=>"The x passed in valueAndGrad(f)(x) must be a tensor");
        _util.assert(dy == null || dy instanceof (0, _tensor.Tensor), ()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
        const { grads, value } = (0, _engine.ENGINE).gradients(()=>f(x), [
            x
        ], dy);
        checkGrads(grads);
        return {
            grad: grads[0],
            value
        };
    };
}
/**
 * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`
 * returns a metric you want to show.
 *
 * The result is a rich object with the following properties:
 * - grads: The gradients of `f()` w.r.t. each input (result of `tf.grads`).
 * - value: The value returned by `f(x)`.
 *
 * ```js
 * // f(a, b) = a * b
 * const f = (a, b) => a.mul(b);
 * // df/da = b, df/db = a
 * const g = tf.valueAndGrads(f);
 *
 * const a = tf.tensor1d([2, 3]);
 * const b = tf.tensor1d([-2, -3]);
 * const {value, grads} = g([a, b]);
 *
 * const [da, db] = grads;
 *
 * console.log('value');
 * value.print();
 *
 * console.log('da');
 * da.print();
 * console.log('db');
 * db.print();
 * ```
 *
 * @doc {heading: 'Training', subheading: 'Gradients'}
 */ function valueAndGrads(f) {
    _util.assert(_util.isFunction(f), ()=>"The f passed in valueAndGrads(f) must be a function");
    return (args, dy)=>{
        _util.assert(Array.isArray(args) && args.every((arg)=>arg instanceof (0, _tensor.Tensor)), ()=>"The args passed in valueAndGrads(f)(args) must be array of tensors");
        _util.assert(dy == null || dy instanceof (0, _tensor.Tensor), ()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
        const res = (0, _engine.ENGINE).gradients(()=>f(...args), args, dy);
        if (dy != null) _util.assertShapesMatch(res.value.shape, dy.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])");
        checkGrads(res.grads);
        return res;
    };
}
/**
 * Computes and returns the gradient of f(x) with respect to the list of
 * trainable variables provided by `varList`. If no list is provided, it
 * defaults to all trainable variables.
 *
 * ```js
 * const a = tf.variable(tf.tensor1d([3, 4]));
 * const b = tf.variable(tf.tensor1d([5, 6]));
 * const x = tf.tensor1d([1, 2]);
 *
 * // f(a, b) = a * x ^ 2 + b * x
 * const f = () => a.mul(x.square()).add(b.mul(x)).sum();
 * // df/da = x ^ 2, df/db = x
 * const {value, grads} = tf.variableGrads(f);
 *
 * Object.keys(grads).forEach(varName => grads[varName].print());
 * ```
 *
 * @param f The function to execute. f() should return a scalar.
 * @param varList The list of variables to compute the gradients with respect
 *     to. Defaults to all trainable variables.
 * @returns An object with the following keys and values:
 *   - `value`: The value of the function `f`.
 *   - `grads`: A map from the names of the variables to the gradients.
 *     If the `varList` argument is provided explicitly and contains a subset of
 *     non-trainable variables, this map in the return value will contain keys
 *     that map the names of the non-trainable variables to `null`.
 *
 * @doc {heading: 'Training', subheading: 'Gradients'}
 */ function variableGrads(f, varList) {
    _util.assert(_util.isFunction(f), ()=>"The f passed in variableGrads(f) must be a function");
    _util.assert(varList == null || Array.isArray(varList) && varList.every((v)=>v instanceof (0, _tensor.Variable)), ()=>"The varList passed in variableGrads(f, varList) must be an array of variables");
    const specifiedVarList = varList != null;
    if (!specifiedVarList) {
        // Get all of the trainable variables.
        varList = [];
        for(const varName in (0, _engine.ENGINE).registeredVariables)varList.push((0, _engine.ENGINE).registeredVariables[varName]);
    }
    const specifiedNonTrainable = specifiedVarList ? varList.filter((variable)=>!variable.trainable) : null;
    // Prune non-trainable variables.
    const originalVarCount = varList.length;
    varList = varList.filter((variable)=>variable.trainable);
    _util.assert(varList.length > 0, ()=>`variableGrads() expects at least one of the input variables to ` + `be trainable, but none of the ${originalVarCount} variables is ` + `trainable.`);
    const allowNoGradients = true;
    const { value, grads } = (0, _engine.ENGINE).gradients(f, varList, null, allowNoGradients);
    _util.assert(grads.some((g)=>g != null), ()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
    _util.assert(value.rank === 0, ()=>`The f passed in variableGrads(f) must return a scalar, but it ` + `returned a rank-${value.rank} tensor`);
    const namedGrads = {};
    varList.forEach((v, i)=>{
        if (grads[i] != null) namedGrads[v.name] = grads[i];
    });
    if (specifiedNonTrainable != null) // If varList is explicitly provided and contains non-trainable values,
    // add them to the returned gradients with `null` values.
    specifiedNonTrainable.forEach((v)=>namedGrads[v.name] = null);
    return {
        value,
        grads: namedGrads
    };
}
/**
 * Overrides the gradient computation of a function `f`.
 *
 * Takes a function
 * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}`
 * and returns another function `g(...inputs)` which takes the same inputs as
 * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients
 * with respect to each input of `f` are computed using `f().gradFunc`.
 *
 * The `save` function passed to `f` should be used for saving tensors needed
 * in the gradient. And the `saved` passed to the `gradFunc` is a
 * `NamedTensorMap`, which contains those saved tensors.
 *
 * ```js
 * const customOp = tf.customGrad((x, save) => {
 *   // Save x to make sure it's available later for the gradient.
 *   save([x]);
 *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);
 *   return {
 *     value: x.square(),
 *     // Note `saved.x` which points to the `x` we saved earlier.
 *     gradFunc: (dy, saved) => [dy.mul(saved[0].abs())]
 *   };
 * });
 *
 * const x = tf.tensor1d([-1, -2, 3]);
 * const dx = tf.grad(x => customOp(x));
 *
 * console.log(`f(x):`);
 * customOp(x).print();
 * console.log(`f'(x):`);
 * dx(x).print();
 * ```
 *
 * @param f The function to evaluate in forward mode, which should return
 *     `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc`
 *     returns the custom gradients of `f` with respect to its inputs.
 *
 * @doc {heading: 'Training', subheading: 'Gradients'}
 */ function customGrad(f) {
    return (0, _engine.ENGINE).customGrad(f);
}
function checkGrads(grads) {
    const numNullGradients = grads.filter((g)=>g == null).length;
    if (numNullGradients > 0) throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
    the f you passed encloses all operations that lead from x to y.`);
}

},{"./engine":"1g2qo","./tensor":"cJNoh","./tensor_util_env":"6vfHI","./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3jQfI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "neg", ()=>neg);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes `-1 * x` element-wise.
 *
 * ```js
 * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);
 *
 * x.neg().print();  // or tf.neg(x)
 * ```
 *
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function neg_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "neg");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Neg), inputs);
}
const neg = /* @__PURE__ */ (0, _operation.op)({
    neg_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4zLJS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "softplus", ()=>softplus);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.softplus().print();  // or tf.softplus(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function softplus_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "softplus");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Softplus), inputs);
}
const softplus = /* @__PURE__ */ (0, _operation.op)({
    softplus_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5yBCd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logSoftmax", ()=>logSoftmax);
var _gradients = require("../gradients");
var _tensorUtilEnv = require("../tensor_util_env");
var _cast = require("./cast");
var _exp = require("./exp");
var _log = require("./log");
var _max = require("./max");
var _mul = require("./mul");
var _operation = require("./operation");
var _sub = require("./sub");
var _sum = require("./sum");
/**
 * Computes the log softmax.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 *
 * a.logSoftmax().print();  // or tf.logSoftmax(a)
 * ```
 *
 * ```js
 * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
 *
 * a.logSoftmax().print();  // or tf.logSoftmax(a)
 * ```
 *
 * @param logits The logits array.
 * @param axis The dimension softmax would be performed on. Defaults to `-1`
 *     which indicates the last dimension.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */ function logSoftmax_(logits, axis = -1) {
    const $logits = (0, _tensorUtilEnv.convertToTensor)(logits, "logits", "logSoftmax");
    if (axis === -1) axis = $logits.rank - 1;
    if (axis !== $logits.rank - 1) throw Error("Log Softmax along a non-last dimension is not yet supported. " + `Logits was rank ${$logits.rank} and axis was ${axis}`);
    // const forward: ForwardFunc<Tensor> = (backend, save) => {
    //   const keepDims = true;
    //   const xMax = max(logits, axis, true);
    //   const shifted = sub(logits, xMax);
    //   const value =
    //       sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis,
    //       keepDims)));
    //   save([value]);
    //   return value;
    // };
    // Use a custom gradient for numerical stability.
    const customOp = (0, _gradients.customGrad)((logits, save)=>{
        const keepDims = true;
        const xMax = (0, _max.max)(logits, axis, true);
        const shifted = (0, _sub.sub)(logits, xMax);
        const value = (0, _sub.sub)((0, _cast.cast)(shifted, "float32"), (0, _log.log)((0, _sum.sum)((0, _exp.exp)(shifted), axis, keepDims)));
        save([
            value
        ]);
        const gradFunc = (dy, saved)=>{
            const [value] = saved;
            const keepDims = true;
            const softmax = (0, _exp.exp)(value);
            return (0, _sub.sub)(dy, (0, _mul.mul)((0, _sum.sum)(dy, axis, keepDims), softmax));
        };
        return {
            value,
            gradFunc
        };
    });
    return customOp($logits);
// TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this.
// const inputs: LogSoftmaxInputs = {logits: $logits};
// const attrs: LogSoftmaxAttrs = {axis};
// return ENGINE.runKernel(
//            LogSoftmax, inputs as unknown as NamedTensorMap,
//            attrs as unknown as NamedAttrMap);
}
const logSoftmax = /* @__PURE__ */ (0, _operation.op)({
    logSoftmax_
});

},{"../gradients":"dDHpg","../tensor_util_env":"6vfHI","./cast":"b4VDA","./exp":"1CsGu","./log":"iex5O","./max":"cb6Hb","./mul":"fOWrC","./operation":"ip64W","./sub":"jBBQ2","./sum":"5awd0","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jBBQ2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sub", ()=>sub);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([10, 20, 30, 40]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 *
 * a.sub(b).print();  // or tf.sub(a, b)
 * ```
 *
 * ```js
 * // Broadcast subtract a with b.
 * const a = tf.tensor1d([10, 20, 30, 40]);
 * const b = tf.scalar(5);
 *
 * a.sub(b).print();  // or tf.sub(a, b)
 * ```
 * @param a The first `tf.Tensor` to subtract from.
 * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
 * `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function sub_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "sub");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "sub");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Sub), inputs);
}
const sub = /* @__PURE__ */ (0, _operation.op)({
    sub_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"feRCM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logSumExp", ()=>logSumExp);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _add = require("./add");
var _axisUtil = require("./axis_util");
var _exp = require("./exp");
var _log = require("./log");
var _max = require("./max");
var _operation = require("./operation");
var _reshape = require("./reshape");
var _sub = require("./sub");
var _sum = require("./sum");
/**
 * Computes the log(sum(exp(elements across the reduction dimensions))).
 *
 * Reduces the input along the dimensions given in `axis`. Unless `keepDims`
 * is true, the rank of the array is reduced by 1 for each entry in `axis`.
 * If `keepDims` is true, the reduced dimensions are retained with length 1.
 * If `axis` has no entries, all dimensions are reduced, and an array with a
 * single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.logSumExp().print();  // or tf.logSumExp(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * const axis = 1;
 * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)
 * ```
 * @param x The input tensor.
 * @param axis The dimension(s) to reduce. If null (the default),
 *     reduces all dimensions.
 * @param keepDims If true, retains reduced dimensions with length
 *     of 1. Defaults to false.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function logSumExp_(x, axis = null, keepDims = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "logSumExp");
    const axes = (0, _util.parseAxisParam)(axis, $x.shape);
    const xMax = (0, _max.max)($x, axes, true);
    const a = (0, _sub.sub)($x, xMax);
    const b = (0, _exp.exp)(a);
    const c = (0, _sum.sum)(b, axes);
    const d = (0, _log.log)(c);
    const res = (0, _add.add)((0, _reshape.reshape)(xMax, d.shape), d);
    if (keepDims) {
        const newShape = (0, _axisUtil.expandShapeToKeepDim)(res.shape, axes);
        return (0, _reshape.reshape)(res, newShape);
    }
    return res;
}
const logSumExp = /* @__PURE__ */ (0, _operation.op)({
    logSumExp_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./add":"aynh7","./axis_util":"hzS4B","./exp":"1CsGu","./log":"iex5O","./max":"cb6Hb","./operation":"ip64W","./reshape":"ew3wZ","./sub":"jBBQ2","./sum":"5awd0","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6zYjJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalAnd", ()=>logicalAnd);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of `a AND b` element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([false, false, true, true], 'bool');
 * const b = tf.tensor1d([false, true, false, true], 'bool');
 *
 * a.logicalAnd(b).print();
 * ```
 *
 * @param a The first input tensor. Must be of dtype bool.
 * @param b The second input tensor. Must be of dtype bool.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function logicalAnd_(a, b) {
    const $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "logicalAnd", "bool");
    const $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "logicalAnd", "bool");
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.LogicalAnd), inputs);
}
const logicalAnd = /* @__PURE__ */ (0, _operation.op)({
    logicalAnd_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gSuOh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalNot", ()=>logicalNot);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns the truth value of `NOT x` element-wise.
 *
 * ```js
 * const a = tf.tensor1d([false, true], 'bool');
 *
 * a.logicalNot().print();
 * ```
 *
 * @param x The input tensor. Must be of dtype 'bool'.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function logicalNot_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "logicalNot", "bool");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.LogicalNot), inputs);
}
const logicalNot = /* @__PURE__ */ (0, _operation.op)({
    logicalNot_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dH6xG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalOr", ()=>logicalOr);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of `a OR b` element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([false, false, true, true], 'bool');
 * const b = tf.tensor1d([false, true, false, true], 'bool');
 *
 * a.logicalOr(b).print();
 * ```
 * @param a The first input tensor. Must be of dtype bool.
 * @param b The second input tensor. Must be of dtype bool.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function logicalOr_(a, b) {
    const $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "logicalOr", "bool");
    const $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "logicalOr", "bool");
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.LogicalOr), inputs);
}
const logicalOr = /* @__PURE__ */ (0, _operation.op)({
    logicalOr_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7Y9m5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalXor", ()=>logicalXor);
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _logicalAnd = require("./logical_and");
var _logicalNot = require("./logical_not");
var _logicalOr = require("./logical_or");
var _operation = require("./operation");
/**
 * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([false, false, true, true], 'bool');
 * const b = tf.tensor1d([false, true, false, true], 'bool');
 *
 * a.logicalXor(b).print();
 * ```
 *
 * @param a The first input tensor. Must be of dtype bool.
 * @param b The second input tensor. Must be of dtype bool.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function logicalXor_(a, b) {
    const $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "logicalXor", "bool");
    const $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "logicalXor", "bool");
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    // x ^ y = (x | y) & ~(x & y)
    return (0, _logicalAnd.logicalAnd)((0, _logicalOr.logicalOr)(a, b), (0, _logicalNot.logicalNot)((0, _logicalAnd.logicalAnd)(a, b)));
}
const logicalXor = /* @__PURE__ */ (0, _operation.op)({
    logicalXor_
});

},{"../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./logical_and":"6zYjJ","./logical_not":"gSuOh","./logical_or":"dH6xG","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2XtFq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Searches for where a value would go in a sorted sequence.
 *
 * This is not a method for checking containment (like javascript in).
 *
 * The typical use case for this operation is "binning", "bucketing", or
 * "discretizing". The values are assigned to bucket-indices based on the edges
 * listed in 'sortedSequence'. This operation returns the bucket-index for each
 * value.
 *
 * The index returned corresponds to the first edge greater than or equal to the
 * value.
 *
 * The axis is not settable for this operation. It always operates on the
 * innermost dimension (axis=-1). The operation will accept any number of outer
 * dimensions.
 *
 * Note: This operation assumes that 'lowerBound' is sorted along the
 * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not
 * sorted no error is raised and the content of the returned tensor is not well
 * defined.
 *
 * ```js
 * const edges = tf.tensor1d([-1, 3.3, 9.1, 10.0]);
 * let values = tf.tensor1d([0.0, 4.1, 12.0]);
 * const result1 = tf.lowerBound(edges, values);
 * result1.print(); // [1, 2, 4]
 *
 * const seq = tf.tensor1d([0, 3, 9, 10, 10]);
 * values = tf.tensor1d([0, 4, 10]);
 * const result2 = tf.lowerBound(seq, values);
 * result2.print(); // [0, 2, 3]
 *
 * const sortedSequence = tf.tensor2d([[0., 3., 8., 9., 10.],
 *                                     [1., 2., 3., 4., 5.]]);
 * values = tf.tensor2d([[9.8, 2.1, 4.3],
 *                       [0.1, 6.6, 4.5, ]]);
 * const result3 = tf.lowerBound(sortedSequence, values);
 * result3.print(); // [[4, 1, 2], [0, 5, 4]]
 * ```
 * @param sortedSequence: N-D. Sorted sequence.
 * @param values: N-D. Search values.
 * @return An N-D int32 tensor the size of values containing the result of
 *     applying lower bound to each value. The result is not a global index to
 *     the entire Tensor, but the index in the last dimension.
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */ parcelHelpers.export(exports, "lowerBound", ()=>lowerBound);
var _searchSorted = require("./search_sorted");
function lowerBound(sortedSequence, values) {
    return (0, _searchSorted.searchSorted)(sortedSequence, values, "left");
}

},{"./search_sorted":"4gaGh","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4gaGh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "searchSorted", ()=>searchSorted);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _utilBase = require("../util_base");
var _operation = require("./operation");
var _reshape = require("./reshape");
const INT32_MAX = 2147483648;
/**
 * Searches for where a value would go in a sorted sequence.
 *
 * This is not a method for checking containment (like javascript in).
 *
 * The typical use case for this operation is "binning", "bucketing", or
 * "discretizing". The values are assigned to bucket-indices based on the edges
 * listed in 'sortedSequence'. This operation returns the bucket-index for each
 * value.
 *
 * The side argument controls which index is returned if a value lands exactly
 * on an edge.
 *
 * The axis is not settable for this operation. It always operates on the
 * innermost dimension (axis=-1). The operation will accept any number of outer
 * dimensions.
 *
 * Note: This operation assumes that 'sortedSequence' is sorted along the
 * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not
 * sorted no error is raised and the content of the returned tensor is not well
 * defined.
 *
 * ```js
 * const edges = tf.tensor1d([-1, 3.3, 9.1, 10.0]);
 * let values = tf.tensor1d([0.0, 4.1, 12.0]);
 * const result1 = tf.searchSorted(edges, values, 'left');
 * result1.print(); // [1, 2, 4]
 *
 * const seq = tf.tensor1d([0, 3, 9, 10, 10]);
 * values = tf.tensor1d([0, 4, 10]);
 * const result2 = tf.searchSorted(seq, values, 'left');
 * result2.print(); // [0, 2, 3]
 * const result3 = tf.searchSorted(seq, values, 'right');
 * result3.print(); // [1, 2, 5]
 *
 * const sortedSequence = tf.tensor2d([[0., 3., 8., 9., 10.],
 *                                     [1., 2., 3., 4., 5.]]);
 * values = tf.tensor2d([[9.8, 2.1, 4.3],
 *                       [0.1, 6.6, 4.5, ]]);
 * const result4 = tf.searchSorted(sortedSequence, values, 'left');
 * result4.print(); // [[4, 1, 2], [0, 5, 4]]
 * ```
 * @param sortedSequence: N-D. Sorted sequence.
 * @param values: N-D. Search values.
 * @param side: 'left'|'right'. Defaults to 'left'. 'left' corresponds to lower
 *     bound and 'right' to upper bound.
 * @return An N-D int32 tensor the size of values containing the result of
 *     applying either lower bound or upper bound (depending on side) to each
 *     value. The result is not a global index to the entire Tensor, but the
 *     index in the last dimension.
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */ function searchSorted_(sortedSequence, values, side = "left") {
    const $sortedSequence = (0, _tensorUtilEnv.convertToTensor)(sortedSequence, "sortedSequence", "searchSorted");
    const $values = (0, _tensorUtilEnv.convertToTensor)(values, "values", "searchSorted");
    const sequenceSize = $sortedSequence.shape[$sortedSequence.shape.length - 1];
    const valuesSize = $values.shape[$values.shape.length - 1];
    const $sortedSequence2D = (0, _reshape.reshape)($sortedSequence, [
        -1,
        sequenceSize
    ]);
    const $values2D = (0, _reshape.reshape)($values, [
        -1,
        valuesSize
    ]);
    if ($sortedSequence2D.rank < 2) throw new Error(`Sorted input argument must be at least 2-dimensional`);
    if ($sortedSequence2D.shape[0] !== $values2D.shape[0]) throw new Error(`Leading dimension of 'sortedSequence' and 'values' must match.`);
    if ((0, _utilBase.sizeFromShape)($values2D.shape) >= INT32_MAX) throw new Error(`values tensor size must less than ${INT32_MAX}`);
    if ($sortedSequence2D.shape[1] >= INT32_MAX) throw new Error(`trailing dim_size must less than ${INT32_MAX} for int32 output type, was ${$sortedSequence2D.shape[1]}`);
    const inputs = {
        sortedSequence: $sortedSequence2D,
        values: $values2D
    };
    const attrs = {
        side
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SearchSorted), inputs, attrs);
}
const searchSorted = /* @__PURE__ */ (0, _operation.op)({
    searchSorted_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util_base":"4Vo4a","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hx37d":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool", ()=>maxPool);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the 2D max pooling of an image.
 *
 * @param x The input tensor, of rank 4 or rank 3 of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
 * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
 *     `filterSize` is a single number, then `filterHeight == filterWidth`.
 * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
 *     `strides` is a single number, then `strideHeight == strideWidth`.
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 */ function maxPool_(x, filterSize, strides, pad, dimRoundingMode) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "maxPool");
    const dilations = 1;
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    _util.assert(x4D.rank === 4, ()=>`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
    _util.assert(_convUtil.eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in maxPool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    _convUtil.checkPadOnDimRoundingMode("maxPool", pad, dimRoundingMode);
    const inputs = {
        x: x4D
    };
    const attrs = {
        filterSize,
        strides,
        pad,
        dimRoundingMode
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.MaxPool), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const maxPool = /* @__PURE__ */ (0, _operation.op)({
    maxPool_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"z14HA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool3d", ()=>maxPool3d);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the 3D max pooling.
 *
 * ```js
 * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
 * const result = tf.maxPool3d(x, 2, 1, 'valid');
 * result.print();
 * ```
 *
 * @param x The input tensor, of rank 5 or rank 4 of shape
 *     `[batch, depth, height, width, inChannels]`.
 * @param filterSize The filter size:
 *     `[filterDepth, filterHeight, filterWidth]`.
 *     If `filterSize` is a single number,
 *     then `filterDepth == filterHeight == filterWidth`.
 * @param strides The strides of the pooling:
 *     `[strideDepth, strideHeight, strideWidth]`.
 *     If `strides` is a single number,
 *     then `strideDepth == strideHeight == strideWidth`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1*1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
 *     "NDHWC". Specify the data format of the input and output data. With the
 *     default format "NDHWC", the data is stored in the order of: [batch,
 *     depth, height, width, channels]. Only "NDHWC" is currently supported.
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function maxPool3d_(x, filterSize = [
    1,
    1,
    1
], strides, pad, dimRoundingMode, dataFormat = "NDHWC") {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "maxPool3d");
    let x5D = $x;
    let reshapedTo5D = false;
    if ($x.rank === 4) {
        reshapedTo5D = true;
        x5D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2],
            $x.shape[3]
        ]);
    }
    _util.assert(x5D.rank === 5, ()=>`Error in maxPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
    _util.assert(dataFormat === "NDHWC", ()=>`Error in maxPool3d: Only NDHWC is currently supported, ` + `but got dataFormat of ${dataFormat}`);
    (0, _convUtil.checkPadOnDimRoundingMode)("maxPool3d", pad, dimRoundingMode);
    const inputs = {
        x: x5D
    };
    const attrs = {
        filterSize,
        strides,
        pad,
        dimRoundingMode,
        dataFormat
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.MaxPool3D), inputs, attrs);
    if (reshapedTo5D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3],
        res.shape[4]
    ]);
    return res;
}
const maxPool3d = /* @__PURE__ */ (0, _operation.op)({
    maxPool3d_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hPUnJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPoolWithArgmax", ()=>maxPoolWithArgmax);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the 2D max pooling of an image with Argmax index.
 * The indices in argmax are flattened, so that a maximum value at position `[b,
 * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if
 * include_batch_in_index is False; `((b * height + y) * width + x) * channels
 * +c` if include_batch_in_index is True.
 *
 * The indices returned are always in `[0, height) x [0, width)` before
 * flattening.
 *
 * @param x The input tensor, of rank 4 or rank 3 of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
 * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
 *     `filterSize` is a single number, then `filterHeight == filterWidth`.
 * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
 *     `strides` is a single number, then `strideHeight == strideWidth`.
 * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
 *     "NDHWC". Specify the data format of the input and output data. With the
 *     default format "NDHWC", the data is stored in the order of: [batch,
 *     depth, height, width, channels]. Only "NDHWC" is currently supported.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param includeBatchIndex Defaults to False. Whether to include batch
 *    dimension in flattened index of argmax.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "maxPoolWithArgmax");
    const inputs = {
        x: $x
    };
    const attrs = {
        filterSize,
        strides,
        pad,
        includeBatchInIndex
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.MaxPoolWithArgmax), inputs, attrs);
    return {
        result: result[0],
        indexes: result[1]
    };
}
const maxPoolWithArgmax = /* @__PURE__ */ (0, _operation.op)({
    maxPoolWithArgmax_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1nFpL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maximum", ()=>maximum);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _cast = require("./cast");
var _operation = require("./operation");
/**
 * Returns the max of a and b (`a > b ? a : b`) element-wise.
 * Supports broadcasting.
 *
 * We also expose `tf.maximumStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.maximum(b).print();  // or tf.maximum(a, b)
 * ```
 *
 * ```js
 * // Broadcast maximum a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.maximum(b).print();  // or tf.maximum(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function maximum_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "maximum");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "maximum");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    if ($a.dtype === "bool") {
        $a = (0, _cast.cast)($a, "int32");
        $b = (0, _cast.cast)($b, "int32");
    }
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Maximum), inputs);
}
const maximum = /* @__PURE__ */ (0, _operation.op)({
    maximum_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./cast":"b4VDA","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hzzLs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mean", ()=>mean);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the mean of elements across dimensions of a `tf.Tensor`.
 *
 * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is
 * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.
 * If `keepDims` is true, the reduced dimensions are retained with length 1.
 * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with
 * a single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.mean().print();  // or tf.mean(a)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * const axis = 1;
 * x.mean(axis).print();  // or tf.mean(x, axis)
 * ```
 *
 * @param x The input tensor.
 * @param axis The dimension(s) to reduce. By default it reduces
 *     all dimensions.
 * @param keepDims If true, retains reduced dimensions with size 1.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function mean_(x, axis = null, keepDims = false) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "mean");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        keepDims
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Mean), inputs, attrs);
}
const mean = /* @__PURE__ */ (0, _operation.op)({
    mean_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jFI6G":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Broadcasts parameters for evaluation on an N-D grid.
 *
 * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs`
 * of N-D coordinate arrays for evaluating expressions on an N-D grid.
 *
 * Notes:
 * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions.
 * When the `indexing` argument is set to 'xy' (the default), the broadcasting
 * instructions for the first two dimensions are swapped.
 * Examples:
 * Calling `const [X, Y] = meshgrid(x, y)` with the tensors
 *
 * ```javascript
 * const x = [1, 2, 3];
 * const y = [4, 5, 6];
 * const [X, Y] = tf.meshgrid(x, y);
 * // X = [[1, 2, 3],
 * //      [1, 2, 3],
 * //      [1, 2, 3]]
 * // Y = [[4, 4, 4],
 * //      [5, 5, 5],
 * //      [6, 6, 6]]
 * ```
 *
 * @param x Tensor with rank geq 1.
 * @param y Tensor with rank geq 1.
 * @param indexing
 *
 * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
 */ parcelHelpers.export(exports, "meshgrid", ()=>meshgrid);
var _matMul = require("./mat_mul");
var _ones = require("./ones");
var _reshape = require("./reshape");
var _tensor = require("../tensor");
var _tensorUtilEnv = require("../tensor_util_env");
var _utilBase = require("../util_base");
function meshgrid(x, y, { indexing = "xy" } = {}) {
    if (indexing !== "xy" && indexing !== "ij") throw new TypeError(`${indexing} is not a valid third argument to meshgrid`);
    if (x === undefined) return [];
    let $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "meshgrid", x instanceof (0, _tensor.Tensor) ? x.dtype : "float32");
    if (y === undefined) return [
        $x
    ];
    let $y = (0, _tensorUtilEnv.convertToTensor)(y, "y", "meshgrid", y instanceof (0, _tensor.Tensor) ? y.dtype : "float32");
    const w = (0, _utilBase.sizeFromShape)($x.shape);
    const h = (0, _utilBase.sizeFromShape)($y.shape);
    if (indexing === "xy") {
        $x = (0, _reshape.reshape)($x, [
            1,
            -1
        ]);
        $y = (0, _reshape.reshape)($y, [
            -1,
            1
        ]);
        return [
            (0, _matMul.matMul)((0, _ones.ones)([
                h,
                1
            ], $x.dtype), $x),
            (0, _matMul.matMul)($y, (0, _ones.ones)([
                1,
                w
            ], $y.dtype))
        ];
    }
    $x = (0, _reshape.reshape)($x, [
        -1,
        1
    ]);
    $y = (0, _reshape.reshape)($y, [
        1,
        -1
    ]);
    return [
        (0, _matMul.matMul)($x, (0, _ones.ones)([
            1,
            h
        ], $x.dtype)),
        (0, _matMul.matMul)((0, _ones.ones)([
            w,
            1
        ], $y.dtype), $y)
    ];
}

},{"./mat_mul":"fiMp4","./ones":"2gdnE","./reshape":"ew3wZ","../tensor":"cJNoh","../tensor_util_env":"6vfHI","../util_base":"4Vo4a","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2gdnE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates a `tf.Tensor` with all elements set to 1.
 *
 * ```js
 * tf.ones([2, 2]).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param dtype The type of an element in the resulting tensor. Defaults to
 *     'float'.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "ones", ()=>ones);
var _engine = require("../engine");
var _util = require("../util");
var _utilBase = require("../util_base");
var _complex = require("./complex");
var _zeros = require("./zeros");
function ones(shape, dtype = "float32") {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    if (dtype === "complex64") {
        const real = ones(shape, "float32");
        const imag = (0, _zeros.zeros)(shape, "float32");
        return (0, _complex.complex)(real, imag);
    }
    const values = (0, _util.makeOnesTypedArray)((0, _util.sizeFromShape)(shape), dtype);
    return (0, _engine.ENGINE).makeTensor(values, shape, dtype);
}

},{"../engine":"1g2qo","../util":"ctavo","../util_base":"4Vo4a","./complex":"bPDAx","./zeros":"fcPHR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fcPHR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates a `tf.Tensor` with all elements set to 0.
 *
 * ```js
 * tf.zeros([2, 2]).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param dtype The type of an element in the resulting tensor. Can
 *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "zeros", ()=>zeros);
var _engine = require("../engine");
var _util = require("../util");
var _complex = require("./complex");
function zeros(shape, dtype = "float32") {
    (0, _util.assertNonNegativeIntegerDimensions)(shape);
    if (dtype === "complex64") {
        const real = zeros(shape, "float32");
        const imag = zeros(shape, "float32");
        return (0, _complex.complex)(real, imag);
    }
    const values = (0, _util.makeZerosTypedArray)((0, _util.sizeFromShape)(shape), dtype);
    return (0, _engine.ENGINE).makeTensor(values, shape, dtype);
}

},{"../engine":"1g2qo","../util":"ctavo","./complex":"bPDAx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"guwat":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "minimum", ()=>minimum);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _cast = require("./cast");
var _operation = require("./operation");
/**
 * Returns the min of a and b (`a < b ? a : b`) element-wise.
 * Supports broadcasting.
 *
 * We also expose `minimumStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.minimum(b).print();  // or tf.minimum(a, b)
 * ```
 *
 * ```js
 * // Broadcast minimum a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.minimum(b).print();  // or tf.minimum(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function minimum_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "minimum");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "minimum");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    if ($a.dtype === "bool") {
        $a = (0, _cast.cast)($a, "int32");
        $b = (0, _cast.cast)($b, "int32");
    }
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Minimum), inputs);
}
const minimum = /* @__PURE__ */ (0, _operation.op)({
    minimum_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./cast":"b4VDA","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1Sy5n":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mirrorPad", ()=>mirrorPad);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Pads a `tf.Tensor` using mirror padding.
 *
 * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad.
 *
 * ```js
 * const x = tf.range(0, 9).reshape([1, 1, 3, 3]);
 * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print();
 * ```
 * @param x The tensor to pad.
 * @param paddings An array of length `R` (the rank of the tensor), where
 * each element is a length-2 tuple of ints `[padBefore, padAfter]`,
 * specifying how much to pad along each dimension of the tensor.
 * In "reflect" mode, the padded regions do not include the borders,
 * while in "symmetric" mode the padded regions do include the borders.
 * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`,
 * then the output is `[1, 2, 3, 2, 1]` in "reflect" mode, and
 * `[1, 2, 3, 3, 2]` in "symmetric" mode.
 * If `mode` is "reflect" then both `paddings[D, 0]` and `paddings[D, 1]`
 * must be no greater than `x.shape[D] - 1`. If mode is "symmetric"
 * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than
 * `x.shape[D]`
 * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'`
 */ /** @doc {heading: 'Tensors', subheading: 'Transformations'} */ function mirrorPad_(x, paddings, mode) {
    _util.assert(mode === "reflect" || mode === "symmetric", ()=>`Invalid mode. Mode must be either reflect or symmetric. ` + `Got ${mode}.`);
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "mirrorPad");
    if ($x.rank === 0) throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
    _util.assert(paddings.length === $x.rank, ()=>`Padding doesn't match input. Must be ${$x.rank}. ` + `Got ${paddings.length}.`);
    const shapeOffset = mode === "reflect" ? 1 : 0;
    for(let i = 0; i < $x.rank; i++){
        _util.assert(paddings[i].length === 2, ()=>`Invalid number of paddings. Must be length of 2 each.`);
        _util.assert(paddings[i][0] >= 0 && paddings[i][0] <= $x.shape[i] - shapeOffset && paddings[i][1] >= 0 && paddings[i][1] <= $x.shape[i] - shapeOffset, ()=>`Padding in dimension ${i} cannot be greater than or equal ` + `to ${$x.shape[i] - shapeOffset} or less than 0 for input of ` + `shape ${$x.shape}`);
    }
    const attrs = {
        paddings,
        mode
    };
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.MirrorPad), inputs, attrs);
}
const mirrorPad = /* @__PURE__ */ (0, _operation.op)({
    mirrorPad_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g5efu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mod", ()=>mod);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns the mod of a and b element-wise.
 * `floor(x / y) * y + mod(x, y) = x`
 * Supports broadcasting.
 *
 * We also expose `tf.modStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.mod(b).print();  // or tf.mod(a, b)
 * ```
 *
 * ```js
 * // Broadcast a mod b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.mod(b).print();  // or tf.mod(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function mod_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "mod");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "mod");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Mod), inputs);
}
const mod = /* @__PURE__ */ (0, _operation.op)({
    mod_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ef1IE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "moments", ()=>moments);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _axisUtil = require("./axis_util");
var _cast = require("./cast");
var _mean = require("./mean");
var _operation = require("./operation");
var _reshape = require("./reshape");
var _square = require("./square");
var _sub = require("./sub");
/**
 * Calculates the mean and variance of `x`. The mean and variance are
 * calculated by aggregating the contents of `x` across `axes`. If `x` is
 * 1-D and `axes = [0]` this is just the mean and variance of a vector.
 *
 * @param x The input tensor.
 * @param axis The dimension(s) along with to compute mean and
 *     variance. By default it reduces all dimensions.
 * @param keepDims If true, the moments have the same dimensionality as the
 *     input.
 * @return An object with two keys: `mean` and `variance`.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */ function moments_(x, axis = null, keepDims = false) {
    x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "moments");
    const axes = (0, _util.parseAxisParam)(axis, x.shape);
    const xMean = (0, _mean.mean)(x, axes, keepDims);
    let keepDimsShape = xMean.shape;
    if (!keepDims) keepDimsShape = (0, _axisUtil.expandShapeToKeepDim)(xMean.shape, axes);
    const devSquared = (0, _square.square)((0, _sub.sub)((0, _cast.cast)(x, "float32"), (0, _reshape.reshape)(xMean, keepDimsShape)));
    const variance = (0, _mean.mean)(devSquared, axes, keepDims);
    return {
        mean: xMean,
        variance
    };
}
const moments = /* @__PURE__ */ (0, _operation.op)({
    moments_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./axis_util":"hzS4B","./cast":"b4VDA","./mean":"hzzLs","./operation":"ip64W","./reshape":"ew3wZ","./square":"cOosW","./sub":"jBBQ2","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lNZ3B":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "multiRNNCell", ()=>multiRNNCell);
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the next states and outputs of a stack of LSTMCells.
 *
 * Each cell output is used as input to the next cell.
 *
 * Returns `[cellState, cellOutput]`.
 *
 * Derived from tf.contrib.rn.MultiRNNCell.
 *
 * @param lstmCells Array of LSTMCell functions.
 * @param data The input to the cell.
 * @param c Array of previous cell states.
 * @param h Array of previous cell outputs.
 *
 * @doc {heading: 'Operations', subheading: 'RNN'}
 */ function multiRNNCell_(lstmCells, data, c, h) {
    const $data = (0, _tensorUtilEnv.convertToTensor)(data, "data", "multiRNNCell");
    const $c = (0, _tensorUtilEnv.convertToTensorArray)(c, "c", "multiRNNCell");
    const $h = (0, _tensorUtilEnv.convertToTensorArray)(h, "h", "multiRNNCell");
    let input = $data;
    const newStates = [];
    for(let i = 0; i < lstmCells.length; i++){
        const output = lstmCells[i](input, $c[i], $h[i]);
        newStates.push(output[0]);
        newStates.push(output[1]);
        input = output[1];
    }
    const newC = [];
    const newH = [];
    for(let i = 0; i < newStates.length; i += 2){
        newC.push(newStates[i]);
        newH.push(newStates[i + 1]);
    }
    return [
        newC,
        newH
    ];
}
const multiRNNCell = /* @__PURE__ */ (0, _operation.op)({
    multiRNNCell_
});

},{"../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g1s8L":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "multinomial", ()=>multinomial);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Creates a `tf.Tensor` with values drawn from a multinomial distribution.
 *
 * ```js
 * const probs = tf.tensor([.75, .25]);
 * tf.multinomial(probs, 3).print();
 * ```
 *
 * @param logits 1D array with unnormalized log-probabilities, or
 *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`
 *     parameter.
 * @param numSamples Number of samples to draw for each row slice.
 * @param seed The seed number.
 * @param normalized Whether the provided `logits` are normalized true
 *     probabilities (sum to 1). Defaults to false.
 * @return 1D array of shape `[numSamples]`, or 2D array of shape
 *     `[batchSize, numSamples]`, depending on the rank of the input.
 *
 * @doc {heading: 'Tensors', subheading: 'Random'}
 */ function multinomial_(logits, numSamples, seed, normalized = false) {
    const $logits = (0, _tensorUtilEnv.convertToTensor)(logits, "logits", "multinomial");
    const numOutcomes = $logits.size;
    const origRank = $logits.rank;
    if (numOutcomes < 2) throw new Error(`Error in multinomial: you need at least 2 outcomes, but got ` + `${numOutcomes}.`);
    if (origRank > 2) throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);
    // TODO(lina128): Investigate correct seed behavior. The code seems not allow
    // setting see to 0.
    seed = seed || Math.random();
    // The kernel only accepts (and returns) rank 2 tensors.
    const logits2D = origRank === 1 ? (0, _reshape.reshape)($logits, [
        1,
        -1
    ]) : $logits;
    const inputs = {
        logits: logits2D
    };
    const attrs = {
        numSamples,
        seed,
        normalized
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.Multinomial), inputs, attrs);
    // tslint:disable-next-line:no-unnecessary-type-assertion
    return origRank === 1 ? (0, _reshape.reshape)(res, [
        res.size
    ]) : res;
}
const multinomial = /* @__PURE__ */ (0, _operation.op)({
    multinomial_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gUKb0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "notEqual", ()=>notEqual);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns the truth value of (a != b) element-wise. Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 * const b = tf.tensor1d([0, 2, 3]);
 *
 * a.notEqual(b).print();
 * ```
 * @param a The first input tensor.
 * @param b The second input tensor. Must have the same dtype as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ function notEqual_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "notEqual", "string_or_numeric");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "notEqual", "string_or_numeric");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.NotEqual), inputs);
}
const notEqual = /* @__PURE__ */ (0, _operation.op)({
    notEqual_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"91jNM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "oneHot", ()=>oneHot);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take
 * value `onValue` (defaults to 1), while all other locations take value
 * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank
 * `R+1` with the last axis of size `depth`.
 * `indices` used to encode prediction class must start from 0. For example,
 *  if you have 3 classes of data, class 1 should be encoded as 0, class 2
 *  should be 1, and class 3 should be 2.
 *
 * ```js
 * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();
 * ```
 *
 * @param indices `tf.Tensor` of indices with dtype `int32`. Indices must
 * start from 0.
 * @param depth The depth of the one hot dimension.
 * @param onValue A number used to fill in the output when the index matches
 * the location.
 * @param offValue A number used to fill in the output when the index does
 *     not match the location.
 * @param dtype The dtype of the output tensor, default to 'int32'.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function oneHot_(indices, depth, onValue = 1, offValue = 0, dtype = "int32") {
    if (depth < 2) throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "oneHot", "int32");
    const inputs = {
        indices: $indices
    };
    const attrs = {
        dtype,
        depth,
        onValue,
        offValue
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.OneHot), inputs, attrs);
}
const oneHot = /* @__PURE__ */ (0, _operation.op)({
    oneHot_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7TDfu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "onesLike", ()=>onesLike);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the
 * given tensor.
 *
 * ```js
 * const x = tf.tensor([1, 2]);
 * tf.onesLike(x).print();
 * ```
 * @param x A tensor.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function onesLike_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "onesLike");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.OnesLike), inputs);
}
const onesLike = /* @__PURE__ */ (0, _operation.op)({
    onesLike_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"emoMO":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "outerProduct", ()=>outerProduct);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _matMul = require("./mat_mul");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the outer product of two vectors, `v1` and `v2`.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 * const b = tf.tensor1d([3, 4, 5]);
 *
 * tf.outerProduct(a, b).print();
 * ```
 * @param v1 The first vector in the outer product operation.
 * @param v2 The second vector in the outer product operation.
 *
 * @doc {heading: 'Operations', subheading: 'Matrices'}
 */ function outerProduct_(v1, v2) {
    const $v1 = (0, _tensorUtilEnv.convertToTensor)(v1, "v1", "outerProduct");
    const $v2 = (0, _tensorUtilEnv.convertToTensor)(v2, "v2", "outerProduct");
    _util.assert($v1.rank === 1 && $v2.rank === 1, ()=>`Error in outerProduct: inputs must be rank 1, but got ranks ` + `${$v1.rank} and ${$v2.rank}.`);
    const v12D = (0, _reshape.reshape)($v1, [
        -1,
        1
    ]);
    const v22D = (0, _reshape.reshape)($v2, [
        1,
        -1
    ]);
    return (0, _matMul.matMul)(v12D, v22D);
}
const outerProduct = /* @__PURE__ */ (0, _operation.op)({
    outerProduct_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./mat_mul":"fiMp4","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"89mWw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pad", ()=>pad);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Pads a `tf.Tensor` with a given value and paddings.
 *
 * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`,
 * refer to `tf.mirrorPad`.
 *
 * Also available are stricter rank-specific methods with the same signature
 * as this method that assert that `paddings` is of given length.
 *   - `tf.pad1d`
 *   - `tf.pad2d`
 *   - `tf.pad3d`
 *   - `tf.pad4d`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 * x.pad([[1, 2]]).print();
 * ```
 * @param x The tensor to pad.
 * @param paddings An array of length `R` (the rank of the tensor), where
 * each element is a length-2 tuple of ints `[padBefore, padAfter]`,
 * specifying how much to pad along each dimension of the tensor.
 * @param constantValue The pad value to use. Defaults to 0.
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function pad_(x, paddings, constantValue = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "pad");
    if ($x.rank === 0) throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
    const attrs = {
        paddings,
        constantValue
    };
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.PadV2), inputs, attrs);
}
const pad = /* @__PURE__ */ (0, _operation.op)({
    pad_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gOSFg":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pad1d", ()=>pad1d);
var _util = require("../util");
var _operation = require("./operation");
var _pad = require("./pad");
/**
 * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.
 */ function pad1d_(x, paddings, constantValue = 0) {
    (0, _util.assert)(paddings.length === 2, ()=>"Invalid number of paddings. Must be length of 2.");
    return (0, _pad.pad)(x, [
        paddings
    ], constantValue);
}
const pad1d = /* @__PURE__ */ (0, _operation.op)({
    pad1d_
});

},{"../util":"ctavo","./operation":"ip64W","./pad":"89mWw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2QXo6":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pad2d", ()=>pad2d);
var _util = require("../util");
var _operation = require("./operation");
var _pad = require("./pad");
/**
 * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.
 */ function pad2d_(x, paddings, constantValue = 0) {
    (0, _util.assert)(paddings.length === 2 && paddings[0].length === 2 && paddings[1].length === 2, ()=>"Invalid number of paddings. Must be length of 2 each.");
    return (0, _pad.pad)(x, paddings, constantValue);
}
const pad2d = /* @__PURE__ */ (0, _operation.op)({
    pad2d_
});

},{"../util":"ctavo","./operation":"ip64W","./pad":"89mWw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6eqEc":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pad3d", ()=>pad3d);
var _util = require("../util");
var _operation = require("./operation");
var _pad = require("./pad");
/**
 * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.
 */ function pad3d_(x, paddings, constantValue = 0) {
    (0, _util.assert)(paddings.length === 3 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2, ()=>"Invalid number of paddings. Must be length of 2 each.");
    return (0, _pad.pad)(x, paddings, constantValue);
}
const pad3d = /* @__PURE__ */ (0, _operation.op)({
    pad3d_
});

},{"../util":"ctavo","./operation":"ip64W","./pad":"89mWw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dP5zO":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pad4d", ()=>pad4d);
var _util = require("../util");
var _operation = require("./operation");
var _pad = require("./pad");
/**
 * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.
 */ function pad4d_(x, paddings, constantValue = 0) {
    (0, _util.assert)(paddings.length === 4 && paddings[0].length === 2 && paddings[1].length === 2 && paddings[2].length === 2 && paddings[3].length === 2, ()=>"Invalid number of paddings. Must be length of 2 each.");
    return (0, _pad.pad)(x, paddings, constantValue);
}
const pad4d = /* @__PURE__ */ (0, _operation.op)({
    pad4d_
});

},{"../util":"ctavo","./operation":"ip64W","./pad":"89mWw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"sCTfp":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pool", ()=>pool);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _avgPool = require("./avg_pool");
var _batchToSpaceNd = require("./batch_to_space_nd");
var _convUtil = require("./conv_util");
var _maxPool = require("./max_pool");
var _operation = require("./operation");
var _reshape = require("./reshape");
var _spaceToBatchNd = require("./space_to_batch_nd");
/**
 * Performs an N-D pooling operation
 *
 * @param input The input tensor, of rank 4 or rank 3 of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
 * @param windowShape The filter size: `[filterHeight, filterWidth]`. If
 *     `filterSize` is a single number, then `filterHeight == filterWidth`.
 * @param poolingType The type of pooling, either 'max' or 'avg'.
 * @param pad The type of padding algorithm:
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
 *         https://www.tensorflow.org/api_guides/python/nn#Convolution)
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
 *     `strides` is a single number, then `strideHeight == strideWidth`.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function pool_(input, windowShape, poolingType, pad, dilations, strides, dimRoundingMode) {
    if (dilations == null) dilations = [
        1,
        1
    ];
    if (strides == null) strides = 1;
    if (pad === 0) pad = "valid";
    const $x = (0, _tensorUtilEnv.convertToTensor)(input, "x", "maxPool");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    _util.assert(_convUtil.eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in pool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = _convUtil.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);
    const dilation = [
        convInfo.dilationHeight,
        convInfo.dilationWidth
    ];
    // The following implementation does batchToSpace(pool(spaceToBatch(x)))
    // whenever dilation > 1 since the TF kernels do not support dilation > 1.
    // tslint:disable-next-line:max-line-length
    // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037
    let basePadding;
    if (pad === "same") basePadding = withSpaceToBatchBasePaddings([
        convInfo.filterHeight,
        convInfo.filterWidth
    ], dilation);
    else basePadding = [
        [
            0,
            0
        ],
        [
            0,
            0
        ]
    ];
    const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
    const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings([
        convInfo.inHeight,
        convInfo.inWidth
    ], dilation, basePadding);
    const convertedPad = isDilationOne ? pad : "valid";
    const convertedX = isDilationOne ? x4D : (0, _spaceToBatchNd.spaceToBatchND)(x4D, dilation, adjustedPadding);
    const forwardOp = poolingType === "avg" ? ()=>(0, _avgPool.avgPool)(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : ()=>(0, _maxPool.maxPool)(convertedX, windowShape, strides, convertedPad, dimRoundingMode);
    const y = forwardOp();
    const res = isDilationOne ? y : (0, _batchToSpaceNd.batchToSpaceND)(y, dilation, adjustedCrops);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
// Helper function to compute crops and paddings for pool with dilation > 1.
// tslint:disable-next-line:max-line-length
// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184
function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
    const padStart = basePadding.map((b)=>b[0]);
    const origPadEnd = basePadding.map((b)=>b[1]);
    const fullInputShape = inputShape.concat(padStart, origPadEnd);
    const padEndExtra = blockShape.map((b, i)=>(b - fullInputShape[i] % b) % b);
    const padEnd = origPadEnd.map((s, i)=>s + padEndExtra[i]);
    const paddings = blockShape.map((_, i)=>[
            padStart[i],
            padEnd[i]
        ]);
    const crops = blockShape.map((_, i)=>[
            0,
            padEndExtra[i]
        ]);
    return [
        paddings,
        crops
    ];
}
// Helper function to compute base paddings for pool with dilation > 1.
// tslint:disable-next-line:max-line-length
// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524
function withSpaceToBatchBasePaddings(filterShape, dilation) {
    // Spatial dimensions of the filters and the upsampled filters in which we
    // introduce (rate - 1) zeros between consecutive filter values.
    const dilatedFilterShape = filterShape.map((s, i)=>{
        return s + (s - 1) * (dilation[i] - 1);
    });
    const padExtraShape = dilatedFilterShape.map((s)=>s - 1);
    // When padding is odd, we pad more at end, following the same
    // convention as conv2d.
    const padExtraStart = padExtraShape.map((s)=>Math.floor(s / 2));
    const padExtraEnd = padExtraShape.map((s, i)=>s - padExtraStart[i]);
    return padExtraShape.map((_, i)=>{
        return [
            padExtraStart[i],
            padExtraEnd[i]
        ];
    });
}
const pool = /* @__PURE__ */ (0, _operation.op)({
    pool_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./avg_pool":"d8nrP","./batch_to_space_nd":"5A2j5","./conv_util":"bf4MH","./max_pool":"hx37d","./operation":"ip64W","./reshape":"ew3wZ","./space_to_batch_nd":"dDwiy","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dDwiy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "spaceToBatchND", ()=>spaceToBatchND);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * This operation divides "spatial" dimensions `[1, ..., M]` of the input into
 * a grid of blocks of shape `blockShape`, and interleaves these blocks with
 * the "batch" dimension (0) such that in the output, the spatial
 * dimensions `[1, ..., M]` correspond to the position within the grid,
 * and the batch dimension combines both the position within a spatial block
 * and the original batch position. Prior to division into blocks,
 * the spatial dimensions of the input are optionally zero padded
 * according to `paddings`. See below for a precise description.
 *
 * ```js
 * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
 * const blockShape = [2, 2];
 * const paddings = [[0, 0], [0, 0]];
 *
 * x.spaceToBatchND(blockShape, paddings).print();
 * ```
 *
 * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +
 * remainingShape`, where spatialShape has `M` dimensions.
 * @param blockShape A 1-D array. Must have shape `[M]`, all values must
 * be >= 1.
 * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=
 *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad
 * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It
 * is required that
 * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`
 *
 * This operation is equivalent to the following steps:
 *
 * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input
 * according to `paddings` to produce `padded` of shape paddedShape.
 *
 * 2. Reshape `padded` to `reshapedPadded` of shape:
 * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,
 * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`
 *
 * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`
 * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,
 * paddedShape[M] / blockShape[M-1]] + remainingShape`
 *
 * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the
 * batch dimension, producing an output tensor of shape:
 * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,
 * paddedShape[M] / blockShape[M-1]] + remainingShape`
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function spaceToBatchND_(x, blockShape, paddings) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "spaceToBatchND");
    _util.assert($x.rank >= 1 + blockShape.length, ()=>`input rank ${$x.rank} should be > than [blockShape] ${blockShape.length}`);
    _util.assert(paddings.length === blockShape.length, ()=>`paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${blockShape.length}`);
    _util.assert($x.shape.reduce((a, b, i)=>{
        if (i > 0 && i <= blockShape.length) return a && (b + paddings[i - 1][0] + paddings[i - 1][1]) % blockShape[i - 1] === 0;
        return a;
    }, true), ()=>`input spatial dimensions ${$x.shape.slice(1)} with paddings ${paddings.toString()} must be divisible by blockShapes ${blockShape.toString()}`);
    const inputs = {
        x: $x
    };
    const attrs = {
        blockShape,
        paddings
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SpaceToBatchND), inputs, attrs);
}
const spaceToBatchND = /* @__PURE__ */ (0, _operation.op)({
    spaceToBatchND_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"azJEF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "prelu", ()=>prelu);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes leaky rectified linear element-wise with parametric alphas.
 *
 * `x < 0 ? alpha * x : f(x) = x`
 *
 * ```js
 * const x = tf.tensor1d([-1, 2, -3, 4]);
 * const alpha = tf.scalar(0.1);
 *
 * x.prelu(alpha).print();  // or tf.prelu(x, alpha)
 * ```
 * @param x The input tensor.
 * @param alpha Scaling factor for negative values.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function prelu_(x, alpha) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "prelu");
    const $alpha = (0, _tensorUtilEnv.convertToTensor)(alpha, "alpha", "prelu");
    const inputs = {
        x: $x,
        alpha: $alpha
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Prelu), inputs);
}
const prelu = /* @__PURE__ */ (0, _operation.op)({
    prelu_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hawIk":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "prod", ()=>prod);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _cast = require("./cast");
var _operation = require("./operation");
/**
 * Computes the product of elements across dimensions of a `tf.Tensor`.
 *
 * Reduces the input along the dimensions given in `axes`. Unless `keepDims`
 * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
 * `axes`. If `keepDims` is true, the reduced dimensions are retained with
 * length 1. If `axes` has no entries, all dimensions are reduced, and a
 * `tf.Tensor` with a single element is returned.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3]);
 *
 * x.prod().print();  // or tf.prod(x)
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * const axis = 1;
 * x.prod(axis).print();  // or tf.prod(x, axis)
 * ```
 *
 * @param x The input tensor to compute the product over. If the dtype is `bool`
 *   it will be converted to `int32` and the output dtype will be `int32`.
 * @param axis The dimension(s) to reduce. By default it reduces
 *     all dimensions.
 * @param keepDims If true, retains reduced dimensions with size 1.
 *
 * @doc {heading: 'Operations', subheading: 'Reduction'}
 */ function prod_(x, axis = null, keepDims = false) {
    let $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "prod");
    if ($x.dtype === "bool") // bool is not an allowed type for the underlying kernel.
    $x = (0, _cast.cast)($x, "int32");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis,
        keepDims
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Prod), inputs, attrs);
}
const prod = /* @__PURE__ */ (0, _operation.op)({
    prod_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./cast":"b4VDA","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a5cAV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedGather", ()=>raggedGather);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
function raggedGather_(paramsNestedSplits, paramsDenseValues, indices, outputRaggedRank) {
    const $paramsNestedSplits = paramsNestedSplits.map((t, i)=>(0, _tensorUtilEnv.convertToTensor)(t, `tensors${i}`, "raggedGather", "int32"));
    const $paramsDenseValues = (0, _tensorUtilEnv.convertToTensor)(paramsDenseValues, "paramsDenseValues", "raggedGather");
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "raggedGather", "int32");
    const inputs = {
        paramsNestedSplits: $paramsNestedSplits,
        paramsDenseValues: $paramsDenseValues,
        indices: $indices
    };
    const attrs = {
        outputRaggedRank
    };
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.RaggedGather), inputs, attrs);
    return {
        outputNestedSplits: result.slice(0, result.length - 1),
        outputDenseValues: result[result.length - 1]
    };
}
const raggedGather = /* @__PURE__ */ (0, _operation.op)({
    raggedGather_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9Z2C7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedRange", ()=>raggedRange);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns a RaggedTensor result composed from rtDenseValues and rtNestedSplits,
 * such that result[i] = [starts[i], starts[i] + deltas[i], ..., limits[i]]).
 *
 * @param starts: A Tensor. Must be one of the following types:
 *     'float32', 'int32'. The starts of each range.
 * @param limits: A Tensor. Must have the same type as starts. The limits of
 *     each range.
 * @param deltas: A Tensor. Must have the same type as starts. The deltas of
 *     each range.
 * @return A map with the following properties:
 *     - rtNestedSplits: A Tensor of type 'int32'.
 *     - rtDenseValues: A Tensor. Has the same type as starts.
 */ function raggedRange_(starts, limits, deltas) {
    const $starts = (0, _tensorUtilEnv.convertToTensor)(starts, "starts", "raggedRange");
    const $limits = (0, _tensorUtilEnv.convertToTensor)(limits, "limits", "raggedRange", $starts.dtype);
    const $deltas = (0, _tensorUtilEnv.convertToTensor)(deltas, "deltas", "raggedRange", $starts.dtype);
    const inputs = {
        starts: $starts,
        limits: $limits,
        deltas: $deltas
    };
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.RaggedRange), inputs);
    return {
        rtNestedSplits: result[0],
        rtDenseValues: result[1]
    };
}
const raggedRange = /* @__PURE__ */ (0, _operation.op)({
    raggedRange_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"93163":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedTensorToTensor", ()=>raggedTensorToTensor);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Create a dense tensor from a ragged tensor, possibly altering its shape.
 *
 * The raggedTensorToTensor op creates a dense tensor from am array of row
 * partition tensors, a value vector, and default values. If the shape is
 * unspecified, the minimal shape required to contain all the elements in the
 * ragged tensor (the natural shape) will be used. If some dimensions are left
 * unspecified, then the size of the natural shape is used in that dimension.
 *
 * The defaultValue will be broadcast to the output shape. After that, the
 * values from the ragged tensor overwrite the default values. Note that the
 * defaultValue must have less dimensions than the value.
 *
 * The row partition tensors are in the order of the dimensions. At present, the
 * types can be: "ROW_SPLITS": the row_splits tensor from the ragged tensor.
 *   "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor.
 *   "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it
 * is preceded by "FIRST_DIM_SIZE".
 * ```
 * @param shape: A Tensor. Must be one of the following types: 'int32'. The
 *     desired shape of the output tensor. If left unspecified (empty), the
 *     minimal shape required to contain all the elements in the ragged tensor
 *     (the natural shape) will be used. If some dimensions are left
 *     unspecified, then the size of the natural shape is used in that
 *     dimension.
 *
 *     Note that dense dimensions cannot be modified by the shape argument.
 *     Trying to change the size of a dense dimension will cause the op to fail.
 *     Examples: natural shape: [4, 5, 6] shape: -1 output shape: [4, 5, 6]
 *
 *     natural shape: [4, 5, 6] shape: [3, -1, 2] output shape: [3, 5, 2]
 *
 *     natural shape: [4, 5, 6] shape: [3, 7, 2] output shape: [3, 7, 2]
 * @param values: A Tensor. A 1D tensor representing the values of the ragged
 *     tensor.
 * @param defaultValue: A Tensor. Must have the same type as values. The
 *     defaultValue when the shape is larger than the ragged tensor. The
 *     defaultValue is broadcast until it is the shape of the output tensor,
 *     and then overwritten by values in the ragged tensor. The default value
 *     must be compatible with this broadcast operation, and must have fewer
 *     dimensions than the value tensor.
 * @param rowPartitionTensors: A list of at least 1 Tensor objects with the same
 *     type in: 'int32'.
 * @param rowPartitionTypes: A list of strings. The types of the row partition
 *     tensors. At present, these can be:
 *     "ROW_SPLITS": the row_splits tensor from the ragged tensor.
 *     "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor.
 *     "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then
 *         it is preceeded by "FIRST_DIM_SIZE". The tensors are in the order of
 *         the dimensions.
 * @return A Tensor. Has the same type as values.
 * @doc {heading: 'Operations', subheading: 'Ragged'}
 */ function raggedTensorToTensor_(shape, values, defaultValue, rowPartitionTensors, rowPartitionTypes) {
    const $shape = (0, _tensorUtilEnv.convertToTensor)(shape, "shape", "raggedTensorToTensor", "int32");
    const $values = (0, _tensorUtilEnv.convertToTensor)(values, "values", "raggedTensorToTensor");
    const $defaultValue = (0, _tensorUtilEnv.convertToTensor)(defaultValue, "defaultValue", "raggedTensorToTensor", $values.dtype);
    const $rowPartitionTensors = rowPartitionTensors.map((t, i)=>(0, _tensorUtilEnv.convertToTensor)(t, `tensors${i}`, "raggedTensorToTensor", "int32"));
    const inputs = {
        shape: $shape,
        values: $values,
        defaultValue: $defaultValue,
        rowPartitionTensors: $rowPartitionTensors
    };
    const attrs = {
        rowPartitionTypes
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.RaggedTensorToTensor), inputs, attrs);
}
const raggedTensorToTensor = /* @__PURE__ */ (0, _operation.op)({
    raggedTensorToTensor_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eyCJc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rand", ()=>rand);
var _engine = require("../engine");
var _util = require("../util");
var _utilBase = require("../util_base");
var _operation = require("./operation");
/**
 * Creates a `tf.Tensor` with values sampled from a random number generator
 * function defined by the user.
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param randFunction A random number generator function which is called
 * for each element in the output tensor.
 * @param dtype The data type of the output tensor. Defaults to 'float32'.
 *
 * @doc {heading: 'Tensors', subheading: 'Random'}
 */ function rand_(shape, randFunction, dtype) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    const size = (0, _util.sizeFromShape)(shape);
    let values = null;
    if (dtype == null || dtype === "float32") values = new Float32Array(size);
    else if (dtype === "int32") values = new Int32Array(size);
    else if (dtype === "bool") values = new Uint8Array(size);
    else throw new Error(`Unknown data type ${dtype}`);
    for(let i = 0; i < size; i++)values[i] = randFunction();
    return (0, _engine.ENGINE).makeTensor(values, shape, dtype);
}
const rand = /* @__PURE__ */ (0, _operation.op)({
    rand_
});

},{"../engine":"1g2qo","../util":"ctavo","../util_base":"4Vo4a","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4v8VB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "randomGamma", ()=>randomGamma);
var _utilBase = require("../util_base");
var _buffer = require("./buffer");
var _operation = require("./operation");
var _randUtil = require("./rand_util");
/**
 * Creates a `tf.Tensor` with values sampled from a gamma distribution.
 *
 * ```js
 * tf.randomGamma([2, 2], 1).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param alpha The shape parameter of the gamma distribution.
 * @param beta The inverse scale parameter of the gamma distribution. Defaults
 *     to 1.
 * @param dtype The data type of the output. Defaults to float32.
 * @param seed The seed for the random number generator.
 *
 * @doc {heading: 'Tensors', subheading: 'Random'}
 */ function randomGamma_(shape, alpha, beta = 1, dtype = "float32", seed) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    if (beta == null) beta = 1;
    if (dtype == null) dtype = "float32";
    if (dtype !== "float32" && dtype !== "int32") throw new Error(`Unsupported data type ${dtype}`);
    const rgamma = new (0, _randUtil.RandGamma)(alpha, beta, dtype, seed);
    const res = (0, _buffer.buffer)(shape, dtype);
    for(let i = 0; i < res.values.length; i++)res.values[i] = rgamma.nextValue();
    return res.toTensor();
}
const randomGamma = /* @__PURE__ */ (0, _operation.op)({
    randomGamma_
});

},{"../util_base":"4Vo4a","./buffer":"dYyY7","./operation":"ip64W","./rand_util":"3z7XO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3z7XO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
// https://en.wikipedia.org/wiki/Marsaglia_polar_method
parcelHelpers.export(exports, "MPRandGauss", ()=>MPRandGauss);
// Marsaglia, George, and Wai Wan Tsang. 2000. "A Simple Method for Generating
// Gamma Variables."
parcelHelpers.export(exports, "RandGamma", ()=>RandGamma);
parcelHelpers.export(exports, "UniformRandom", ()=>UniformRandom);
parcelHelpers.export(exports, "jarqueBeraNormalityTest", ()=>jarqueBeraNormalityTest);
parcelHelpers.export(exports, "expectArrayInMeanStdRange", ()=>expectArrayInMeanStdRange);
var _seedrandom = require("seedrandom");
var _testUtil = require("../test_util");
class MPRandGauss {
    constructor(mean, stdDeviation, dtype, truncated, seed){
        this.mean = mean;
        this.stdDev = stdDeviation;
        this.dtype = dtype;
        this.nextVal = NaN;
        this.truncated = truncated;
        if (this.truncated) {
            this.upper = this.mean + this.stdDev * 2;
            this.lower = this.mean - this.stdDev * 2;
        }
        const seedValue = seed ? seed : Math.random();
        this.random = _seedrandom.alea(seedValue.toString());
    }
    /** Returns next sample from a Gaussian distribution. */ nextValue() {
        if (!isNaN(this.nextVal)) {
            const value = this.nextVal;
            this.nextVal = NaN;
            return value;
        }
        let resultX, resultY;
        let isValid = false;
        while(!isValid){
            let v1, v2, s;
            do {
                v1 = 2 * this.random() - 1;
                v2 = 2 * this.random() - 1;
                s = v1 * v1 + v2 * v2;
            }while (s >= 1 || s === 0);
            const mul = Math.sqrt(-2 * Math.log(s) / s);
            resultX = this.mean + this.stdDev * v1 * mul;
            resultY = this.mean + this.stdDev * v2 * mul;
            if (!this.truncated || this.isValidTruncated(resultX)) isValid = true;
        }
        if (!this.truncated || this.isValidTruncated(resultY)) this.nextVal = this.convertValue(resultY);
        return this.convertValue(resultX);
    }
    /** Handles proper rounding for non-floating-point numbers. */ convertValue(value) {
        if (this.dtype == null || this.dtype === "float32") return value;
        return Math.round(value);
    }
    /** Returns true if less than 2-standard-deviations from the mean. */ isValidTruncated(value) {
        return value <= this.upper && value >= this.lower;
    }
}
class RandGamma {
    constructor(alpha, beta, dtype, seed){
        this.alpha = alpha;
        this.beta = 1 / beta; // convert rate to scale parameter
        this.dtype = dtype;
        const seedValue = seed ? seed : Math.random();
        this.randu = _seedrandom.alea(seedValue.toString());
        this.randn = new MPRandGauss(0, 1, dtype, false, this.randu());
        if (alpha < 1) this.d = alpha + 2 / 3;
        else this.d = alpha - 1 / 3;
        this.c = 1 / Math.sqrt(9 * this.d);
    }
    /** Returns next sample from a gamma distribution. */ nextValue() {
        let x2, v0, v1, x, u, v;
        while(true){
            do {
                x = this.randn.nextValue();
                v = 1 + this.c * x;
            }while (v <= 0);
            v *= v * v;
            x2 = x * x;
            v0 = 1 - 0.331 * x2 * x2;
            v1 = 0.5 * x2 + this.d * (1 - v + Math.log(v));
            u = this.randu();
            if (u < v0 || Math.log(u) < v1) break;
        }
        v = 1 / this.beta * this.d * v;
        if (this.alpha < 1) v *= Math.pow(this.randu(), 1 / this.alpha);
        return this.convertValue(v);
    }
    /** Handles proper rounding for non-floating-point numbers. */ convertValue(value) {
        if (this.dtype === "float32") return value;
        return Math.round(value);
    }
}
class UniformRandom {
    constructor(min = 0, max = 1, dtype, seed){
        /** Handles proper rounding for non floating point numbers. */ this.canReturnFloat = ()=>this.dtype == null || this.dtype === "float32";
        this.min = min;
        this.range = max - min;
        this.dtype = dtype;
        if (seed == null) seed = Math.random();
        if (typeof seed === "number") seed = seed.toString();
        if (!this.canReturnFloat() && this.range <= 1) throw new Error(`The difference between ${min} - ${max} <= 1 and dtype is not float`);
        this.random = _seedrandom.alea(seed);
    }
    convertValue(value) {
        if (this.canReturnFloat()) return value;
        return Math.round(value);
    }
    nextValue() {
        return this.convertValue(this.min + this.range * this.random());
    }
}
function jarqueBeraNormalityTest(values) {
    // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test
    const n = values.length;
    const s = skewness(values);
    const k = kurtosis(values);
    const jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));
    // JB test requires 2-degress of freedom from Chi-Square @ 0.95:
    // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm
    const CHI_SQUARE_2DEG = 5.991;
    if (jb > CHI_SQUARE_2DEG) throw new Error(`Invalid p-value for JB: ${jb}`);
}
function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {
    if (epsilon == null) epsilon = (0, _testUtil.testEpsilon)();
    const actualMean = mean(actual);
    (0, _testUtil.expectNumbersClose)(actualMean, expectedMean, epsilon);
    (0, _testUtil.expectNumbersClose)(standardDeviation(actual, actualMean), expectedStdDev, epsilon);
}
function mean(values) {
    let sum = 0;
    for(let i = 0; i < values.length; i++)sum += values[i];
    return sum / values.length;
}
function standardDeviation(values, mean) {
    let squareDiffSum = 0;
    for(let i = 0; i < values.length; i++){
        const diff = values[i] - mean;
        squareDiffSum += diff * diff;
    }
    return Math.sqrt(squareDiffSum / values.length);
}
function kurtosis(values) {
    // https://en.wikipedia.org/wiki/Kurtosis
    const valuesMean = mean(values);
    const n = values.length;
    let sum2 = 0;
    let sum4 = 0;
    for(let i = 0; i < n; i++){
        const v = values[i] - valuesMean;
        sum2 += Math.pow(v, 2);
        sum4 += Math.pow(v, 4);
    }
    return 1 / n * sum4 / Math.pow(1 / n * sum2, 2);
}
function skewness(values) {
    // https://en.wikipedia.org/wiki/Skewness
    const valuesMean = mean(values);
    const n = values.length;
    let sum2 = 0;
    let sum3 = 0;
    for(let i = 0; i < n; i++){
        const v = values[i] - valuesMean;
        sum2 += Math.pow(v, 2);
        sum3 += Math.pow(v, 3);
    }
    return 1 / n * sum3 / Math.pow(1 / (n - 1) * sum2, 1.5);
}

},{"seedrandom":"9SiBG","../test_util":"3IEFi","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9SiBG":[function(require,module,exports) {
// A library of seedable RNGs implemented in Javascript.
//
// Usage:
//
// var seedrandom = require('seedrandom');
// var random = seedrandom(1); // or any seed.
// var x = random();       // 0 <= x < 1.  Every bit is random.
// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.
// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.
// Period: ~2^116
// Reported to pass all BigCrush tests.
var alea = require("12650d0e2f5e5e92");
// xor128, a pure xor-shift generator by George Marsaglia.
// Period: 2^128-1.
// Reported to fail: MatrixRank and LinearComp.
var xor128 = require("2e9ff46cdee4aab4");
// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.
// Period: 2^192-2^32
// Reported to fail: CollisionOver, SimpPoker, and LinearComp.
var xorwow = require("a04af32374227880");
// xorshift7, by François Panneton and Pierre L'ecuyer, takes
// a different approach: it adds robustness by allowing more shifts
// than Marsaglia's original three.  It is a 7-shift generator
// with 256 bits, that passes BigCrush with no systmatic failures.
// Period 2^256-1.
// No systematic BigCrush failures reported.
var xorshift7 = require("65d05f6db8da652c");
// xor4096, by Richard Brent, is a 4096-bit xor-shift with a
// very long period that also adds a Weyl generator. It also passes
// BigCrush with no systematic failures.  Its long period may
// be useful if you have many generators and need to avoid
// collisions.
// Period: 2^4128-2^32.
// No systematic BigCrush failures reported.
var xor4096 = require("1e972c6b140abbe7");
// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random
// number generator derived from ChaCha, a modern stream cipher.
// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
// Period: ~2^127
// No systematic BigCrush failures reported.
var tychei = require("286eb9753e804cda");
// The original ARC4-based prng included in this library.
// Period: ~2^1600
var sr = require("a0d0bb1ebcf7d3f9");
sr.alea = alea;
sr.xor128 = xor128;
sr.xorwow = xorwow;
sr.xorshift7 = xorshift7;
sr.xor4096 = xor4096;
sr.tychei = tychei;
module.exports = sr;

},{"12650d0e2f5e5e92":"cjnHc","2e9ff46cdee4aab4":"hD4Ju","a04af32374227880":"4UiN4","65d05f6db8da652c":"bOULY","1e972c6b140abbe7":"aTjJH","286eb9753e804cda":"7crAu","a0d0bb1ebcf7d3f9":"jDzc2"}],"cjnHc":[function(require,module,exports) {
// A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010
// http://baagoe.com/en/RandomMusings/javascript/
// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror
// Original work is under MIT license -
// Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
// THE SOFTWARE.
(function(global, module1, define1) {
    function Alea(seed) {
        var me = this, mash = Mash();
        me.next = function() {
            var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32
            me.s0 = me.s1;
            me.s1 = me.s2;
            return me.s2 = t - (me.c = t | 0);
        };
        // Apply the seeding algorithm from Baagoe.
        me.c = 1;
        me.s0 = mash(" ");
        me.s1 = mash(" ");
        me.s2 = mash(" ");
        me.s0 -= mash(seed);
        if (me.s0 < 0) me.s0 += 1;
        me.s1 -= mash(seed);
        if (me.s1 < 0) me.s1 += 1;
        me.s2 -= mash(seed);
        if (me.s2 < 0) me.s2 += 1;
        mash = null;
    }
    function copy(f, t) {
        t.c = f.c;
        t.s0 = f.s0;
        t.s1 = f.s1;
        t.s2 = f.s2;
        return t;
    }
    function impl(seed, opts) {
        var xg = new Alea(seed), state = opts && opts.state, prng = xg.next;
        prng.int32 = function() {
            return xg.next() * 0x100000000 | 0;
        };
        prng.double = function() {
            return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53
        };
        prng.quick = prng;
        if (state) {
            if (typeof state == "object") copy(state, xg);
            prng.state = function() {
                return copy(xg, {});
            };
        }
        return prng;
    }
    function Mash() {
        var n = 0xefc8249d;
        var mash = function(data) {
            data = String(data);
            for(var i = 0; i < data.length; i++){
                n += data.charCodeAt(i);
                var h = 0.02519603282416938 * n;
                n = h >>> 0;
                h -= n;
                h *= n;
                n = h >>> 0;
                h -= n;
                n += h * 0x100000000; // 2^32
            }
            return (n >>> 0) * 2.3283064365386963e-10; // 2^-32
        };
        return mash;
    }
    if (module1 && module1.exports) module1.exports = impl;
    else if (define1 && define1.amd) define1(function() {
        return impl;
    });
    else this.alea = impl;
})(this, module, typeof define == "function" && define // present with an AMD loader
);

},{}],"hD4Ju":[function(require,module,exports) {
// A Javascript implementaion of the "xor128" prng algorithm by
// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
(function(global, module1, define1) {
    function XorGen(seed) {
        var me = this, strseed = "";
        me.x = 0;
        me.y = 0;
        me.z = 0;
        me.w = 0;
        // Set up generator function.
        me.next = function() {
            var t = me.x ^ me.x << 11;
            me.x = me.y;
            me.y = me.z;
            me.z = me.w;
            return me.w ^= me.w >>> 19 ^ t ^ t >>> 8;
        };
        if (seed === (seed | 0)) // Integer seed.
        me.x = seed;
        else // String seed.
        strseed += seed;
        // Mix in string seed, then discard an initial batch of 64 values.
        for(var k = 0; k < strseed.length + 64; k++){
            me.x ^= strseed.charCodeAt(k) | 0;
            me.next();
        }
    }
    function copy(f, t) {
        t.x = f.x;
        t.y = f.y;
        t.z = f.z;
        t.w = f.w;
        return t;
    }
    function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
            return (xg.next() >>> 0) / 0x100000000;
        };
        prng.double = function() {
            do var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / 2097152;
            while (result === 0);
            return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
            if (typeof state == "object") copy(state, xg);
            prng.state = function() {
                return copy(xg, {});
            };
        }
        return prng;
    }
    if (module1 && module1.exports) module1.exports = impl;
    else if (define1 && define1.amd) define1(function() {
        return impl;
    });
    else this.xor128 = impl;
})(this, module, typeof define == "function" && define // present with an AMD loader
);

},{}],"4UiN4":[function(require,module,exports) {
// A Javascript implementaion of the "xorwow" prng algorithm by
// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
(function(global, module1, define1) {
    function XorGen(seed) {
        var me = this, strseed = "";
        // Set up generator function.
        me.next = function() {
            var t = me.x ^ me.x >>> 2;
            me.x = me.y;
            me.y = me.z;
            me.z = me.w;
            me.w = me.v;
            return (me.d = me.d + 362437 | 0) + (me.v = me.v ^ me.v << 4 ^ (t ^ t << 1)) | 0;
        };
        me.x = 0;
        me.y = 0;
        me.z = 0;
        me.w = 0;
        me.v = 0;
        if (seed === (seed | 0)) // Integer seed.
        me.x = seed;
        else // String seed.
        strseed += seed;
        // Mix in string seed, then discard an initial batch of 64 values.
        for(var k = 0; k < strseed.length + 64; k++){
            me.x ^= strseed.charCodeAt(k) | 0;
            if (k == strseed.length) me.d = me.x << 10 ^ me.x >>> 4;
            me.next();
        }
    }
    function copy(f, t) {
        t.x = f.x;
        t.y = f.y;
        t.z = f.z;
        t.w = f.w;
        t.v = f.v;
        t.d = f.d;
        return t;
    }
    function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
            return (xg.next() >>> 0) / 0x100000000;
        };
        prng.double = function() {
            do var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / 2097152;
            while (result === 0);
            return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
            if (typeof state == "object") copy(state, xg);
            prng.state = function() {
                return copy(xg, {});
            };
        }
        return prng;
    }
    if (module1 && module1.exports) module1.exports = impl;
    else if (define1 && define1.amd) define1(function() {
        return impl;
    });
    else this.xorwow = impl;
})(this, module, typeof define == "function" && define // present with an AMD loader
);

},{}],"bOULY":[function(require,module,exports) {
// A Javascript implementaion of the "xorshift7" algorithm by
// François Panneton and Pierre L'ecuyer:
// "On the Xorgshift Random Number Generators"
// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf
(function(global, module1, define1) {
    function XorGen(seed) {
        var me = this;
        // Set up generator function.
        me.next = function() {
            // Update xor generator.
            var X = me.x, i = me.i, t, v, w;
            t = X[i];
            t ^= t >>> 7;
            v = t ^ t << 24;
            t = X[i + 1 & 7];
            v ^= t ^ t >>> 10;
            t = X[i + 3 & 7];
            v ^= t ^ t >>> 3;
            t = X[i + 4 & 7];
            v ^= t ^ t << 7;
            t = X[i + 7 & 7];
            t = t ^ t << 13;
            v ^= t ^ t << 9;
            X[i] = v;
            me.i = i + 1 & 7;
            return v;
        };
        function init(me, seed) {
            var j, w, X = [];
            if (seed === (seed | 0)) // Seed state array using a 32-bit integer.
            w = X[0] = seed;
            else {
                // Seed state using a string.
                seed = "" + seed;
                for(j = 0; j < seed.length; ++j)X[j & 7] = X[j & 7] << 15 ^ seed.charCodeAt(j) + X[j + 1 & 7] << 13;
            }
            // Enforce an array length of 8, not all zeroes.
            while(X.length < 8)X.push(0);
            for(j = 0; j < 8 && X[j] === 0; ++j);
            if (j == 8) w = X[7] = -1;
            else w = X[j];
            me.x = X;
            me.i = 0;
            // Discard an initial 256 values.
            for(j = 256; j > 0; --j)me.next();
        }
        init(me, seed);
    }
    function copy(f, t) {
        t.x = f.x.slice();
        t.i = f.i;
        return t;
    }
    function impl(seed, opts) {
        if (seed == null) seed = +new Date;
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
            return (xg.next() >>> 0) / 0x100000000;
        };
        prng.double = function() {
            do var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / 2097152;
            while (result === 0);
            return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
            if (state.x) copy(state, xg);
            prng.state = function() {
                return copy(xg, {});
            };
        }
        return prng;
    }
    if (module1 && module1.exports) module1.exports = impl;
    else if (define1 && define1.amd) define1(function() {
        return impl;
    });
    else this.xorshift7 = impl;
})(this, module, typeof define == "function" && define // present with an AMD loader
);

},{}],"aTjJH":[function(require,module,exports) {
// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.
//
// This fast non-cryptographic random number generator is designed for
// use in Monte-Carlo algorithms. It combines a long-period xorshift
// generator with a Weyl generator, and it passes all common batteries
// of stasticial tests for randomness while consuming only a few nanoseconds
// for each prng generated.  For background on the generator, see Brent's
// paper: "Some long-period random number generators using shifts and xors."
// http://arxiv.org/pdf/1004.3115v1.pdf
//
// Usage:
//
// var xor4096 = require('xor4096');
// random = xor4096(1);                        // Seed with int32 or string.
// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.
// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.
//
// For nonzero numeric keys, this impelementation provides a sequence
// identical to that by Brent's xorgens 3 implementaion in C.  This
// implementation also provides for initalizing the generator with
// string seeds, or for saving and restoring the state of the generator.
//
// On Chrome, this prng benchmarks about 2.1 times slower than
// Javascript's built-in Math.random().
(function(global, module1, define1) {
    function XorGen(seed) {
        var me = this;
        // Set up generator function.
        me.next = function() {
            var w = me.w, X = me.X, i = me.i, t, v;
            // Update Weyl generator.
            me.w = w = w + 0x61c88647 | 0;
            // Update xor generator.
            v = X[i + 34 & 127];
            t = X[i = i + 1 & 127];
            v ^= v << 13;
            t ^= t << 17;
            v ^= v >>> 15;
            t ^= t >>> 12;
            // Update Xor generator array state.
            v = X[i] = v ^ t;
            me.i = i;
            // Result is the combination.
            return v + (w ^ w >>> 16) | 0;
        };
        function init(me, seed) {
            var t, v, i, j, w, X = [], limit = 128;
            if (seed === (seed | 0)) {
                // Numeric seeds initialize v, which is used to generates X.
                v = seed;
                seed = null;
            } else {
                // String seeds are mixed into v and X one character at a time.
                seed = seed + "\0";
                v = 0;
                limit = Math.max(limit, seed.length);
            }
            // Initialize circular array and weyl value.
            for(i = 0, j = -32; j < limit; ++j){
                // Put the unicode characters into the array, and shuffle them.
                if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);
                // After 32 shuffles, take v as the starting w value.
                if (j === 0) w = v;
                v ^= v << 10;
                v ^= v >>> 15;
                v ^= v << 4;
                v ^= v >>> 13;
                if (j >= 0) {
                    w = w + 0x61c88647 | 0; // Weyl.
                    t = X[j & 127] ^= v + w; // Combine xor and weyl to init array.
                    i = 0 == t ? i + 1 : 0; // Count zeroes.
                }
            }
            // We have detected all zeroes; make the key nonzero.
            if (i >= 128) X[(seed && seed.length || 0) & 127] = -1;
            // Run the generator 512 times to further mix the state before using it.
            // Factoring this as a function slows the main generator, so it is just
            // unrolled here.  The weyl generator is not advanced while warming up.
            i = 127;
            for(j = 512; j > 0; --j){
                v = X[i + 34 & 127];
                t = X[i = i + 1 & 127];
                v ^= v << 13;
                t ^= t << 17;
                v ^= v >>> 15;
                t ^= t >>> 12;
                X[i] = v ^ t;
            }
            // Storing state as object members is faster than using closure variables.
            me.w = w;
            me.X = X;
            me.i = i;
        }
        init(me, seed);
    }
    function copy(f, t) {
        t.i = f.i;
        t.w = f.w;
        t.X = f.X.slice();
        return t;
    }
    function impl(seed, opts) {
        if (seed == null) seed = +new Date;
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
            return (xg.next() >>> 0) / 0x100000000;
        };
        prng.double = function() {
            do var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / 2097152;
            while (result === 0);
            return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
            if (state.X) copy(state, xg);
            prng.state = function() {
                return copy(xg, {});
            };
        }
        return prng;
    }
    if (module1 && module1.exports) module1.exports = impl;
    else if (define1 && define1.amd) define1(function() {
        return impl;
    });
    else this.xor4096 = impl;
})(this, module, typeof define == "function" && define // present with an AMD loader
);

},{}],"7crAu":[function(require,module,exports) {
// A Javascript implementaion of the "Tyche-i" prng algorithm by
// Samuel Neves and Filipe Araujo.
// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
(function(global, module1, define1) {
    function XorGen(seed) {
        var me = this, strseed = "";
        // Set up generator function.
        me.next = function() {
            var b = me.b, c = me.c, d = me.d, a = me.a;
            b = b << 25 ^ b >>> 7 ^ c;
            c = c - d | 0;
            d = d << 24 ^ d >>> 8 ^ a;
            a = a - b | 0;
            me.b = b = b << 20 ^ b >>> 12 ^ c;
            me.c = c = c - d | 0;
            me.d = d << 16 ^ c >>> 16 ^ a;
            return me.a = a - b | 0;
        };
        /* The following is non-inverted tyche, which has better internal
   * bit diffusion, but which is about 25% slower than tyche-i in JS.
  me.next = function() {
    var a = me.a, b = me.b, c = me.c, d = me.d;
    a = (me.a + me.b | 0) >>> 0;
    d = me.d ^ a; d = d << 16 ^ d >>> 16;
    c = me.c + d | 0;
    b = me.b ^ c; b = b << 12 ^ d >>> 20;
    me.a = a = a + b | 0;
    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;
    me.c = c = c + d | 0;
    b = b ^ c;
    return me.b = (b << 7 ^ b >>> 25);
  }
  */ me.a = 0;
        me.b = 0;
        me.c = -1640531527;
        me.d = 1367130551;
        if (seed === Math.floor(seed)) {
            // Integer seed.
            me.a = seed / 0x100000000 | 0;
            me.b = seed | 0;
        } else // String seed.
        strseed += seed;
        // Mix in string seed, then discard an initial batch of 64 values.
        for(var k = 0; k < strseed.length + 20; k++){
            me.b ^= strseed.charCodeAt(k) | 0;
            me.next();
        }
    }
    function copy(f, t) {
        t.a = f.a;
        t.b = f.b;
        t.c = f.c;
        t.d = f.d;
        return t;
    }
    function impl(seed, opts) {
        var xg = new XorGen(seed), state = opts && opts.state, prng = function() {
            return (xg.next() >>> 0) / 0x100000000;
        };
        prng.double = function() {
            do var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, result = (top + bot) / 2097152;
            while (result === 0);
            return result;
        };
        prng.int32 = xg.next;
        prng.quick = prng;
        if (state) {
            if (typeof state == "object") copy(state, xg);
            prng.state = function() {
                return copy(xg, {});
            };
        }
        return prng;
    }
    if (module1 && module1.exports) module1.exports = impl;
    else if (define1 && define1.amd) define1(function() {
        return impl;
    });
    else this.tychei = impl;
})(this, module, typeof define == "function" && define // present with an AMD loader
);

},{}],"jDzc2":[function(require,module,exports) {
/*
Copyright 2019 David Bau.

Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

*/ (function(global, pool, math) {
    //
    // The following constants are related to IEEE 754 limits.
    //
    var width = 256, chunks = 6, digits = 52, rngname = "random", startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; // node.js crypto module, initialized at the bottom.
    //
    // seedrandom()
    // This is the seedrandom function described above.
    //
    function seedrandom(seed, options, callback) {
        var key = [];
        options = options == true ? {
            entropy: true
        } : options || {};
        // Flatten the seed string or build one from local entropy if needed.
        var shortseed = mixkey(flatten(options.entropy ? [
            seed,
            tostring(pool)
        ] : seed == null ? autoseed() : seed, 3), key);
        // Use the seed to initialize an ARC4 generator.
        var arc4 = new ARC4(key);
        // This function returns a random double in [0, 1) that contains
        // randomness in every bit of the mantissa of the IEEE 754 value.
        var prng = function() {
            var n = arc4.g(chunks), d = startdenom, x = 0; //   and no 'extra last byte'.
            while(n < significance){
                n = (n + x) * width; //   shifting numerator and
                d *= width; //   denominator and generating a
                x = arc4.g(1); //   new least-significant-byte.
            }
            while(n >= overflow){
                n /= 2; //   last byte, shift everything
                d /= 2; //   right using integer math until
                x >>>= 1; //   we have exactly the desired bits.
            }
            return (n + x) / d; // Form the number within [0, 1).
        };
        prng.int32 = function() {
            return arc4.g(4) | 0;
        };
        prng.quick = function() {
            return arc4.g(4) / 0x100000000;
        };
        prng.double = prng;
        // Mix the randomness into accumulated entropy.
        mixkey(tostring(arc4.S), pool);
        // Calling convention: what to return as a function of prng, seed, is_math.
        return (options.pass || callback || function(prng, seed, is_math_call, state) {
            if (state) {
                // Load the arc4 state from the given state if it has an S array.
                if (state.S) copy(state, arc4);
                // Only provide the .state method if requested via options.state.
                prng.state = function() {
                    return copy(arc4, {});
                };
            }
            // If called as a method of Math (Math.seedrandom()), mutate
            // Math.random because that is how seedrandom.js has worked since v1.0.
            if (is_math_call) {
                math[rngname] = prng;
                return seed;
            } else return prng;
        })(prng, shortseed, "global" in options ? options.global : this == math, options.state);
    }
    //
    // ARC4
    //
    // An ARC4 implementation.  The constructor takes a key in the form of
    // an array of at most (width) integers that should be 0 <= x < (width).
    //
    // The g(count) method returns a pseudorandom integer that concatenates
    // the next (count) outputs from ARC4.  Its return value is a number x
    // that is in the range 0 <= x < (width ^ count).
    //
    function ARC4(key) {
        var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
        // The empty key [] is treated as [0].
        if (!keylen) key = [
            keylen++
        ];
        // Set up S using the standard key scheduling algorithm.
        while(i < width)s[i] = i++;
        for(i = 0; i < width; i++){
            s[i] = s[j = mask & j + key[i % keylen] + (t = s[i])];
            s[j] = t;
        }
        // The "g" method returns the next (count) outputs as one number.
        (me.g = function(count) {
            // Using instance members instead of closure state nearly doubles speed.
            var t, r = 0, i = me.i, j = me.j, s = me.S;
            while(count--){
                t = s[i = mask & i + 1];
                r = r * width + s[mask & (s[i] = s[j = mask & j + t]) + (s[j] = t)];
            }
            me.i = i;
            me.j = j;
            return r;
        // For robust unpredictability, the function call below automatically
        // discards an initial batch of values.  This is called RC4-drop[256].
        // See http://google.com/search?q=rsa+fluhrer+response&btnI
        })(width);
    }
    //
    // copy()
    // Copies internal state of ARC4 to or from a plain object.
    //
    function copy(f, t) {
        t.i = f.i;
        t.j = f.j;
        t.S = f.S.slice();
        return t;
    }
    //
    // flatten()
    // Converts an object tree to nested arrays of strings.
    //
    function flatten(obj, depth) {
        var result = [], typ = typeof obj, prop;
        if (depth && typ == "object") {
            for(prop in obj)try {
                result.push(flatten(obj[prop], depth - 1));
            } catch (e) {}
        }
        return result.length ? result : typ == "string" ? obj : obj + "\0";
    }
    //
    // mixkey()
    // Mixes a string seed into a key that is an array of integers, and
    // returns a shortened string seed that is equivalent to the result key.
    //
    function mixkey(seed, key) {
        var stringseed = seed + "", smear, j = 0;
        while(j < stringseed.length)key[mask & j] = mask & (smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++);
        return tostring(key);
    }
    //
    // autoseed()
    // Returns an object for autoseeding, using window.crypto and Node crypto
    // module if available.
    //
    function autoseed() {
        try {
            var out;
            if (nodecrypto && (out = nodecrypto.randomBytes)) // The use of 'out' to remember randomBytes makes tight minified code.
            out = out(width);
            else {
                out = new Uint8Array(width);
                (global.crypto || global.msCrypto).getRandomValues(out);
            }
            return tostring(out);
        } catch (e) {
            var browser = global.navigator, plugins = browser && browser.plugins;
            return [
                +new Date,
                global,
                plugins,
                global.screen,
                tostring(pool)
            ];
        }
    }
    //
    // tostring()
    // Converts an array of charcodes to a string
    //
    function tostring(a) {
        return String.fromCharCode.apply(0, a);
    }
    //
    // When seedrandom.js is loaded, we immediately mix a few bits
    // from the built-in RNG into the entropy pool.  Because we do
    // not want to interfere with deterministic PRNG state later,
    // seedrandom will not call math.random on its own again after
    // initialization.
    //
    mixkey(math.random(), pool);
    //
    // Nodejs and AMD support: export the implementation as a module using
    // either convention.
    //
    if (0, module.exports) {
        module.exports = seedrandom;
        // When in node.js, try using crypto package for autoseeding.
        try {
            nodecrypto = require("55ffaba5e09143c0");
        } catch (ex) {}
    } else if (typeof define == "function" && define.amd) define(function() {
        return seedrandom;
    });
    else // When included as a plain script, set up Math.seedrandom global.
    math["seed" + rngname] = seedrandom;
// End anonymous scope, and pass initial values.
})(// global: `self` in browsers (including strict mode and web workers),
// otherwise `this` in Node and other environments
typeof self !== "undefined" ? self : this, [], Math // math: package containing random, pow, and seedrandom
);

},{"55ffaba5e09143c0":"1kJBy"}],"3IEFi":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "TEST_EPSILON_FLOAT16", ()=>TEST_EPSILON_FLOAT16);
parcelHelpers.export(exports, "expectArraysClose", ()=>expectArraysClose);
parcelHelpers.export(exports, "testEpsilon", ()=>testEpsilon);
parcelHelpers.export(exports, "expectPromiseToFail", ()=>expectPromiseToFail);
parcelHelpers.export(exports, "expectArraysEqual", ()=>expectArraysEqual);
parcelHelpers.export(exports, "expectNumbersClose", ()=>expectNumbersClose);
parcelHelpers.export(exports, "expectValuesInRange", ()=>expectValuesInRange);
parcelHelpers.export(exports, "expectArrayBuffersEqual", ()=>expectArrayBuffersEqual);
/** Encodes strings into utf-8 bytes. */ parcelHelpers.export(exports, "encodeStrings", ()=>encodeStrings);
/** Creates an HTMLVideoElement with autoplay-friendly default settings. */ parcelHelpers.export(exports, "createVideoElement", ()=>createVideoElement);
parcelHelpers.export(exports, "play", ()=>play);
var _engine = require("./engine");
var _tensorUtilEnv = require("./tensor_util_env");
var _util = require("./util");
const TEST_EPSILON_FLOAT32 = 1e-3;
const TEST_EPSILON_FLOAT16 = 1e-1;
function expectArraysClose(actual, expected, epsilon) {
    if (epsilon == null) epsilon = testEpsilon();
    return expectArraysPredicate(actual, expected, (a, b)=>areClose(a, b, epsilon));
}
function testEpsilon() {
    return (0, _engine.ENGINE).backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 : TEST_EPSILON_FLOAT16;
}
function expectArraysPredicate(actual, expected, predicate) {
    let checkClassType = true;
    if ((0, _util.isTypedArray)(actual) || (0, _util.isTypedArray)(expected)) checkClassType = false;
    if ((0, _util.isTypedArray)(actual) && (0, _util.isTypedArray)(expected)) checkClassType = true;
    if (checkClassType) {
        const aType = actual.constructor.name;
        const bType = expected.constructor.name;
        if (aType !== bType) throw new Error(`Arrays are of different type. Actual: ${aType}. ` + `Expected: ${bType}`);
    }
    if (Array.isArray(actual) && Array.isArray(expected)) {
        const actualShape = (0, _tensorUtilEnv.inferShape)(actual);
        const expectedShape = (0, _tensorUtilEnv.inferShape)(expected);
        if (!(0, _util.arraysEqual)(actualShape, expectedShape)) throw new Error(`Arrays have different shapes. ` + `Actual: [${actualShape}]. Expected: [${expectedShape}]`);
    }
    const actualFlat = (0, _util.isTypedArray)(actual) ? actual : (0, _util.flatten)(actual);
    const expectedFlat = (0, _util.isTypedArray)(expected) ? expected : (0, _util.flatten)(expected);
    if (actualFlat.length !== expectedFlat.length) throw new Error(`Arrays have different lengths actual: ${actualFlat.length} vs ` + `expected: ${expectedFlat.length}.\n` + `Actual:   ${actualFlat}.\n` + `Expected: ${expectedFlat}.`);
    for(let i = 0; i < expectedFlat.length; ++i){
        const a = actualFlat[i];
        const e = expectedFlat[i];
        if (!predicate(a, e)) throw new Error(`Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\n` + `Actual:   ${actualFlat}.\n` + `Expected: ${expectedFlat}.`);
    }
    if (typeof expect !== "undefined") expect().nothing();
}
function expectPromiseToFail(fn, done) {
    fn().then(()=>done.fail(), ()=>done());
    if (typeof expect !== "undefined") expect().nothing();
}
function expectArraysEqual(actual, expected) {
    const exp = typeof expected === "string" || typeof expected === "number" || typeof expected === "boolean" ? [
        expected
    ] : expected;
    if ((0, _util.isString)(actual) || (0, _util.isString)(actual[0]) || (0, _util.isString)(expected) || (0, _util.isString)(expected[0])) // tslint:disable-next-line: triple-equals
    return expectArraysPredicate(actual, exp, (a, b)=>a == b);
    return expectArraysPredicate(actual, expected, (a, b)=>areClose(a, b, 0));
}
function expectNumbersClose(a, e, epsilon) {
    if (epsilon == null) epsilon = testEpsilon();
    if (!areClose(a, e, epsilon)) throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);
    if (typeof expect !== "undefined") expect().nothing();
}
function areClose(a, e, epsilon) {
    if (!isFinite(a) && !isFinite(e)) return true;
    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) return false;
    return true;
}
function expectValuesInRange(actual, low, high) {
    for(let i = 0; i < actual.length; i++){
        if (actual[i] < low || actual[i] > high) throw new Error(`Value out of range:${actual[i]} low: ${low}, high: ${high}`);
    }
}
function expectArrayBuffersEqual(actual, expected) {
    // Safari does not like comparing ArrayBuffers directly. Wrapping in
    // a Float32Array solves this issue.
    const actualArray = new Float32Array(actual);
    const expectedArray = new Float32Array(expected);
    if (actualArray.length !== expectedArray.length) throw new Error("Expected ArrayBuffer to be of length " + `${expectedArray.length}, but it was ${actualArray.length}`);
    for(let i = 0; i < expectedArray.length; i++){
        if (actualArray[i] !== expectedArray[i]) throw new Error(`Expected ArrayBuffer value at ${i} to be ` + `${expectedArray[i]} but got ${actualArray[i]} instead`);
    }
}
function encodeStrings(a) {
    for(let i = 0; i < a.length; i++){
        const val = a[i];
        if (Array.isArray(val)) encodeStrings(val);
        else a[i] = (0, _util.encodeString)(val);
    }
    return a;
}
function createVideoElement(source) {
    const video = document.createElement("video");
    if ("playsInline" in video) // tslint:disable-next-line:no-any
    video.playsInline = true;
    video.muted = true;
    video.loop = true;
    video.style.position = "fixed";
    video.style.left = "0px";
    video.style.top = "0px";
    video.preload = "auto";
    video.appendChild(source);
    return new Promise((resolve)=>{
        video.addEventListener("loadeddata", (_)=>resolve(video));
        video.load();
    });
}
async function play(video) {
    await video.play();
    if ("requestVideoFrameCallback" in video) await new Promise((resolve)=>{
        // tslint:disable-next-line:no-any
        video.requestVideoFrameCallback(resolve);
    });
}

},{"./engine":"1g2qo","./tensor_util_env":"6vfHI","./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"72nVa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "randomNormal", ()=>randomNormal);
var _utilBase = require("../util_base");
var _buffer = require("./buffer");
var _operation = require("./operation");
var _randUtil = require("./rand_util");
/**
 * Creates a `tf.Tensor` with values sampled from a normal distribution.
 *
 * ```js
 * tf.randomNormal([2, 2]).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param mean The mean of the normal distribution.
 * @param stdDev The standard deviation of the normal distribution.
 * @param dtype The data type of the output.
 * @param seed The seed for the random number generator.
 *
 * @doc {heading: 'Tensors', subheading: 'Random'}
 */ function randomNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    if (dtype != null && dtype === "bool") throw new Error(`Unsupported data type ${dtype}`);
    const randGauss = new (0, _randUtil.MPRandGauss)(mean, stdDev, dtype, false, seed);
    const res = (0, _buffer.buffer)(shape, dtype);
    for(let i = 0; i < res.values.length; i++)res.values[i] = randGauss.nextValue();
    return res.toTensor();
}
const randomNormal = /* @__PURE__ */ (0, _operation.op)({
    randomNormal_
});

},{"../util_base":"4Vo4a","./buffer":"dYyY7","./operation":"ip64W","./rand_util":"3z7XO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eOkY1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "randomStandardNormal", ()=>randomStandardNormal);
var _operation = require("./operation");
var _randomNormal = require("./random_normal");
/**
 * Creates a `tf.Tensor` with values sampled from a normal distribution.
 *
 * The generated values will have mean 0 and standard deviation 1.
 *
 * ```js
 * tf.randomStandardNormal([2, 2]).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param dtype The data type of the output.
 * @param seed The seed for the random number generator.
 *
 * @doc {heading: 'Tensors', subheading: 'Random'}
 */ function randomStandardNormal_(shape, dtype, seed) {
    if (dtype != null && dtype === "bool") throw new Error(`Unsupported data type ${dtype}`);
    return (0, _randomNormal.randomNormal)(shape, 0, 1, dtype, seed);
}
const randomStandardNormal = /* @__PURE__ */ (0, _operation.op)({
    randomStandardNormal_
});

},{"./operation":"ip64W","./random_normal":"72nVa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8LDYI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "randomUniform", ()=>randomUniform);
var _utilBase = require("../util_base");
var _buffer = require("./buffer");
var _operation = require("./operation");
var _randUtil = require("./rand_util");
/**
 * Creates a `tf.Tensor` with values sampled from a uniform distribution.
 *
 * The generated values follow a uniform distribution in the range [minval,
 * maxval). The lower bound minval is included in the range, while the upper
 * bound maxval is excluded.
 *
 * ```js
 * tf.randomUniform([2, 2]).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param minval The lower bound on the range of random values to generate.
 *   Defaults to 0.
 * @param maxval The upper bound on the range of random values to generate.
 *   Defaults to 1.
 * @param dtype The data type of the output tensor. Defaults to 'float32'.
 * @param seed An optional int. Defaults to 0. If seed is set to be non-zero,
 *   the random number generator is seeded by the given seed. Otherwise, it is
 *   seeded by a random seed.
 *
 * @doc {heading: 'Tensors', subheading: 'Random'}
 */ function randomUniform_(shape, minval = 0, maxval = 1, dtype = "float32", seed) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    const res = (0, _buffer.buffer)(shape, dtype);
    const random = new (0, _randUtil.UniformRandom)(minval, maxval, null, seed);
    for(let i = 0; i < res.values.length; i++)res.values[i] = random.nextValue();
    return res.toTensor();
}
const randomUniform = /* @__PURE__ */ (0, _operation.op)({
    randomUniform_
});

},{"../util_base":"4Vo4a","./buffer":"dYyY7","./operation":"ip64W","./rand_util":"3z7XO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a0IEB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "randomUniformInt", ()=>randomUniformInt);
var _operation = require("./operation");
var _randomUniform = require("./random_uniform");
/**
 * Creates a `tf.Tensor` with integers sampled from a uniform distribution.
 *
 * The generated values are uniform integers in the range [minval, maxval). The
 * lower bound minval is included in the range, while the upper bound maxval is
 * excluded.
 *
 * ```js
 * tf.randomUniformInt([2, 2], 0, 10).print();
 * ```
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param minval Inclusive lower bound on the generated integers.
 * @param maxval Exclusive upper bound on the generated integers.
 * @param seed An optional int. Defaults to 0. If seed is set to be non-zero,
 *   the random number generator is seeded by the given seed. Otherwise, it is
 *   seeded by a random seed.
 *
 * @doc {heading: 'Tensors', subheading: 'Random'}
 */ function randomUniformInt_(shape, minval, maxval, seed) {
    // TODO(mattsoulanille): Handle optional seed2 input.
    return (0, _randomUniform.randomUniform)(shape, minval, maxval, "int32", seed);
}
const randomUniformInt = /* @__PURE__ */ (0, _operation.op)({
    randomUniformInt_
});

},{"./operation":"ip64W","./random_uniform":"8LDYI","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3seHd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.
 *
 * The tensor is a half-open interval meaning it includes start, but
 * excludes stop. Decrementing ranges and negative step values are also
 * supported.
 *
 *
 * ```js
 * tf.range(0, 9, 2).print();
 * ```
 *
 * @param start An integer start value
 * @param stop An integer stop value
 * @param step An integer increment (will default to 1 or -1)
 * @param dtype The data type of the output tensor. Defaults to 'float32'.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "range", ()=>range);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
function range(start, stop, step = 1, dtype = "float32") {
    if (step === 0) throw new Error("Cannot have a step of zero");
    const attrs = {
        start,
        stop,
        step,
        dtype
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Range), {} /* inputs */ , attrs);
}

},{"../engine":"1g2qo","../kernel_names":"hniYO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8i3Ch":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "real", ()=>real);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns the real part of a complex (or real) tensor.
 *
 * Given a tensor input, this operation returns a tensor of type float that is
 * the real part of each element in input considered as a complex number.
 *
 * If the input is real, it simply makes a clone.
 *
 * ```js
 * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
 * tf.real(x).print();
 * ```
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function real_(input) {
    const $input = (0, _tensorUtilEnv.convertToTensor)(input, "input", "real");
    const inputs = {
        input: $input
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Real), inputs);
}
const real = /* @__PURE__ */ (0, _operation.op)({
    real_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bxIjM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reciprocal", ()=>reciprocal);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes reciprocal of x element-wise: `1 / x`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, 2]);
 *
 * x.reciprocal().print();  // or tf.reciprocal(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function reciprocal_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "reciprocal");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Reciprocal), inputs);
}
const reciprocal = /* @__PURE__ */ (0, _operation.op)({
    reciprocal_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lcv3E":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "relu", ()=>relu);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes rectified linear element-wise: `max(x, 0)`.
 *
 * ```js
 * const x = tf.tensor1d([-1, 2, -3, 4]);
 *
 * x.relu().print();  // or tf.relu(x)
 * ```
 * @param x The input tensor. If the dtype is `bool`, the output dtype will be
 *     `int32`.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function relu_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "relu");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Relu), inputs);
}
const relu = /* @__PURE__ */ (0, _operation.op)({
    relu_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cvYLK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "relu6", ()=>relu6);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`.
 *
 * ```js
 * const x = tf.tensor1d([-1, 2, -3, 8]);
 *
 * x.relu6().print();  // or tf.relu6(x)
 * ```
 * @param x The input tensor. If the dtype is `bool`, the output dtype will be
 *     `int32`.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function relu6_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "relu6");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Relu6), inputs);
}
const relu6 = /* @__PURE__ */ (0, _operation.op)({
    relu6_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9yp4z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reverse", ()=>reverse);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Reverses a `tf.Tensor` along a specified axis.
 *
 * Also available are stricter rank-specific methods that assert that `x` is
 * of the given rank:
 *   - `tf.reverse1d`
 *   - `tf.reverse2d`
 *   - `tf.reverse3d`
 *   - `tf.reverse4d`
 *
 * Except `tf.reverse1d` (which does not have axis param), all methods have
 * same signature as this method.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 *
 * x.reverse().print();
 * ```
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * const axis = 1;
 * x.reverse(axis).print();
 * ```
 * @param x The input tensor to be reversed.
 * @param axis The set of dimensions to reverse. Must be in the
 *     range [-rank(x), rank(x)). Defaults to all axes.
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function reverse_(x, axis) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "reverse");
    const inputs = {
        x: $x
    };
    const attrs = {
        dims: axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Reverse), inputs, attrs);
}
const reverse = /* @__PURE__ */ (0, _operation.op)({
    reverse_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1LPYc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reverse1d", ()=>reverse1d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _reverse = require("./reverse");
/**
 * Reverses a `tf.Tensor1D`.
 *
 * @param x The input tensor.
 */ function reverse1d_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "reverse");
    _util.assert($x.rank === 1, ()=>`Error in reverse1D: x must be rank 1 but got rank ${$x.rank}.`);
    return (0, _reverse.reverse)($x, 0);
}
const reverse1d = /* @__PURE__ */ (0, _operation.op)({
    reverse1d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./reverse":"9yp4z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7RrO2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reverse2d", ()=>reverse2d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _reverse = require("./reverse");
/**
 * Reverses a `tf.Tensor2D` along a specified axis.
 *
 * @param x The input tensor.
 * @param axis The set of dimensions to reverse. Must be in the
 *     range [-rank(x), rank(x)). Defaults to all axes.
 */ function reverse2d_(x, axis) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "reverse");
    _util.assert($x.rank === 2, ()=>`Error in reverse2D: x must be rank 2 but got rank ${$x.rank}.`);
    return (0, _reverse.reverse)($x, axis);
}
const reverse2d = /* @__PURE__ */ (0, _operation.op)({
    reverse2d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./reverse":"9yp4z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jdnrR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reverse3d", ()=>reverse3d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _reverse = require("./reverse");
/**
 * Reverses a `tf.Tensor3D` along a specified axis.
 *
 * @param x The input tensor.
 * @param axis The set of dimensions to reverse. Must be in the
 *     range [-rank(x), rank(x)). Defaults to all axes.
 */ function reverse3d_(x, axis) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "reverse");
    _util.assert($x.rank === 3, ()=>`Error in reverse3D: x must be rank 3 but got rank ${$x.rank}.`);
    return (0, _reverse.reverse)($x, axis);
}
const reverse3d = /* @__PURE__ */ (0, _operation.op)({
    reverse3d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./reverse":"9yp4z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7dqzq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reverse4d", ()=>reverse4d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _reverse = require("./reverse");
/**
 * Reverses a `tf.Tensor4D` along a specified axis.
 *
 * @param x The input tensor.
 * @param axis The set of dimensions to reverse. Must be in the
 *     range [-rank(x), rank(x)). Defaults to all axes.
 */ function reverse4d_(x, axis) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "reverse");
    _util.assert($x.rank === 4, ()=>`Error in reverse4D: x must be rank 4 but got rank ${$x.rank}.`);
    return (0, _reverse.reverse)($x, axis);
}
const reverse4d = /* @__PURE__ */ (0, _operation.op)({
    reverse4d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./reverse":"9yp4z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4aCvQ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "round", ()=>round);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes round of input `tf.Tensor` element-wise: `round(x)`.
 * It implements banker's rounding.
 *
 * ```js
 * const x = tf.tensor1d([.6, 1.1, -3.3]);
 *
 * x.round().print();  // or tf.round(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function round_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "round");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Round), inputs);
}
const round = /* @__PURE__ */ (0, _operation.op)({
    round_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7D6sy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rsqrt", ()=>rsqrt);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes reciprocal of square root of the input `tf.Tensor` element-wise:
 * `y = 1 / sqrt(x)`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 4, -1]);
 *
 * x.rsqrt().print();  // or tf.rsqrt(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function rsqrt_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "rsqrt", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Rsqrt), inputs);
}
const rsqrt = /* @__PURE__ */ (0, _operation.op)({
    rsqrt_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kGS0T":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "selu", ()=>selu);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes scaled exponential linear element-wise.
 *
 * `x < 0 ? scale * alpha * (exp(x) - 1) : scale * x`
 *
 * ```js
 * const x = tf.tensor1d([-1, 2, -3, 4]);
 *
 * x.selu().print();  // or tf.selu(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function selu_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "selu");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Selu), inputs);
}
const selu = /* @__PURE__ */ (0, _operation.op)({
    selu_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1MPx9":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "separableConv2d", ()=>separableConv2d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _conv2D = require("./conv2d");
var _depthwiseConv2D = require("./depthwise_conv2d");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * 2-D convolution with separable filters.
 *
 * Performs a depthwise convolution that acts separately on channels followed
 * by a pointwise convolution that mixes channels. Note that this is
 * separability between dimensions [1, 2] and 3, not spatial separability
 * between dimensions 1 and 2.
 *
 * See
 * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](
 *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)
 * for more details.
 *
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape
 *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is
 *     the filter used in the first step.
 * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape
 *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is
 *     the filter used in the second step.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`. If strides is a single number, then `strideHeight ==
 * strideWidth`.
 * @param pad The type of padding algorithm.
 *   - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *   - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels]. Only "NHWC" is currently supported.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */ function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation = [
    1,
    1
], dataFormat = "NHWC") {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "separableConv2d");
    const $depthwiseFilter = (0, _tensorUtilEnv.convertToTensor)(depthwiseFilter, "depthwiseFilter", "separableConv2d");
    const $pointwiseFilter = (0, _tensorUtilEnv.convertToTensor)(pointwiseFilter, "pointwiseFilter", "separableConv2d");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    if (dataFormat === "NCHW") throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
    _util.assert(x4D.rank === 4, ()=>`Error in separableConv2d: input must be rank 4, but got ` + `rank ${x4D.rank}.`);
    _util.assert($depthwiseFilter.rank === 4, ()=>`Error in separableConv2d: depthwise filter must be rank 4, but ` + `got rank ${$depthwiseFilter.rank}.`);
    _util.assert($pointwiseFilter.rank === 4, ()=>`Error in separableConv2d: pointwise filter must be rank 4, but ` + `got rank ${$depthwiseFilter.rank}.`);
    _util.assert($pointwiseFilter.shape[0] === 1, ()=>`Error in separableConv2d: the first dimension of pointwise filter ` + ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);
    _util.assert($pointwiseFilter.shape[1] === 1, ()=>`Error in separableConv2d: the second dimension of pointwise ` + `filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
    const inChannels = $depthwiseFilter.shape[2];
    const channelMultiplier = $depthwiseFilter.shape[3];
    _util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, ()=>`Error in separableConv2d: the third dimension of pointwise filter ` + `must be ${inChannels * channelMultiplier}, ` + `but got ${$pointwiseFilter.shape[2]}.`);
    const depthwise = (0, _depthwiseConv2D.depthwiseConv2d)(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
    const pointwiseStride = 1;
    const res = (0, _conv2D.conv2d)(depthwise, $pointwiseFilter, pointwiseStride, "valid", dataFormat);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const separableConv2d = /* @__PURE__ */ (0, _operation.op)({
    separableConv2d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./conv2d":"lmJyO","./depthwise_conv2d":"cZHpb","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8tR1Z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "setdiff1dAsync", ()=>setdiff1dAsync);
var _tensor = require("../tensor");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
/**
 * Computes the difference between two lists of numbers.
 *
 * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`
 * that represents all values that are in `x` but not in `y`. The returned
 * Tensor `out` is sorted in the same order that the numbers appear in `x`
 * (duplicates are preserved). This operation also returns a Tensor indices that
 * represents the position of each out element in `x`. In other words:
 *
 * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`
 *
 * ```js
 * const x = [1, 2, 3, 4, 5, 6];
 * const y = [1, 3, 5];
 *
 * const [out, indices] = await tf.setdiff1dAsync(x, y);
 * out.print(); // [2, 4, 6]
 * indices.print(); // [1, 3, 5]
 * ```
 *
 * @param x 1-D Tensor. Values to keep.
 * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the
 *     output.
 * @returns Promise of Tensor tuple [out, indices].
 *  out: Tensor with the same type as x.
 *  indices: A Tensor of type int32.
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ async function setdiff1dAsync_(x, y) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "setdiff1d");
    const $y = (0, _tensorUtilEnv.convertToTensor)(y, "y", "setdiff1d");
    _util.assert($x.dtype === $y.dtype, ()=>`x and y should have the same dtype, but got x (${$x.dtype}) and y (${$y.dtype}).`);
    _util.assert($x.rank === 1, ()=>`x should be 1D tensor, but got x (${$x.shape}).`);
    _util.assert($y.rank === 1, ()=>`y should be 1D tensor, but got y (${$y.shape}).`);
    const xVals = await $x.data();
    const yVals = await $y.data();
    const ySet = new Set(yVals);
    let outputSize = 0;
    for(let i = 0; i < xVals.length; i++)if (!ySet.has(xVals[i])) outputSize++;
    const buffer = new (0, _tensor.TensorBuffer)([
        outputSize
    ], $x.dtype);
    const indices = new (0, _tensor.TensorBuffer)([
        outputSize
    ], "int32");
    for(let i = 0, p = 0; i < xVals.length; i++)if (!ySet.has(xVals[i])) {
        buffer.values[p] = xVals[i];
        indices.values[p] = i;
        p++;
    }
    return [
        buffer.toTensor(),
        indices.toTensor()
    ];
}
const setdiff1dAsync = setdiff1dAsync_;

},{"../tensor":"cJNoh","../tensor_util_env":"6vfHI","../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3bcO0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sign", ()=>sign);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Returns an element-wise indication of the sign of a number.
 *
 * ```js
 * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);
 *
 * x.sign().print();  // or tf.sign(x)
 * ```
 * @param x The input Tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function sign_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "sign");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Sign), inputs);
}
const sign = /* @__PURE__ */ (0, _operation.op)({
    sign_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gSMA7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sin", ()=>sin);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes sin of the input Tensor element-wise: `sin(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
 *
 * x.sin().print();  // or tf.sin(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function sin_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "sin", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Sin), inputs);
}
const sin = /* @__PURE__ */ (0, _operation.op)({
    sin_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5VZJu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sinh", ()=>sinh);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, 1, -1, .7]);
 *
 * x.sinh().print();  // or tf.sinh(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function sinh_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "sinh");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Sinh), inputs);
}
const sinh = /* @__PURE__ */ (0, _operation.op)({
    sinh_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4krOi":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "slice1d", ()=>slice1d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _slice = require("./slice");
/**
 * Extracts a 1D slice from 1D array starting at coordinates `begin` and is
 * of length `size`. See `slice` for details.
 */ function slice1d_(x, begin, size) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "slice1d");
    _util.assert($x.rank === 1, ()=>`slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);
    return (0, _slice.slice)($x, [
        begin
    ], [
        size
    ]);
}
const slice1d = /* @__PURE__ */ (0, _operation.op)({
    slice1d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./slice":"dr7Gq","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2Dy3M":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "slice2d", ()=>slice2d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _slice = require("./slice");
/**
 * Extracts a 2D slice from a 2D array starting at coordinates `begin` and
 * is of size `size`. See `slice` for details.
 */ function slice2d_(x, begin, size) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "slice2d");
    _util.assert($x.rank === 2, ()=>`slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);
    return (0, _slice.slice)($x, begin, size);
}
const slice2d = /* @__PURE__ */ (0, _operation.op)({
    slice2d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./slice":"dr7Gq","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3DCYE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "slice3d", ()=>slice3d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _slice = require("./slice");
/**
 * Extracts a 3D slice from a 3D array starting at coordinates `begin` and
 * is of size `size`. See `slice` for details.
 */ function slice3d_(x, begin, size) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "slice3d");
    _util.assert($x.rank === 3, ()=>`slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);
    return (0, _slice.slice)($x, begin, size);
}
const slice3d = /* @__PURE__ */ (0, _operation.op)({
    slice3d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./slice":"dr7Gq","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bMRWt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "slice4d", ()=>slice4d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _slice = require("./slice");
/**
 * Extracts a 4D slice from a 4D array starting at coordinates `begin` and
 * is of size `size`. See `slice` for details.
 */ function slice4d_(x, begin, size) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "slice4d");
    _util.assert($x.rank === 4, ()=>`slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);
    return (0, _slice.slice)($x, begin, size);
}
const slice4d = /* @__PURE__ */ (0, _operation.op)({
    slice4d_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./slice":"dr7Gq","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5C9bA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "softmax", ()=>softmax);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes the softmax normalized vector given the logits.
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3]);
 *
 * a.softmax().print();  // or tf.softmax(a)
 * ```
 *
 * ```js
 * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);
 *
 * a.softmax().print();  // or tf.softmax(a)
 * ```
 *
 * @param logits The logits array.
 * @param dim The dimension softmax would be performed on. Defaults to `-1`
 *     which indicates the last dimension.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */ function softmax_(logits, dim = -1) {
    const $logits = (0, _tensorUtilEnv.convertToTensor)(logits, "logits", "softmax", "float32");
    if (dim === -1) dim = $logits.rank - 1;
    if (dim !== $logits.rank - 1) throw Error("Softmax along a non-last dimension is not yet supported. " + `Logits was rank ${$logits.rank} and dim was ${dim}`);
    const inputs = {
        logits: $logits
    };
    const attrs = {
        dim
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Softmax), inputs, attrs);
}
const softmax = /* @__PURE__ */ (0, _operation.op)({
    softmax_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2vcIN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fft", ()=>fft);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _util = require("../../util");
var _operation = require("../operation");
/**
 * Fast Fourier transform.
 *
 * Computes the 1-dimensional discrete Fourier transform over the inner-most
 * dimension of input.
 *
 * ```js
 * const real = tf.tensor1d([1, 2, 3]);
 * const imag = tf.tensor1d([1, 2, 3]);
 * const x = tf.complex(real, imag);
 *
 * x.fft().print();  // tf.spectral.fft(x).print();
 * ```
 * @param input The complex input to compute an fft over.
 *
 * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
 */ function fft_(input) {
    (0, _util.assert)(input.dtype === "complex64", ()=>`The dtype for tf.spectral.fft() must be complex64 ` + `but got ${input.dtype}.`);
    const inputs = {
        input
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.FFT), inputs);
}
const fft = /* @__PURE__ */ (0, _operation.op)({
    fft_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../util":"ctavo","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4lzNl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ifft", ()=>ifft);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _util = require("../../util");
var _operation = require("../operation");
/**
 * Inverse fast Fourier transform.
 *
 * Computes the inverse 1-dimensional discrete Fourier transform over the
 * inner-most dimension of input.
 *
 * ```js
 * const real = tf.tensor1d([1, 2, 3]);
 * const imag = tf.tensor1d([1, 2, 3]);
 * const x = tf.complex(real, imag);
 *
 * x.ifft().print();  // tf.spectral.ifft(x).print();
 * ```
 * @param input The complex input to compute an ifft over.
 *
 * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
 */ function ifft_(input) {
    (0, _util.assert)(input.dtype === "complex64", ()=>`The dtype for tf.spectral.ifft() must be complex64 ` + `but got ${input.dtype}.`);
    const inputs = {
        input
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.IFFT), inputs);
}
const ifft = /* @__PURE__ */ (0, _operation.op)({
    ifft_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../util":"ctavo","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hzbzS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "irfft", ()=>irfft);
var _complex = require("../complex");
var _concat = require("../concat");
var _imag = require("../imag");
var _mul = require("../mul");
var _operation = require("../operation");
var _real = require("../real");
var _reshape = require("../reshape");
var _reverse = require("../reverse");
var _scalar = require("../scalar");
var _slice = require("../slice");
var _ifft = require("./ifft");
/**
 * Inversed real value input fast Fourier transform.
 *
 * Computes the 1-dimensional inversed discrete Fourier transform over the
 * inner-most dimension of the real input.
 *
 * ```js
 * const real = tf.tensor1d([1, 2, 3]);
 * const imag = tf.tensor1d([0, 0, 0]);
 * const x = tf.complex(real, imag);
 *
 * x.irfft().print();
 * ```
 * @param input The real value input to compute an irfft over.
 *
 * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
 */ function irfft_(input) {
    const innerDimensionSize = input.shape[input.shape.length - 1];
    const batch = input.size / innerDimensionSize;
    let ret;
    if (innerDimensionSize <= 2) {
        const complexInput = (0, _reshape.reshape)(input, [
            batch,
            innerDimensionSize
        ]);
        ret = (0, _ifft.ifft)(complexInput);
    } else {
        // The length of unique components of the DFT of a real-valued signal
        // is 2 * (input_len - 1)
        const outputShape = [
            batch,
            2 * (innerDimensionSize - 1)
        ];
        const realInput = (0, _reshape.reshape)((0, _real.real)(input), [
            batch,
            innerDimensionSize
        ]);
        const imagInput = (0, _reshape.reshape)((0, _imag.imag)(input), [
            batch,
            innerDimensionSize
        ]);
        const realConjugate = (0, _reverse.reverse)((0, _slice.slice)(realInput, [
            0,
            1
        ], [
            batch,
            innerDimensionSize - 2
        ]), 1);
        const imagConjugate = (0, _mul.mul)((0, _reverse.reverse)((0, _slice.slice)(imagInput, [
            0,
            1
        ], [
            batch,
            innerDimensionSize - 2
        ]), 1), (0, _scalar.scalar)(-1));
        const r = (0, _concat.concat)([
            realInput,
            realConjugate
        ], 1);
        const i = (0, _concat.concat)([
            imagInput,
            imagConjugate
        ], 1);
        const complexInput = (0, _reshape.reshape)((0, _complex.complex)(r, i), [
            outputShape[0],
            outputShape[1]
        ]);
        ret = (0, _ifft.ifft)(complexInput);
    }
    ret = (0, _real.real)(ret);
    // reshape the result if the input is 3D tensor.
    if (input.rank === 3 && input.shape[0] !== 0) {
        const temp = ret;
        const batch = input.shape[0];
        ret = (0, _reshape.reshape)(ret, [
            batch,
            ret.shape[0] / batch,
            ret.shape[1]
        ]);
        temp.dispose();
    }
    return ret;
}
const irfft = /* @__PURE__ */ (0, _operation.op)({
    irfft_
});

},{"../complex":"bPDAx","../concat":"1NOyL","../imag":"lDORm","../mul":"fOWrC","../operation":"ip64W","../real":"8i3Ch","../reshape":"ew3wZ","../reverse":"9yp4z","../scalar":"7lhC8","../slice":"dr7Gq","./ifft":"4lzNl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hnd5A":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rfft", ()=>rfft);
var _util = require("../../util");
var _complex = require("../complex");
var _concat = require("../concat");
var _imag = require("../imag");
var _operation = require("../operation");
var _real = require("../real");
var _reshape = require("../reshape");
var _slice = require("../slice");
var _split = require("../split");
var _zeros = require("../zeros");
var _zerosLike = require("../zeros_like");
var _fft = require("./fft");
/**
 * Real value input fast Fourier transform.
 *
 * Computes the 1-dimensional discrete Fourier transform over the
 * inner-most dimension of the real input.
 *
 * ```js
 * const real = tf.tensor1d([1, 2, 3]);
 *
 * real.rfft().print();
 * ```
 * @param input The real value input to compute an rfft over.
 *
 * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}
 */ function rfft_(input, fftLength) {
    (0, _util.assert)(input.dtype === "float32", ()=>`The dtype for rfft() must be real value but got ${input.dtype}`);
    let innerDimensionSize = input.shape[input.shape.length - 1];
    const batch = input.size / innerDimensionSize;
    let adjustedInput;
    if (fftLength != null && fftLength < innerDimensionSize) {
        // Need to crop
        const begin = input.shape.map((v)=>0);
        const size = input.shape.map((v)=>v);
        size[input.shape.length - 1] = fftLength;
        adjustedInput = (0, _slice.slice)(input, begin, size);
        innerDimensionSize = fftLength;
    } else if (fftLength != null && fftLength > innerDimensionSize) {
        // Need to pad with zeros
        const zerosShape = input.shape.map((v)=>v);
        zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize;
        adjustedInput = (0, _concat.concat)([
            input,
            (0, _zeros.zeros)(zerosShape)
        ], input.shape.length - 1);
        innerDimensionSize = fftLength;
    } else adjustedInput = input;
    // Complement the input with zero imaginary numbers.
    const zerosInput = (0, _zerosLike.zerosLike)(adjustedInput);
    const complexInput = (0, _reshape.reshape)((0, _complex.complex)(adjustedInput, zerosInput), [
        batch,
        innerDimensionSize
    ]);
    const ret = (0, _fft.fft)(complexInput);
    // Exclude complex conjugations. These conjugations are put symmetrically.
    const half = Math.floor(innerDimensionSize / 2) + 1;
    const realValues = (0, _real.real)(ret);
    const imagValues = (0, _imag.imag)(ret);
    const realComplexConjugate = (0, _split.split)(realValues, [
        half,
        innerDimensionSize - half
    ], realValues.shape.length - 1);
    const imagComplexConjugate = (0, _split.split)(imagValues, [
        half,
        innerDimensionSize - half
    ], imagValues.shape.length - 1);
    const outputShape = adjustedInput.shape.slice();
    outputShape[adjustedInput.shape.length - 1] = half;
    return (0, _reshape.reshape)((0, _complex.complex)(realComplexConjugate[0], imagComplexConjugate[0]), outputShape);
}
const rfft = /* @__PURE__ */ (0, _operation.op)({
    rfft_
});

},{"../../util":"ctavo","../complex":"bPDAx","../concat":"1NOyL","../imag":"lDORm","../operation":"ip64W","../real":"8i3Ch","../reshape":"ew3wZ","../slice":"dr7Gq","../split":"l0HF2","../zeros":"fcPHR","../zeros_like":"1Xjct","./fft":"2vcIN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l0HF2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "split", ()=>split);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Splits a `tf.Tensor` into sub tensors.
 *
 * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`
 * into `numOrSizeSplits` smaller tensors.
 * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.
 *
 * If `numOrSizeSplits` is a number array, splits `x` into
 * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the
 * same size as `x` except along dimension `axis` where the size is
 * `numOrSizeSplits[i]`.
 *
 * ```js
 * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
 * const [a, b] = tf.split(x, 2, 1);
 * a.print();
 * b.print();
 *
 * const [c, d, e] = tf.split(x, [1, 2, 1], 1);
 * c.print();
 * d.print();
 * e.print();
 * ```
 *
 * @param x The input tensor to split.
 * @param numOrSizeSplits Either an integer indicating the number of
 * splits along the axis or an array of integers containing the sizes of
 * each output tensor along the axis. If a number then it must evenly divide
 * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.
 * Can contain one -1 indicating that dimension is to be inferred.
 * @param axis The dimension along which to split. Defaults to 0 (the first
 * dim).
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function split_(x, numOrSizeSplits, axis = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "split");
    const inputs = {
        x: $x
    };
    const attr = {
        numOrSizeSplits,
        axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SplitV), inputs, attr);
}
const split = /* @__PURE__ */ (0, _operation.op)({
    split_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iHBgt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "squaredDifference", ()=>squaredDifference);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _broadcastUtil = require("./broadcast_util");
var _operation = require("./operation");
/**
 * Returns (a - b) * (a - b) element-wise.
 * Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
 * ```
 *
 * ```js
 * // Broadcast squared difference  a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */ function squaredDifference_(a, b) {
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "squaredDifference");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "squaredDifference");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    (0, _broadcastUtil.assertAndGetBroadcastShape)($a.shape, $b.shape);
    const inputs = {
        a: $a,
        b: $b
    };
    const attrs = {};
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SquaredDifference), inputs, attrs);
}
const squaredDifference = /* @__PURE__ */ (0, _operation.op)({
    squaredDifference_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","./broadcast_util":"eLo4J","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"VbY6Z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "squeeze", ()=>squeeze);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Removes dimensions of size 1 from the shape of a `tf.Tensor`.
 *
 * ```js
 * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);
 * x.squeeze().print();
 * ```
 *
 * @param x The input tensor to be squeezed.
 * @param axis An optional list of numbers. If specified, only
 *     squeezes the dimensions listed. The dimension index starts at 0. It
 * is an error to squeeze a dimension that is not 1.
 *
 * @doc {heading: 'Tensors', subheading: 'Transformations'}
 */ function squeeze_(x, axis) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "squeeze", "string_or_numeric");
    return (0, _reshape.reshape)($x, (0, _util.squeezeShape)($x.shape, axis).newShape);
}
const squeeze = /* @__PURE__ */ (0, _operation.op)({
    squeeze_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jeh7M":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stack", ()=>stack);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 * const b = tf.tensor1d([3, 4]);
 * const c = tf.tensor1d([5, 6]);
 * tf.stack([a, b, c]).print();
 * ```
 *
 * @param tensors A list of tensor objects with the same shape and dtype.
 * @param axis The axis to stack along. Defaults to 0 (the first dim).
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function stack_(tensors, axis = 0) {
    const $tensors = (0, _tensorUtilEnv.convertToTensorArray)(tensors, "tensors", "stack", "string_or_numeric");
    _util.assert($tensors.length >= 1, ()=>"Pass at least one tensor to tf.stack");
    if ($tensors.length > 0) _util.assert(axis <= $tensors[0].rank, ()=>"Axis must be <= rank of the tensor");
    const inputs = $tensors;
    const attrs = {
        axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Pack), inputs, attrs);
}
const stack = /* @__PURE__ */ (0, _operation.op)({
    stack_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bBBT4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "step", ()=>step);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha`
 *
 * ```js
 * const x = tf.tensor1d([0, 2, -1, -3]);
 *
 * x.step(.5).print();  // or tf.step(x, .5)
 * ```
 * @param x The input tensor.
 * @param alpha The gradient when input is negative. Defaults to 0.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function step_(x, alpha = 0.0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "step");
    const inputs = {
        x: $x
    };
    const attrs = {
        alpha
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Step), inputs, attrs);
}
const step = /* @__PURE__ */ (0, _operation.op)({
    step_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9Kwrw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stridedSlice", ()=>stridedSlice);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Extracts a strided slice of a tensor.
 *
 * Roughly speaking, this op extracts a slice of size (end-begin)/stride from
 * the given input tensor (x). Starting at the location specified by begin the
 * slice continues by adding stride to the index until all dimensions are not
 * less than end. Note that a stride can be negative, which causes a reverse
 * slice.
 *
 * ```js
 * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
 *    [3, 2, 3]);
 * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]
 * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],
 *                                                     // [4, 4, 4]]]
 * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],
 *                                                     // [3, 3, 3]]]
 * ```
 *
 * @param x The tensor to stride slice.
 * @param begin The coordinates to start the slice from.
 * @param end: The coordinates to end the slice at.
 * @param strides: The size of the slice.
 * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored
 *      and the fullest possible range in that dimension is used instead.
 * @param endMask: If the ith bit of endMask is set, end[i] is ignored
 *      and the fullest possible range in that dimension is used instead.
 * @param shrinkAxisMask: a bitmask where bit i implies that
 * the ith specification should shrink the dimensionality. begin and end must
 * imply a slice of size 1 in the dimension.
 *
 * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
 */ function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "stridedSlice", "string_or_numeric");
    const inputs = {
        x: $x
    };
    const attrs = {
        begin,
        end,
        strides,
        beginMask,
        endMask,
        ellipsisMask,
        newAxisMask,
        shrinkAxisMask
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.StridedSlice), inputs, attrs);
}
const stridedSlice = /* @__PURE__ */ (0, _operation.op)({
    stridedSlice_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c36dQ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tan", ()=>tan);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`
 *
 * ```js
 * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);
 *
 * x.tan().print();  // or tf.tan(x)
 * ```
 * @param x The input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Basic math'}
 */ function tan_(x) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "tan", "float32");
    const inputs = {
        x: $x
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Tan), inputs);
}
const tan = /* @__PURE__ */ (0, _operation.op)({
    tan_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aEL9e":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.
 *
 * The same functionality can be achieved with `tf.tensor`, but in general
 * we recommend using `tf.tensor1d` as it makes the code more readable.
 *
 * ```js
 * tf.tensor1d([1, 2, 3]).print();
 * ```
 *
 * @param values The values of the tensor. Can be array of numbers,
 *     or a `TypedArray`.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "tensor1d", ()=>tensor1d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _tensorOpsUtil = require("./tensor_ops_util");
function tensor1d(values, dtype) {
    (0, _util.assertNonNull)(values);
    const inferredShape = (0, _tensorUtilEnv.inferShape)(values, dtype);
    if (inferredShape.length !== 1) throw new Error("tensor1d() requires values to be a flat/TypedArray");
    const shape = null;
    return (0, _tensorOpsUtil.makeTensor)(values, shape, inferredShape, dtype);
}

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"faX5H":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.
 *
 * The same functionality can be achieved with `tf.tensor`, but in general
 * we recommend using `tf.tensor2d` as it makes the code more readable.
 *
 *  ```js
 * // Pass a nested array.
 * tf.tensor2d([[1, 2], [3, 4]]).print();
 * ```
 * ```js
 * // Pass a flat array and specify a shape.
 * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();
 * ```
 *
 * @param values The values of the tensor. Can be nested array of numbers,
 *     or a flat array, or a `TypedArray`.
 * @param shape The shape of the tensor. If not provided, it is inferred from
 *     `values`.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "tensor2d", ()=>tensor2d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _tensorOpsUtil = require("./tensor_ops_util");
function tensor2d(values, shape, dtype) {
    (0, _util.assertNonNull)(values);
    if (shape != null && shape.length !== 2) throw new Error("tensor2d() requires shape to have two numbers");
    const inferredShape = (0, _tensorUtilEnv.inferShape)(values, dtype);
    if (inferredShape.length !== 2 && inferredShape.length !== 1) throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");
    if (inferredShape.length === 1 && shape == null) throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");
    return (0, _tensorOpsUtil.makeTensor)(values, shape, inferredShape, dtype);
}

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lVDsd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.
 *
 * The same functionality can be achieved with `tf.tensor`, but in general
 * we recommend using `tf.tensor3d` as it makes the code more readable.
 *
 *  ```js
 * // Pass a nested array.
 * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
 * ```
 * ```js
 * // Pass a flat array and specify a shape.
 * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
 * ```
 *
 * @param values The values of the tensor. Can be nested array of numbers,
 *     or a flat array, or a `TypedArray`.
 * @param shape The shape of the tensor. If not provided,  it is inferred from
 *     `values`.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "tensor3d", ()=>tensor3d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _tensorOpsUtil = require("./tensor_ops_util");
function tensor3d(values, shape, dtype) {
    (0, _util.assertNonNull)(values);
    if (shape != null && shape.length !== 3) throw new Error("tensor3d() requires shape to have three numbers");
    const inferredShape = (0, _tensorUtilEnv.inferShape)(values, dtype);
    if (inferredShape.length !== 3 && inferredShape.length !== 1) throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");
    if (inferredShape.length === 1 && shape == null) throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");
    return (0, _tensorOpsUtil.makeTensor)(values, shape, inferredShape, dtype);
}

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5QFb1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.
 *
 * The same functionality can be achieved with `tf.tensor`, but in general
 * we recommend using `tf.tensor4d` as it makes the code more readable.
 *
 *  ```js
 * // Pass a nested array.
 * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
 * ```
 * ```js
 * // Pass a flat array and specify a shape.
 * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
 * ```
 *
 * @param values The values of the tensor. Can be nested array of numbers,
 *     or a flat array, or a `TypedArray`.
 * @param shape The shape of the tensor. Optional. If not provided,
 *   it is inferred from `values`.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "tensor4d", ()=>tensor4d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _tensorOpsUtil = require("./tensor_ops_util");
function tensor4d(values, shape, dtype) {
    (0, _util.assertNonNull)(values);
    if (shape != null && shape.length !== 4) throw new Error("tensor4d() requires shape to have four numbers");
    const inferredShape = (0, _tensorUtilEnv.inferShape)(values, dtype);
    if (inferredShape.length !== 4 && inferredShape.length !== 1) throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");
    if (inferredShape.length === 1 && shape == null) throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");
    return (0, _tensorOpsUtil.makeTensor)(values, shape, inferredShape, dtype);
}

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7LoZX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.
 *
 * The same functionality can be achieved with `tf.tensor`, but in general
 * we recommend using `tf.tensor5d` as it makes the code more readable.
 *
 *  ```js
 * // Pass a nested array.
 * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print();
 * ```
 * ```js
 * // Pass a flat array and specify a shape.
 * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();
 * ```
 *
 * @param values The values of the tensor. Can be nested array of numbers,
 *     or a flat array, or a `TypedArray`.
 * @param shape The shape of the tensor. Optional. If not provided,
 *   it is inferred from `values`.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "tensor5d", ()=>tensor5d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _tensorOpsUtil = require("./tensor_ops_util");
function tensor5d(values, shape, dtype) {
    (0, _util.assertNonNull)(values);
    if (shape != null && shape.length !== 5) throw new Error("tensor5d() requires shape to have five numbers");
    const inferredShape = (0, _tensorUtilEnv.inferShape)(values, dtype);
    if (inferredShape.length !== 5 && inferredShape.length !== 1) throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");
    if (inferredShape.length === 1 && shape == null) throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");
    return (0, _tensorOpsUtil.makeTensor)(values, shape, inferredShape, dtype);
}

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jFXe2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.
 *
 * The same functionality can be achieved with `tf.tensor`, but in general
 * we recommend using `tf.tensor6d` as it makes the code more readable.
 *
 *  ```js
 * // Pass a nested array.
 * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();
 * ```
 * ```js
 * // Pass a flat array and specify a shape.
 * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();
 * ```
 *
 * @param values The values of the tensor. Can be nested array of numbers,
 *     or a flat array, or a `TypedArray`.
 * @param shape The shape of the tensor. Optional. If not provided,
 *   it is inferred from `values`.
 * @param dtype The data type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "tensor6d", ()=>tensor6d);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _tensorOpsUtil = require("./tensor_ops_util");
function tensor6d(values, shape, dtype) {
    (0, _util.assertNonNull)(values);
    if (shape != null && shape.length !== 6) throw new Error("tensor6d() requires shape to have six numbers");
    const inferredShape = (0, _tensorUtilEnv.inferShape)(values, dtype);
    if (inferredShape.length !== 6 && inferredShape.length !== 1) throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");
    if (inferredShape.length === 1 && shape == null) throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");
    shape = shape || inferredShape;
    return (0, _tensorOpsUtil.makeTensor)(values, shape, inferredShape, dtype);
}

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./tensor_ops_util":"adYmX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1L18R":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tensorScatterUpdate", ()=>tensorScatterUpdate);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
var _scatterNdUtil = require("./scatter_nd_util");
/**
 * Creates a new tensor by applying sparse updates to individual
 * values or slices to the passed in tensor according to
 * indices. This operator is the similar to scatterNd op, except that the
 * udpates are scattered on an existing tensor (as opposed to a zero-tensor).
 *
 * If indices contains duplicates, then we pick the last update for the index.
 *
 * If an out of bound index is found on CPU, an error is returned.
 *
 * Warning: There are some GPU specific semantics for this operation.
 *  - If an out of bound index is found, the index is ignored.
 *  - The order in which updates are applied is nondeterministic, so the output
 * will be nondeterministic if indices contains duplicates.
 * ```js
 * const shape = [8];
 * const tensor = tf.ones(shape);
 * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');
 * const updates = tf.tensor1d([9, 10, 11, 12]);
 *
 * tf.tensorScatterUpdate(tensor, indices, updates).print();
 *    //[1, 11, 1, 10, 9, 1, 1, 12]
 * ```
 *
 * @param tensor A Tensor. Tensor to copy/update.
 * @param indices The tensor contains the indices into the output tensor, must
 *     have at least 2 axes: (num_updates, index_depth).
 * @param updates The tensor contains the value for the indices.
 *
 * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
 */ function tensorScatterUpdate_(tensor, indices, updates) {
    const $tensor = (0, _tensorUtilEnv.convertToTensor)(tensor, "tensor", "tensorScatterupdate");
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "tensorScatterupdate", "int32");
    const $updates = (0, _tensorUtilEnv.convertToTensor)(updates, "updates", "tensorScatterupdate");
    _scatterNdUtil.validateInput($updates, $indices, $tensor.shape);
    if ($tensor.dtype !== $updates.dtype) throw new Error(`tensor and updates must have the same dtype, instead they are ${$tensor.dtype} and ${$updates.dtype}.`);
    const inputs = {
        tensor: $tensor,
        indices: $indices,
        updates: $updates
    };
    const attrs = {};
    // tslint:disable-next-line: no-unnecessary-type-assertion
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.TensorScatterUpdate), inputs, attrs);
}
const tensorScatterUpdate = (0, _operation.op)({
    tensorScatterUpdate_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","./scatter_nd_util":"dDUQ5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dDUQ5":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Check whether updates.shape = indices.shape[:batchDim] +
 * shape[sliceDim:]
 *
 * @param x The input tensor.
 */ parcelHelpers.export(exports, "validateUpdateShape", ()=>validateUpdateShape);
/**
 * Validate scatter nd inputs.
 *
 * @param update The tensor contains the update values.
 * @param indices The tensor contains the indices for the update values.
 * @param shape The shape of the output tensor.
 */ parcelHelpers.export(exports, "validateInput", ()=>validateInput);
/**
 * Calculate the shape information for the output.
 *
 * @param update The tensor contains the update values.
 * @param indices The tensor contains the indices for the update values.
 * @param shape The shape of the output tensor.
 *
 * @returns ScatterShapeInfo
 */ parcelHelpers.export(exports, "calculateShapes", ()=>calculateShapes);
var _util = require("../util");
function validateUpdateShape(shape, indices, updates) {
    const sliceDim = indices.rank > 1 ? indices.shape[indices.rank - 1] : 1;
    const batchDim = indices.rank > 1 ? indices.rank - 1 : 1;
    const shapeError = "Must have updates.shape = indices.shape[:batchDim] + " + `shape[sliceDim:], got updates.shape: ${updates.shape}` + `, indices.shape: ${indices.shape}, shape: ${shape}` + `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
    if (updates.rank < batchDim) throw new Error(shapeError + ` update.rank < ${batchDim}. `);
    if (shape.length < sliceDim + (updates.rank - batchDim)) throw new Error(shapeError + ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
    if (updates.rank !== batchDim + shape.length - sliceDim) throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
    for(let d = 0; d < batchDim; ++d){
        if (updates.shape[d] !== indices.shape[d]) throw new Error(shapeError + ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
    }
    for(let d = 0; d < updates.rank - batchDim; ++d){
        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) throw new Error(shapeError + ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
    }
}
function validateInput(updates, indices, shape) {
    if (indices.rank < 1) throw new Error("tf.scatterND() expects the indices to be rank 1 or higher," + ` but the rank was ${indices.rank}.`);
    if (updates.rank < 1) throw new Error("tf.scatterND() expects the updates to be rank 1 or higher," + ` but the rank was ${updates.rank}.`);
    if (indices.dtype !== "int32") throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
    if (shape.length < 1) throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
    if (shape.length === 0) {
        if (indices.size === 0) throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
        if (updates.size === 0) throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
    }
    validateUpdateShape(shape, indices, updates);
}
function calculateShapes(updates, indices, shape) {
    // Calculate the number of dimensions in indices
    const indicesRank = indices.shape.length;
    const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
    // Calculate the number of elements that make up each slice of our updated
    // tensor. This allows us to work with flattened tensors and copy over whole
    // slices at a time.
    const totalNd = shape.length;
    let sliceSize = 1;
    for(let i = sliceRank; i < totalNd; ++i)sliceSize *= shape[i];
    const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
    const numUpdates = (0, _util.sizeFromShape)(indices.shape) / safeSliceDim;
    const strides = [
        ...(0, _util.computeStrides)(shape.slice(0, sliceRank)),
        1
    ];
    const outputSize = (0, _util.sizeFromShape)(shape);
    return {
        sliceRank,
        numUpdates,
        sliceSize,
        strides,
        outputSize
    };
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jUBl9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "topk", ()=>topk);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Finds the values and indices of the `k` largest entries along the last
 * dimension.
 *
 * If the input is a vector (rank=1), finds the k largest entries in the vector
 * and outputs their values and indices as vectors. Thus values[j] is the j-th
 * largest entry in input, and its index is indices[j].
 * For higher rank inputs, computes the top k entries along the last dimension.
 *
 * If two elements are equal, the lower-index element appears first.
 *
 * ```js
 * const a = tf.tensor2d([[1, 5], [4, 3]]);
 * const {values, indices} = tf.topk(a);
 * values.print();
 * indices.print();
 * ```
 * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.
 * @param k Number of top elements to look for along the last dimension.
 * @param sorted If true, the resulting `k` elements will be sorted by the
 *     values in descending order.
 *
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */ function topk_(x, k = 1, sorted = true) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "topk");
    if ($x.rank === 0) throw new Error("topk() expects the input to be of rank 1 or higher");
    const lastDim = $x.shape[$x.shape.length - 1];
    if (k < 0) throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);
    if (k > lastDim) throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) ` + `but got ${k}`);
    const inputs = {
        x: $x
    };
    const attrs = {
        k,
        sorted
    };
    const [values, indices] = (0, _engine.ENGINE).runKernel((0, _kernelNames.TopK), inputs, attrs);
    return {
        values,
        indices
    };
}
const topk = /* @__PURE__ */ (0, _operation.op)({
    topk_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jsVmm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "truncatedNormal", ()=>truncatedNormal);
var _utilBase = require("../util_base");
var _buffer = require("./buffer");
var _operation = require("./operation");
var _randUtil = require("./rand_util");
/**
 * Creates a `tf.Tensor` with values sampled from a truncated normal
 * distribution.
 *
 * ```js
 * tf.truncatedNormal([2, 2]).print();
 * ```
 *
 * The generated values follow a normal distribution with specified mean and
 * standard deviation, except that values whose magnitude is more than 2
 * standard deviations from the mean are dropped and re-picked.
 *
 * @param shape An array of integers defining the output tensor shape.
 * @param mean The mean of the normal distribution.
 * @param stdDev The standard deviation of the normal distribution.
 * @param dtype The data type of the output tensor.
 * @param seed The seed for the random number generator.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ function truncatedNormal_(shape, mean = 0, stdDev = 1, dtype, seed) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    if (dtype != null && dtype === "bool") throw new Error(`Unsupported data type $ { dtype }`);
    const randGauss = new (0, _randUtil.MPRandGauss)(mean, stdDev, dtype, true, seed);
    const res = (0, _buffer.buffer)(shape, dtype);
    for(let i = 0; i < res.values.length; i++)res.values[i] = randGauss.nextValue();
    return res.toTensor();
}
const truncatedNormal = /* @__PURE__ */ (0, _operation.op)({
    truncatedNormal_
});

},{"../util_base":"4Vo4a","./buffer":"dYyY7","./operation":"ip64W","./rand_util":"3z7XO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kc2B3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unique", ()=>unique);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Finds unique elements along an axis of a tensor.
 *
 * It returns a tensor `values` containing all of the unique elements along the
 * `axis` of the given tensor `x` in the same order that they occur along the
 * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor
 * `indices` the same size as the number of the elements in `x` along the `axis`
 * dimension. It contains the index in the unique output `values`.
 *
 * ```js
 * // A 1-D tensor
 * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]);
 * const {values, indices} = tf.unique(a);
 * values.print();   // [1, 2, 4, 7, 8,]
 * indices.print();  // [0, 0, 1, 2, 2, 2, 3, 4, 4]
 * ```
 *
 * ```js
 * // A 2-D tensor with axis=0
 * //
 * // 'a' is: [[1, 0, 0],
 * //          [1, 0, 0],
 * //          [2, 0, 0]]
 * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);
 * const {values, indices} = tf.unique(a, 0)
 * values.print();   // [[1, 0, 0],
 *                   //  [2, 0, 0]]
 * indices.print();  // [0, 0, 1]
 * ```
 *
 * ```js
 * // A 2-D tensor with axis=1
 * //
 * // 'a' is: [[1, 0, 0],
 * //          [1, 0, 0],
 * //          [2, 0, 0]]
 * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]);
 * const {values, indices} = tf.unique(a, 1)
 * values.print();   // [[1, 0],
 *                   //  [1, 0],
 *                   //  [2, 0]]
 * indices.print();  // [0, 1, 1]
 * ```
 * @param x A tensor (int32, string, bool).
 * @param axis The axis of the tensor to find the unique elements.
 * @returns [uniqueElements, indices] (see above for details)
 *
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */ function unique_(x, axis = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "unique", "string_or_numeric");
    (0, _util.assert)($x.rank > 0, ()=>"The input tensor must be at least 1D");
    const inputs = {
        x: $x
    };
    const attrs = {
        axis
    };
    const [values, indices] = (0, _engine.ENGINE).runKernel((0, _kernelNames.Unique), inputs, attrs);
    return {
        values,
        indices
    };
}
const unique = /* @__PURE__ */ (0, _operation.op)({
    unique_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8ZRDN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unsortedSegmentSum", ()=>unsortedSegmentSum);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Computes the sum along segments of a `tf.Tensor`.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');
 * const numSegments = 3;
 *
 * x.unsortedSegmentSum(segmentIds, numSegments).print()
 * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)
 * ```
 * @param x The `tf.Tensor` that will be summed along its segments.
 * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s
 * dimension along the `axis`.  Maps each element of `x` to a segment.
 * @param numSegments The number of distinct `segmentIds`.
 *
 * @doc {heading: 'Operations', subheading: 'Segment'}
 */ function unsortedSegmentSum_(x, segmentIds, numSegments) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "unsortedSegmentSum");
    const $segmentIds = (0, _tensorUtilEnv.convertToTensor)(segmentIds, "segmentIds", "unsortedSegmentSum", "int32");
    (0, _util.assert)((0, _util.isInt)(numSegments), ()=>"numSegments must be of dtype int");
    const inputs = {
        x: $x,
        segmentIds: $segmentIds
    };
    const attrs = {
        numSegments
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.UnsortedSegmentSum), inputs, attrs);
}
const unsortedSegmentSum = /* @__PURE__ */ (0, _operation.op)({
    unsortedSegmentSum_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"90Kbb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unstack", ()=>unstack);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _operation = require("./operation");
/**
 * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.
 *
 * ```js
 * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 *
 * tf.unstack(a).forEach(tensor => tensor.print());
 * ```
 *
 * @param x A tensor object.
 * @param axis The axis to unstack along. Defaults to 0 (the first dim).
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ function unstack_(x, axis = 0) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "unstack", "string_or_numeric");
    _util.assert(axis >= -$x.shape.length && axis < $x.shape.length, ()=>`Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
    const inputs = {
        value: $x
    };
    const attrs = {
        axis
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Unpack), inputs, attrs);
}
const unstack = /* @__PURE__ */ (0, _operation.op)({
    unstack_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"csQSE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Searches for where a value would go in a sorted sequence.
 *
 * This is not a method for checking containment (like javascript in).
 *
 * The typical use case for this operation is "binning", "bucketing", or
 * "discretizing". The values are assigned to bucket-indices based on the edges
 * listed in 'sortedSequence'. This operation returns the bucket-index for each
 * value.
 *
 * The index returned corresponds to the first edge greater than the value.
 *
 * The axis is not settable for this operation. It always operates on the
 * innermost dimension (axis=-1). The operation will accept any number of outer
 * dimensions.
 *
 * Note: This operation assumes that 'upperBound' is sorted along the
 * innermost axis, maybe using 'sort(..., axis=-1)'. If the sequence is not
 * sorted no error is raised and the content of the returned tensor is not well
 * defined.
 *
 * ```js
 * const seq = tf.tensor1d([0, 3, 9, 10, 10]);
 * const values = tf.tensor1d([0, 4, 10]);
 * const result = tf.upperBound(seq, values);
 * result.print(); // [1, 2, 5]
 * ```
 * @param sortedSequence: N-D. Sorted sequence.
 * @param values: N-D. Search values.
 * @return An N-D int32 tensor the size of values containing the result of
 *     applying upper bound to each value. The result is not a global index to
 *     the entire Tensor, but the index in the last dimension.
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */ parcelHelpers.export(exports, "upperBound", ()=>upperBound);
var _searchSorted = require("./search_sorted");
function upperBound(sortedSequence, values) {
    return (0, _searchSorted.searchSorted)(sortedSequence, values, "right");
}

},{"./search_sorted":"4gaGh","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c3Bg5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates a new variable with the provided initial value.
 * ```js
 * const x = tf.variable(tf.tensor([1, 2, 3]));
 * x.assign(tf.tensor([4, 5, 6]));
 *
 * x.print();
 * ```
 *
 * @param initialValue Initial value for the tensor.
 * @param trainable If true, optimizers are allowed to update it.
 * @param name Name of the variable. Defaults to a unique id.
 * @param dtype If set, initialValue will be converted to the given type.
 *
 * @doc {heading: 'Tensors', subheading: 'Creation'}
 */ parcelHelpers.export(exports, "variable", ()=>variable);
var _engine = require("../engine");
function variable(initialValue, trainable = true, name, dtype) {
    return (0, _engine.ENGINE).makeVariable(initialValue, trainable, name, dtype);
}

},{"../engine":"1g2qo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2KVsP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "whereAsync", ()=>whereAsync);
var _whereImpl = require("../backends/where_impl");
var _tensorUtilEnv = require("../tensor_util_env");
/**
 * Returns the coordinates of true elements of condition.
 *
 * The coordinates are returned in a 2-D tensor where the first dimension (rows)
 * represents the number of true elements, and the second dimension (columns)
 * represents the coordinates of the true elements. Keep in mind, the shape of
 * the output tensor can vary depending on how many true values there are in
 * input. Indices are output in row-major order. The resulting tensor has the
 * shape `[numTrueElems, condition.rank]`.
 *
 * This is analogous to calling the python `tf.where(cond)` without an x or y.
 *
 * ```js
 * const cond = tf.tensor1d([false, false, true], 'bool');
 * const result = await tf.whereAsync(cond);
 * result.print();
 * ```
 *
 * @doc {heading: 'Operations', subheading: 'Logical'}
 */ async function whereAsync_(condition) {
    const $condition = (0, _tensorUtilEnv.convertToTensor)(condition, "condition", "whereAsync", "bool");
    const vals = await $condition.data();
    const res = (0, _whereImpl.whereImpl)($condition.shape, vals);
    if (condition !== $condition) $condition.dispose();
    return res;
}
const whereAsync = whereAsync_;

},{"../backends/where_impl":"lNCmF","../tensor_util_env":"6vfHI","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lNCmF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /** An implementation of the Where kernel shared between cpu and webgl */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "whereImpl", ()=>whereImpl);
var _buffer = require("../ops/buffer");
function whereImpl(condShape, condVals) {
    const indices = [];
    for(let i = 0; i < condVals.length; i++)if (condVals[i]) indices.push(i);
    const inBuffer = (0, _buffer.buffer)(condShape, "int32");
    const out = (0, _buffer.buffer)([
        indices.length,
        condShape.length
    ], "int32");
    for(let i = 0; i < indices.length; i++){
        const loc = inBuffer.indexToLoc(indices[i]);
        const offset = i * condShape.length;
        out.values.set(loc, offset);
    }
    return out.toTensor();
}

},{"../ops/buffer":"dYyY7","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jnpjK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "booleanMaskAsync", ()=>booleanMaskAsync);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _gather = require("./gather");
var _reshape = require("./reshape");
var _squeeze = require("./squeeze");
var _whereAsync = require("./where_async");
/**
 * Apply boolean mask to tensor.
 *
 * ```js
 * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
 * const mask = tf.tensor1d([1, 0, 1], 'bool');
 * const result = await tf.booleanMaskAsync(tensor, mask);
 * result.print();
 * ```
 *
 * @param tensor N-D tensor.
 * @param mask K-D boolean tensor, K <= N and K must be known statically.
 * @param axis A 0-D int Tensor representing the axis in tensor to mask from.
 *     By default, axis is 0 which will mask from the first dimension.
 *     Otherwise K + axis <= N.
 *
 * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
 */ async function booleanMaskAsync_(tensor, mask, axis) {
    const $tensor = (0, _tensorUtilEnv.convertToTensor)(tensor, "tensor", "boolMask");
    const $mask = (0, _tensorUtilEnv.convertToTensor)(mask, "mask", "boolMask", "bool");
    const axisFrom = axis == null ? 0 : axis;
    const maskDim = $mask.rank;
    const tensorShape = $tensor.shape;
    _util.assert(maskDim > 0, ()=>"mask cannot be scalar");
    _util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
    let leadingSize = 1;
    for(let i = axisFrom; i < axisFrom + maskDim; i++)leadingSize *= tensorShape[i];
    const targetTensorShape = tensorShape.slice(0, axisFrom).concat([
        leadingSize
    ], tensorShape.slice(axisFrom + maskDim));
    const reshapedTensor = (0, _reshape.reshape)($tensor, targetTensorShape);
    const reshapedMask = (0, _reshape.reshape)($mask, [
        -1
    ]);
    const positivePositions = await (0, _whereAsync.whereAsync)(reshapedMask);
    const indices = (0, _squeeze.squeeze)(positivePositions, [
        1
    ]);
    const res = (0, _gather.gather)(reshapedTensor, indices, axisFrom);
    // Ensure no memory leak.
    if (tensor !== $tensor) $tensor.dispose();
    if (mask !== $mask) $mask.dispose();
    indices.dispose();
    reshapedTensor.dispose();
    reshapedMask.dispose();
    positivePositions.dispose();
    return res;
}
const booleanMaskAsync = booleanMaskAsync_;

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./gather":"5p5vK","./reshape":"ew3wZ","./squeeze":"VbY6Z","./where_async":"2KVsP","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2bqbS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transpose", ()=>transpose);
var _engine = require("../engine");
var _globals = require("../globals");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _complex = require("./complex");
var _imag = require("./imag");
var _neg = require("./neg");
var _operation = require("./operation");
var _real = require("./real");
/**
 * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.
 *
 * The returned `tf.Tensor`'s dimension `i` will correspond to the input
 * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,
 * where `n` is the rank of the input `tf.Tensor`. Hence by default, this
 * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.
 *
 * ```js
 * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
 *
 * a.transpose().print();  // or tf.transpose(a)
 * ```
 *
 * @param x The tensor to transpose.
 * @param perm The permutation of the dimensions of a.
 * @param conjugate Will conjugate complex input if true.
 *
 * @doc {heading: 'Operations', subheading: 'Matrices'}
 */ function transpose_(x, perm, conjugate) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "transpose");
    if (perm == null) perm = $x.shape.map((s, i)=>i).reverse();
    _util.assert($x.rank === perm.length, ()=>`Error in transpose: rank of input ${$x.rank} ` + `must match length of perm ${perm}.`);
    perm.forEach((axis)=>{
        _util.assert(axis >= 0 && axis < $x.rank, ()=>`All entries in 'perm' must be between 0 and ${$x.rank - 1}` + ` but got ${perm}`);
    });
    if ($x.rank <= 1) return $x.clone();
    const inputs = {
        x: $x
    };
    const attrs = {
        perm
    };
    if ($x.dtype === "complex64") return (0, _globals.tidy)(()=>{
        let $real = (0, _real.real)($x);
        let $imag = (0, _imag.imag)($x);
        $real = (0, _engine.ENGINE).runKernel((0, _kernelNames.Transpose), {
            x: $real
        }, attrs);
        $imag = (0, _engine.ENGINE).runKernel((0, _kernelNames.Transpose), {
            x: $imag
        }, attrs);
        if (conjugate) $imag = (0, _neg.neg)($imag);
        return (0, _complex.complex)($real, $imag);
    });
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Transpose), inputs, attrs);
}
const transpose = /* @__PURE__ */ (0, _operation.op)({
    transpose_
});

},{"../engine":"1g2qo","../globals":"eO1Ja","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util":"ctavo","./complex":"bPDAx","./imag":"lDORm","./neg":"3jQfI","./operation":"ip64W","./real":"8i3Ch","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hLPKe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "movingAverage", ()=>movingAverage);
var _tensorUtil = require("../tensor_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _add = require("./add");
var _div = require("./div");
var _mul = require("./mul");
var _operation = require("./operation");
var _pow = require("./pow");
var _scalar = require("./scalar");
var _sub = require("./sub");
/**
 * Compute the moving average of a variable.
 *
 * Without zeroDebias, the moving average operation is defined by:
 *   `v += delta`
 * where
 *   `delta = (1 - decay) * (x - v)`
 *
 * With zeroDebias (default), the `delta` term is scaled to debias the
 * effect of the (assumed) zero-initialization of `v`.
 *   `delta /= (1 - decay ^ step)`
 *
 * For more details on the zero-debiasing algorithm, see:
 *   https://arxiv.org/abs/1412.6980
 *
 * Note that this function is completely stateless and does not keep track of
 * step count. The step count needs to be maintained by the caller and passed
 * in as `step`.
 *
 * @param v The current moving average value.
 * @param x New input value, must have the same shape and dtype as `v`.
 * @param decay The decay factor. Typical values are 0.95 and 0.99.
 * @param step Step count.
 * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).
 * @returns The new moving average value.
 *
 * @doc {heading: 'Operations', subheading: 'Moving Average'}
 */ function movingAverage_(v, x, decay, step, zeroDebias = true) {
    const $v = (0, _tensorUtilEnv.convertToTensor)(v, "v", "movingAverage");
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "movingAverage");
    const $decay = (0, _tensorUtilEnv.convertToTensor)(decay, "decay", "movingAverage");
    (0, _tensorUtil.assertTypesMatch)($v, $x);
    _util.assert(_util.arraysEqual($v.shape, $x.shape), ()=>"Shape mismatch in v and x");
    const one = (0, _scalar.scalar)(1);
    const oneMinusDecay = (0, _sub.sub)(one, $decay);
    let update = (0, _mul.mul)((0, _sub.sub)($x, $v), oneMinusDecay);
    if (zeroDebias) {
        _util.assert(step != null, ()=>"When using zeroDebias: true, step is required.");
        const $step = (0, _tensorUtilEnv.convertToTensor)(step, "step", "movingAverage");
        update = (0, _div.div)(update, (0, _sub.sub)(one, (0, _pow.pow)($decay, $step)));
    }
    return (0, _add.add)($v, update);
}
const movingAverage = /* @__PURE__ */ (0, _operation.op)({
    movingAverage_
});

},{"../tensor_util":"8jzJG","../tensor_util_env":"6vfHI","../util":"ctavo","./add":"aynh7","./div":"5ZT1h","./mul":"fOWrC","./operation":"ip64W","./pow":"gezpC","./scalar":"7lhC8","./sub":"jBBQ2","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2xMWX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "scatterND", ()=>scatterND);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _utilBase = require("../util_base");
var _operation = require("./operation");
var _scatterNdUtil = require("./scatter_nd_util");
/**
 * Creates a new tensor by applying sparse updates to individual
 * values or slices within a zero tensor of the given shape tensor according to
 * indices. This operator is the inverse of the `tf.gatherND` operator which
 * extracts values or slices from a given tensor.
 *
 * ```js
 * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32');
 * const updates = tf.tensor1d([9, 10, 11, 12]);
 * const shape = [8];
 * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12]
 * ```
 *
 * @param indices The tensor contains the indices into the output tensor.
 * @param updates The tensor contains the value for the indices.
 * @param shape: The shape of the output tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
 */ function scatterND_(indices, updates, shape) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(shape);
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "scatterND", "int32");
    const $updates = (0, _tensorUtilEnv.convertToTensor)(updates, "updates", "scatterND");
    _scatterNdUtil.validateInput($updates, $indices, shape);
    const inputs = {
        indices: $indices,
        updates: $updates
    };
    const attrs = {
        shape
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.ScatterNd), inputs, attrs);
}
const scatterND = /* @__PURE__ */ (0, _operation.op)({
    scatterND_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","../util_base":"4Vo4a","./operation":"ip64W","./scatter_nd_util":"dDUQ5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gXN1p":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseToDense", ()=>sparseToDense);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _sparseToDenseUtil = require("../ops/sparse_to_dense_util");
var _tensorUtilEnv = require("../tensor_util_env");
var _utilBase = require("../util_base");
var _operation = require("./operation");
/**
 * Converts a sparse representation into a dense tensor.
 *
 * Builds an array dense with shape outputShape such that:
 *
 * // If sparseIndices is scalar
 * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)
 *
 * // If sparseIndices is a vector, then for each i
 * dense[sparseIndices[i]] = sparseValues[i]
 *
 * // If sparseIndices is an n by d matrix, then for each i in [0, n)
 * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]
 * All other values in dense are set to defaultValue. If sparseValues is a
 * scalar, all sparse indices are set to this single value.
 *
 * If indices are repeated the final value is summed over all values for those
 * indices.
 *
 * ```js
 * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');
 * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');
 * const shape = [8];
 * tf.sparseToDense(indices, values, shape).print();
 * ```
 *
 * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
 * sparseIndices[i] contains the complete index where sparseValues[i] will be
 * placed.
 * @param sparseValues A 0-D or 1-D Tensor. Values
 * corresponding to each row of sparseIndices, or a scalar value to be used for
 * all sparse indices.
 * @param outputShape Shape of the dense output tensor. The type is inferred.
 * @param defaultValue Scalar. Value to set for indices not specified in
 * sparseIndices. Defaults to zero.
 *
 * @doc {heading: 'Operations', subheading: 'Normalization'}
 */ function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue = 0) {
    (0, _utilBase.assertNonNegativeIntegerDimensions)(outputShape);
    const $sparseIndices = (0, _tensorUtilEnv.convertToTensor)(sparseIndices, "sparseIndices", "sparseToDense", "int32");
    const $sparseValues = (0, _tensorUtilEnv.convertToTensor)(sparseValues, "sparseValues", "sparseToDense", "string_or_numeric");
    const $defaultValue = (0, _tensorUtilEnv.convertToTensor)(defaultValue, "defaultValue", "sparseToDense", $sparseValues.dtype);
    _sparseToDenseUtil.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);
    const inputs = {
        sparseIndices: $sparseIndices,
        sparseValues: $sparseValues,
        defaultValue: $defaultValue
    };
    const attrs = {
        outputShape
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseToDense), inputs, attrs);
}
const sparseToDense = /* @__PURE__ */ (0, _operation.op)({
    sparseToDense_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../ops/sparse_to_dense_util":"7Tweu","../tensor_util_env":"6vfHI","../util_base":"4Vo4a","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7Tweu":[function(require,module,exports) {
/**
 * Validate sparseToDense inputs.
 *
 * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
 * sparseIndices[i] contains the complete index where sparseValues[i] will be
 * placed.
 * @param sparseValues A 0-D or 1-D Tensor. Values
 * corresponding to each row of sparseIndices, or a scalar value to be used for
 * all sparse indices.
 * @param outputShape number[]. Shape of the dense output tensor.
 * @param validateIndices boolean. indice validation is not supported, error
 * will be thrown if it is set.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "validateInput", ()=>validateInput);
function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {
    if (sparseIndices.dtype !== "int32") throw new Error("tf.sparseToDense() expects the indices to be int32 type," + ` but the dtype was ${sparseIndices.dtype}.`);
    if (sparseIndices.rank > 2) throw new Error("sparseIndices should be a scalar, vector, or matrix," + ` but got shape ${sparseIndices.shape}.`);
    const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
    const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
    if (outputShape.length !== numDims) throw new Error("outputShape has incorrect number of elements:," + ` ${outputShape.length}, should be: ${numDims}.`);
    const numValues = sparseValues.size;
    if (!(sparseValues.rank === 0 || sparseValues.rank === 1 && numValues === numElems)) throw new Error("sparseValues has incorrect shape " + `${sparseValues.shape}, should be [] or [${numElems}]`);
    if (sparseValues.dtype !== defaultValues.dtype) throw new Error("sparseValues.dtype must match defaultValues.dtype");
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9KIwq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gatherND", ()=>gatherND);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _tensorUtilEnv = require("../tensor_util_env");
var _operation = require("./operation");
/**
 * Gather slices from input tensor into a Tensor with shape specified by
 * `indices`.
 *
 * `indices` is a K-dimensional integer tensor, best thought of as a
 * (K-1)-dimensional tensor of indices into input, where each element defines a
 * slice of input:
 * output[\\(i_0, ..., i_{K-2}\\)] = input[indices[\\(i_0, ..., i_{K-2}\\)]]
 *
 * Whereas in `tf.gather`, `indices` defines slices into the first dimension of
 * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions
 * of input, where N = indices.shape[-1].
 *
 * The last dimension of indices can be at most the rank of input:
 * indices.shape[-1] <= input.rank
 *
 * The last dimension of `indices` corresponds to elements
 * (if indices.shape[-1] == input.rank) or slices
 * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of
 * input.
 * The output tensor has shape
 * indices.shape[:-1] + input.shape[indices.shape[-1]:]
 *
 * Note that on CPU, if an out of bound index is found, an error is returned. On
 * GPU, if an out of bound index is found, a 0 is stored in the corresponding
 * output value.
 *
 * ```js
 * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');
 * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);
 * tf.gatherND(input, indices).print() // [10, 11]
 * ```
 *
 * @param x The tensor from which to gather values.
 * @param indices Index tensor, must be of type int32.
 *
 * @doc {heading: 'Operations', subheading: 'Slicing and Joining'}
 */ function gatherND_(x, indices) {
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "gatherND", "int32");
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "gatherND", "string_or_numeric");
    const inputs = {
        params: $x,
        indices: $indices
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.GatherNd), inputs);
}
const gatherND = /* @__PURE__ */ (0, _operation.op)({
    gatherND_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../tensor_util_env":"6vfHI","./operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6nVzD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "dropout", ()=>dropout);
var _tensor = require("../tensor");
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _add = require("./add");
var _div = require("./div");
var _dropoutUtil = require("./dropout_util");
var _floor = require("./floor");
var _mul = require("./mul");
var _operation = require("./operation");
var _randomUniform = require("./random_uniform");
/**
 * Computes dropout.
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 2, 1]);
 * const rate = 0.75;
 * const output = tf.dropout(x, rate);
 * output.print();
 * ```
 *
 * @param x A floating point Tensor or TensorLike.
 * @param rate A float in the range [0, 1). The probability that each element
 *   of x is discarded.
 * @param noiseShape An array of numbers of type int32, representing the
 * shape for randomly generated keep/drop flags. If the noiseShape has null
 * value, it will be automatically replaced with the x's relative dimension
 * size. Optional.
 * @param seed Used to create random seeds. Optional.
 * @returns A Tensor of the same shape of x.
 *
 * @doc {heading: 'Operations', subheading: 'Dropout'}
 */ function dropout_(x, rate, noiseShape, seed) {
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "dropout");
    _util.assert($x.dtype === "float32", ()=>`x has to be a floating point tensor since it's going to be ` + `scaled, but got a ${$x.dtype} tensor instead.`);
    _util.assert(rate >= 0 && rate < 1, ()=>`rate must be a float in the range [0, 1), but got ${rate}.`);
    if (rate === 0) return x instanceof (0, _tensor.Tensor) ? $x.clone() : $x;
    const $noiseShape = (0, _dropoutUtil.getNoiseShape)($x, noiseShape);
    const keepProb = 1 - rate;
    const multiplier = (0, _div.div)((0, _floor.floor)((0, _add.add)((0, _randomUniform.randomUniform)($noiseShape, 0, 1, "float32", seed), keepProb)), keepProb);
    return (0, _mul.mul)($x, multiplier);
}
const dropout = /* @__PURE__ */ (0, _operation.op)({
    dropout_
});

},{"../tensor":"cJNoh","../tensor_util_env":"6vfHI","../util":"ctavo","./add":"aynh7","./div":"5ZT1h","./dropout_util":"a55Hr","./floor":"35qCf","./mul":"fOWrC","./operation":"ip64W","./random_uniform":"8LDYI","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a55Hr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Normalize noise shape based on provided tensor and noise shape.
 *
 * @param x Tensor.
 * @param noiseShape The shape for the randomly generated keep/drop flags, as
 *   an array of numbers. Optional.
 * @returns Normalized noise shape.
 */ parcelHelpers.export(exports, "getNoiseShape", ()=>getNoiseShape);
var _util = require("../util");
function getNoiseShape(x, noiseShape) {
    if (noiseShape == null) return x.shape.slice();
    if (_util.arraysEqual(x.shape, noiseShape)) return noiseShape;
    if (x.shape.length === noiseShape.length) {
        const newDimension = [];
        for(let i = 0; i < x.shape.length; i++)if (noiseShape[i] == null && x.shape[i] != null) newDimension.push(x.shape[i]);
        else newDimension.push(noiseShape[i]);
        return newDimension;
    }
    return noiseShape;
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1EuF4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "enclosingPowerOfTwo", ()=>enclosingPowerOfTwo);
parcelHelpers.export(exports, "cosineWindow", ()=>cosineWindow);
var _tensor1D = require("./tensor1d");
function enclosingPowerOfTwo(value) {
    // Return 2**N for integer N such that 2**N >= value.
    return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0))));
}
function cosineWindow(windowLength, a, b) {
    const even = 1 - windowLength % 2;
    const newValues = new Float32Array(windowLength);
    for(let i = 0; i < windowLength; ++i){
        const cosArg = 2.0 * Math.PI * i / (windowLength + even - 1);
        newValues[i] = a - b * Math.cos(cosArg);
    }
    return (0, _tensor1D.tensor1d)(newValues, "float32");
}

},{"./tensor1d":"aEL9e","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9rwSr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "inTopKAsync", ()=>inTopKAsync);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _tensor = require("./tensor");
/**
 * Returns whether the targets are in the top K predictions.
 *
 * ```js
 * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
 * const targets = tf.tensor1d([2, 0]);
 * const precision = await tf.inTopKAsync(predictions, targets);
 * precision.print();
 * ```
 * @param predictions 2-D or higher `tf.Tensor` with last dimension being
 *     at least `k`.
 * @param targets 1-D or higher `tf.Tensor`.
 * @param k Optional Number of top elements to look at for computing precision,
 *     default to 1.
 *
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */ async function inTopKAsync_(predictions, targets, k = 1) {
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "inTopK");
    const $targets = (0, _tensorUtilEnv.convertToTensor)(targets, "targets", "inTopK");
    (0, _util.assert)($predictions.rank > 1, ()=>"inTopK() expects the predictions to be of rank 2 or higher, " + `but got ${$predictions.rank}`);
    (0, _util.assert)($predictions.rank - 1 === $targets.rank, ()=>`predictions rank should be 1 larger than ` + `targets rank, but got predictions rank ` + `${$predictions.rank} and targets rank ${$targets.rank}`);
    (0, _util.assertShapesMatch)($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` + "except the last dimension.");
    const lastDim = $predictions.shape[$predictions.shape.length - 1];
    (0, _util.assert)(k > 0 && k <= lastDim, ()=>`'k' passed to inTopK() must be > 0 && <= the predictions last ` + `dimension (${lastDim}), but got ${k}`);
    const predictionsVals = await $predictions.data();
    const targetsVals = await $targets.data();
    // Reshape predictionsVals into a 2d tensor [batch, lastDim]
    // and look up topK along lastDim.
    const [batch, size] = [
        predictionsVals.length / lastDim,
        lastDim
    ];
    const precision = (0, _util.getTypedArrayFromDType)("bool", batch);
    for(let b = 0; b < batch; b++){
        const offset = b * size;
        const vals = predictionsVals.subarray(offset, offset + size);
        const valAndInd = [];
        for(let i = 0; i < vals.length; i++)valAndInd.push({
            value: vals[i],
            index: i
        });
        valAndInd.sort((a, b)=>b.value - a.value);
        precision[b] = 0;
        for(let i = 0; i < k; i++)if (valAndInd[i].index === targetsVals[b]) {
            precision[b] = 1;
            break;
        }
    }
    if (predictions !== $predictions) $predictions.dispose();
    if (targets !== $targets) $targets.dispose();
    // Output precision has the same shape as targets.
    return (0, _tensor.tensor)(precision, $targets.shape, "bool");
}
const inTopKAsync = inTopKAsync_;

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./tensor":"jdbb1","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jqlvt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2d", ()=>(0, _conv2D.conv2d));
parcelHelpers.export(exports, "depthwiseConv2d", ()=>(0, _depthwiseConv2D.depthwiseConv2d));
parcelHelpers.export(exports, "matMul", ()=>(0, _matMul.matMul));
var _conv2D = require("./fused/conv2d");
var _depthwiseConv2D = require("./fused/depthwise_conv2d");
var _matMul = require("./fused/mat_mul");

},{"./fused/conv2d":"i5FXc","./fused/depthwise_conv2d":"cEX74","./fused/mat_mul":"6BB2f","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"i5FXc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2d", ()=>conv2d);
var _engine = require("../../engine");
var _gradients = require("../../gradients");
var _kernelNames = require("../../kernel_names");
var _tensorUtil = require("../../tensor_util");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _add = require("../add");
var _broadcastUtil = require("../broadcast_util");
var _conv2D = require("../conv2d");
var _conv2DBackpropFilter = require("../conv2d_backprop_filter");
var _conv2DBackpropInput = require("../conv2d_backprop_input");
var _convUtil = require("../conv_util");
var _fusedUtil = require("../fused_util");
var _operation = require("../operation");
var _reshape = require("../reshape");
/**
 * Computes a 2D convolution over the input x, optionally fused with adding a
 * bias and applying an activation.
 *
 * ```js
 * const inputDepth = 2;
 * const inShape = [2, 2, 2, inputDepth];
 * const outputDepth = 2;
 * const fSize = 1;
 * const pad = 0;
 * const strides = 1;
 *
 * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
 * 16], inShape);
 * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,
 * outputDepth]);
 *
 * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',
 * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();
 * ```
 *
 * @param obj An object with the following properties:
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter, rank 4, of shape
 *     `[filterHeight, filterWidth, inDepth, outDepth]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`.
 * @param pad The type of padding algorithm.
 *   - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *   - `valid` output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels]. Only "NHWC" is currently supported.
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 * @param bias Tensor to be added to the result.
 * @param activation Name of activation kernel (defaults to `linear`) to be
 *     applied
 *      after biasAdd.
 * @param preluActivationWeights Tensor of prelu weights to be applied as part
 *     of a `prelu` activation, typically the same shape as `x`.
 * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
 *     activation.
 */ function fusedConv2d_({ x, filter, strides, pad, dataFormat = "NHWC", dilations = [
    1,
    1
], dimRoundingMode, bias, activation = "linear", preluActivationWeights, leakyreluAlpha }) {
    activation = activation || "linear";
    if ((0, _fusedUtil.shouldFuse)((0, _engine.ENGINE).state.gradientDepth, activation) === false) {
        // TODO: Transpose bias and preluActivationWeights properly for NCHW
        // format before computation.
        _util.assert(dataFormat === "NHWC", ()=>`Error in fused conv2d: got dataFormat of ${dataFormat} but ` + `only NHWC is currently supported for the case of gradient depth ` + `is 0 and the activation is not linear.`);
        let result = (0, _conv2D.conv2d)(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
        if (bias != null) result = (0, _add.add)(result, bias);
        return (0, _fusedUtil.applyActivation)(result, activation, preluActivationWeights, leakyreluAlpha);
    }
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "conv2d", "float32");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "conv2d", "float32");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    _util.assert(x4D.rank === 4, ()=>`Error in fused conv2d: input must be rank 4, but got rank ` + `${x4D.rank}.`);
    _util.assert($filter.rank === 4, ()=>`Error in fused conv2d: filter must be rank 4, but got rank ` + `${$filter.rank}.`);
    _convUtil.checkPadOnDimRoundingMode("fused conv2d", pad, dimRoundingMode);
    const inputChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
    _util.assert($filter.shape[2] === inputChannels, ()=>`Error in conv2d: depth of input (${inputChannels}) must match ` + `input depth for filter ${$filter.shape[2]}.`);
    _util.assert(_convUtil.eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in conv2D: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = _convUtil.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
    let $bias;
    if (bias != null) {
        $bias = (0, _tensorUtilEnv.convertToTensor)(bias, "bias", "fused conv2d");
        [$bias] = (0, _tensorUtil.makeTypesMatch)($bias, $x);
        // According to TensorFlow, the bias is supposed be a 1-D tensor or a
        // scalar.
        //
        // 3-D or 4-D bias is not disabled for NHWC format, because they are
        // currently being used in some cases. For examplem in our code base,
        // https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/fused_conv2d_test.ts#L1972.
        if (dataFormat === "NHWC") _broadcastUtil.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
        else {
            _util.assert($bias.shape.length <= 1, ()=>`Error in fused conv2d: only supports scalar or 1-D Tensor ` + `bias for NCHW format but got the bias of ` + `rank-${$bias.shape.length}.`);
            _util.assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, ()=>`Error in fused conv2d: bias shape (${$bias.shape}) is not ` + `compatible with the number of output channels ` + `(${convInfo.outChannels})`);
        }
    }
    let $preluActivationWeights;
    if (preluActivationWeights != null) {
        // PReLU's activation weights could be a scalar, a 1-D tensor or a 3-D
        // tensor.
        const alphaShape = preluActivationWeights.shape;
        _util.assert(alphaShape.length <= 1 || alphaShape.length === 3, ()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or ` + `3-D Tensor PReLU activation weights but got a tensor of ` + `rank-${alphaShape.length}.`);
        if (alphaShape.length === 1) // Whether the data format is NCHW or NHWC, the 1-D PReLU activation
        // weights tensor should be aligned with the output channels of conv2d
        // result.
        _util.assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, ()=>`Error in fused conv2d: PReLU activation weights ` + `(${alphaShape}) is not compatible with the number of output ` + `channels (${convInfo.outChannels}).`);
        else if (alphaShape.length === 3) // Whether the data format is NCHW or NHWC, the PReLU activation weights
        // tensor should has the compatible shape with the result of conv2d.
        try {
            _broadcastUtil.assertAndGetBroadcastShape(alphaShape, convInfo.outShape);
        } catch (e) {
            const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) ` + `is not compatible with the output shape of the conv2d ` + `(${convInfo.outShape}).`;
            throw Error(errMsg);
        }
        $preluActivationWeights = (0, _tensorUtilEnv.convertToTensor)(preluActivationWeights, "prelu weights", "fused conv2d");
    }
    const grad = (dy, saved)=>{
        _util.assert(dataFormat === "NHWC", ()=>`Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
        const [$filter, x4D, y, $bias] = saved;
        const dyActivation = (0, _fusedUtil.getFusedDyActivation)(dy, y, activation);
        _util.assert(_convUtil.tupleValuesAreOne(dilations), ()=>"Error in gradient of fused conv2D: " + `dilation rates greater than 1 ` + `are not yet supported in gradients. Got dilations '${dilations}'`);
        const xDer = (0, _conv2DBackpropInput.conv2DBackpropInput)(x4D.shape, dyActivation, $filter, strides, pad);
        const filterDer = (0, _conv2DBackpropFilter.conv2DBackpropFilter)(x4D, dyActivation, $filter.shape, strides, pad);
        const der = [
            xDer,
            filterDer
        ];
        if ($bias != null) {
            const biasDer = (0, _fusedUtil.getFusedBiasGradient)($bias, dyActivation);
            der.push(biasDer);
        }
        return der;
    };
    const inputs = {
        x: x4D,
        filter: $filter,
        bias: $bias,
        preluActivationWeights: $preluActivationWeights
    };
    const attrs = {
        strides,
        pad,
        dataFormat,
        dilations,
        dimRoundingMode,
        activation,
        leakyreluAlpha
    };
    // Depending on the the params passed in we will have different number of
    // inputs and thus a a different number of elements in the gradient.
    if (bias == null) {
        const customOp = (0, _gradients.customGrad)((x4D, filter, save)=>{
            let res = // tslint:disable-next-line: no-unnecessary-type-assertion
            (0, _engine.ENGINE).runKernel((0, _kernelNames.FusedConv2D), inputs, attrs);
            save([
                filter,
                x4D,
                res
            ]);
            if (reshapedTo4D) // tslint:disable-next-line: no-unnecessary-type-assertion
            res = (0, _reshape.reshape)(res, [
                res.shape[1],
                res.shape[2],
                res.shape[3]
            ]);
            return {
                value: res,
                gradFunc: grad
            };
        });
        return customOp(x4D, $filter);
    } else {
        const customOpWithBias = (0, _gradients.customGrad)((x4D, filter, bias, save)=>{
            let res = (0, _engine.ENGINE).runKernel((0, _kernelNames.FusedConv2D), inputs, attrs);
            save([
                filter,
                x4D,
                res,
                bias
            ]);
            if (reshapedTo4D) // tslint:disable-next-line: no-unnecessary-type-assertion
            res = (0, _reshape.reshape)(res, [
                res.shape[1],
                res.shape[2],
                res.shape[3]
            ]);
            return {
                value: res,
                gradFunc: grad
            };
        });
        return customOpWithBias(x4D, $filter, $bias);
    }
}
const conv2d = /* @__PURE__ */ (0, _operation.op)({
    fusedConv2d_
});

},{"../../engine":"1g2qo","../../gradients":"dDHpg","../../kernel_names":"hniYO","../../tensor_util":"8jzJG","../../tensor_util_env":"6vfHI","../../util":"ctavo","../add":"aynh7","../broadcast_util":"eLo4J","../conv2d":"lmJyO","../conv2d_backprop_filter":"1UKq8","../conv2d_backprop_input":"l0Ec6","../conv_util":"bf4MH","../fused_util":"8847K","../operation":"ip64W","../reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1UKq8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2DBackpropFilter", ()=>conv2DBackpropFilter);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _util = require("../util");
var _convUtil = require("./conv_util");
var _operation = require("./operation");
var _reshape = require("./reshape");
/**
 * Computes the derivative of the filter of a 2D convolution.
 *
 * @param x The input tensor, of rank 4 or rank 3 of shape
 *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
 * @param dy The dy image, of rank 4 or rank 3, of shape
 *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.
 * @param filterShape The shape of the filter, length 4,
 *     [filterHeight, filterWidth, inDepth, outDepth].
 * @param strides The strides of the convolution: [strideHeight,
 * strideWidth].
 * @param pad A string from: 'same', 'valid'. The type of padding algorithm
 *     used in the forward prop of the op.
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels].
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 */ function conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat = "NHWC", dimRoundingMode) {
    let x4D = x;
    if (x.rank === 3) x4D = (0, _reshape.reshape)(x, [
        1,
        x.shape[0],
        x.shape[1],
        x.shape[2]
    ]);
    let dy4D = dy;
    if (dy4D.rank === 3) dy4D = (0, _reshape.reshape)(dy, [
        1,
        dy.shape[0],
        dy.shape[1],
        dy.shape[2]
    ]);
    _util.assert(x4D.rank === 4, ()=>`Error in conv2dDerFilter: input must be rank 4, but got shape ` + `${x4D.shape}.`);
    _util.assert(dy4D.rank === 4, ()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ` + `${dy4D.shape}.`);
    _util.assert(filterShape.length === 4, ()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ` + `${filterShape}.`);
    const inDepth = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
    const outDepth = dataFormat === "NHWC" ? dy4D.shape[3] : dy4D.shape[1];
    _util.assert(inDepth === filterShape[2], ()=>`Error in conv2dDerFilter: depth of input ${inDepth}) must ` + `match input depth in filter (${filterShape[2]}.`);
    _util.assert(outDepth === filterShape[3], ()=>`Error in conv2dDerFilter: depth of dy (${outDepth}) must ` + `match output depth for filter (${filterShape[3]}).`);
    _convUtil.checkPadOnDimRoundingMode("conv2dDerFilter", pad, dimRoundingMode);
    const inputs = {
        x: x4D,
        dy: dy4D
    };
    const attrs = {
        strides,
        pad,
        dataFormat,
        dimRoundingMode,
        filterShape
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Conv2DBackpropFilter), inputs, attrs);
}
const conv2DBackpropFilter = /* @__PURE__ */ (0, _operation.op)({
    conv2DBackpropFilter_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","../util":"ctavo","./conv_util":"bf4MH","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8847K":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
// Returns gradient for fused activation.
parcelHelpers.export(exports, "getFusedDyActivation", ()=>getFusedDyActivation);
// Returns gradient for fused bias.
parcelHelpers.export(exports, "getFusedBiasGradient", ()=>getFusedBiasGradient);
parcelHelpers.export(exports, "applyActivation", ()=>applyActivation);
parcelHelpers.export(exports, "shouldFuse", ()=>shouldFuse);
var _broadcastUtil = require("./broadcast_util");
var _elu = require("./elu");
var _leakyRelu = require("./leaky_relu");
var _mul = require("./mul");
var _prelu = require("./prelu");
var _relu = require("./relu");
var _relu6 = require("./relu6");
var _reshape = require("./reshape");
var _sigmoid = require("./sigmoid");
var _step = require("./step");
var _sum = require("./sum");
function getFusedDyActivation(dy, y, activation) {
    if (activation == null || activation === "linear") return dy;
    if (activation === "relu") return (0, _mul.mul)(dy, (0, _step.step)(y));
    throw new Error(`Cannot compute gradient for fused activation ${activation}.`);
}
function getFusedBiasGradient(bias, dyActivation) {
    let res = dyActivation;
    const reduceAxes = _broadcastUtil.getReductionAxes(bias.shape, dyActivation.shape);
    if (reduceAxes.length > 0) res = (0, _sum.sum)(res, reduceAxes);
    return (0, _reshape.reshape)(res, bias.shape);
}
function applyActivation(x, activation, preluActivationWeights, leakyreluAlpha) {
    if (activation === "linear") return x;
    else if (activation === "relu") return (0, _relu.relu)(x);
    else if (activation === "elu") return (0, _elu.elu)(x);
    else if (activation === "relu6") return (0, _relu6.relu6)(x);
    else if (activation === "prelu") return (0, _prelu.prelu)(x, preluActivationWeights);
    else if (activation === "leakyrelu") return (0, _leakyRelu.leakyRelu)(x, leakyreluAlpha);
    else if (activation === "sigmoid") return (0, _sigmoid.sigmoid)(x);
    throw new Error(`Unknown fused activation ${activation}.`);
}
const shouldFuse = (gradientDepth, activation)=>{
    const gradientMode = gradientDepth > 0;
    return !gradientMode || activation === "linear";
};

},{"./broadcast_util":"eLo4J","./elu":"dE197","./leaky_relu":"aRBcm","./mul":"fOWrC","./prelu":"azJEF","./relu":"lcv3E","./relu6":"cvYLK","./reshape":"ew3wZ","./sigmoid":"81E16","./step":"bBBT4","./sum":"5awd0","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cEX74":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2d", ()=>depthwiseConv2d);
var _engine = require("../../engine");
var _gradients = require("../../gradients");
var _kernelNames = require("../../kernel_names");
var _tensorUtil = require("../../tensor_util");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _add = require("../add");
var _broadcastUtil = require("../broadcast_util");
var _convUtil = require("../conv_util");
var _depthwiseConv2D = require("../depthwise_conv2d");
var _depthwiseConv2DNativeBackpropFilter = require("../depthwise_conv2d_native_backprop_filter");
var _depthwiseConv2DNativeBackpropInput = require("../depthwise_conv2d_native_backprop_input");
var _fusedUtil = require("../fused_util");
var _operation = require("../operation");
var _reshape = require("../reshape");
/**
 * Computes depthwise 2D convolution, optionally fused with adding a
 * bias and applying an activation.
 *
 * Given a 4D `input` array and a `filter` array of shape
 * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
 * `inChannels` convolutional filters of depth 1, this op applies a
 * different filter to each input channel (expanding from 1 channel to
 * `channelMultiplier` channels for each), then concatenates the results
 * together. The output has `inChannels * channelMultiplier` channels.
 *
 * See
 * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
 *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
 * for more details.
 *
 * @param obj An object with the following properties:
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter tensor, rank 4, of shape
 *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`. If strides is a single number, then `strideHeight ==
 * strideWidth`.
 * @param pad The type of padding algorithm.
 *   - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *   - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels]. Only "NHWC" is currently supported.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 * @param bias Tensor to be added to the result.
 * @param activation Name of activation kernel (defaults to `linear`).
 * @param preluActivationWeights Tensor of prelu weights to be applied as part
 *     of a `prelu` activation, typically the same shape as `x`.
 * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
 *     activation.
 */ function fusedDepthwiseConv2d_({ x, filter, strides, pad, dataFormat = "NHWC", dilations = [
    1,
    1
], dimRoundingMode, bias, activation = "linear", preluActivationWeights, leakyreluAlpha }) {
    if ((0, _fusedUtil.shouldFuse)((0, _engine.ENGINE).state.gradientDepth, activation) === false) {
        let result = (0, _depthwiseConv2D.depthwiseConv2d)(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
        if (bias != null) result = (0, _add.add)(result, bias);
        return (0, _fusedUtil.applyActivation)(result, activation, preluActivationWeights, leakyreluAlpha);
    }
    const $x = (0, _tensorUtilEnv.convertToTensor)(x, "x", "depthwiseConv2d", "float32");
    const $filter = (0, _tensorUtilEnv.convertToTensor)(filter, "filter", "depthwiseConv2d", "float32");
    let x4D = $x;
    let reshapedTo4D = false;
    if ($x.rank === 3) {
        reshapedTo4D = true;
        x4D = (0, _reshape.reshape)($x, [
            1,
            $x.shape[0],
            $x.shape[1],
            $x.shape[2]
        ]);
    }
    _util.assert(x4D.rank === 4, ()=>`Error in fused depthwiseConv2d: input must be rank 4, but got ` + `rank ${x4D.rank}.`);
    _util.assert($filter.rank === 4, ()=>`Error in fused depthwiseConv2d: filter must be rank 4, ` + `but got rank ${$filter.rank}.`);
    _util.assert(x4D.shape[3] === $filter.shape[2], ()=>`Error in fused depthwiseConv2d: number of input channels ` + `(${x4D.shape[3]}) must match the inChannels dimension in ` + `filter ${$filter.shape[2]}.`);
    if (dilations == null) dilations = [
        1,
        1
    ];
    _util.assert(_convUtil.eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in fused depthwiseConv2d: Either strides or dilations must " + `be 1. Got strides ${strides} and dilations '${dilations}'`);
    _convUtil.checkPadOnDimRoundingMode("fused depthwiseConv2d", pad, dimRoundingMode);
    const convInfo = _convUtil.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true);
    let $bias;
    if (bias != null) {
        $bias = (0, _tensorUtilEnv.convertToTensor)(bias, "bias", "fused conv2d");
        [$bias] = (0, _tensorUtil.makeTypesMatch)($bias, $x);
        _broadcastUtil.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
    }
    let $preluActivationWeights;
    if (preluActivationWeights != null) $preluActivationWeights = (0, _tensorUtilEnv.convertToTensor)(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
    const grad = (dy, saved)=>{
        _util.assert(_convUtil.tupleValuesAreOne(dilations), ()=>"Error in gradient of fused depthwiseConv2d: dilation rates " + `greater than 1 are not yet supported. Got dilations ` + `'${dilations}'`);
        const [$filter, x4D, y, bias] = saved;
        const dyActivation = (0, _fusedUtil.getFusedDyActivation)(dy, y, activation);
        const xDer = (0, _depthwiseConv2DNativeBackpropInput.depthwiseConv2dNativeBackpropInput)(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode);
        const filterDer = (0, _depthwiseConv2DNativeBackpropFilter.depthwiseConv2dNativeBackpropFilter)(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode);
        if (bias != null) {
            const biasDer = (0, _fusedUtil.getFusedBiasGradient)($bias, dyActivation);
            return [
                xDer,
                filterDer,
                biasDer
            ];
        }
        return [
            xDer,
            filterDer
        ];
    };
    const inputs = {
        x: x4D,
        filter: $filter,
        bias: $bias,
        preluActivationWeights: $preluActivationWeights
    };
    const attrs = {
        strides,
        pad,
        dataFormat,
        dilations,
        dimRoundingMode,
        activation,
        leakyreluAlpha
    };
    // Depending on the the params passed in we will have different number of
    // inputs and thus a a different number of elements in the gradient.
    if (bias == null) {
        const customOp = (0, _gradients.customGrad)((x4D, filter, save)=>{
            // tslint:disable-next-line: no-unnecessary-type-assertion
            let res = (0, _engine.ENGINE).runKernel((0, _kernelNames.FusedDepthwiseConv2D), inputs, attrs);
            save([
                filter,
                x4D,
                res
            ]);
            if (reshapedTo4D) // tslint:disable-next-line: no-unnecessary-type-assertion
            res = (0, _reshape.reshape)(res, [
                res.shape[1],
                res.shape[2],
                res.shape[3]
            ]);
            return {
                value: res,
                gradFunc: grad
            };
        });
        return customOp(x4D, $filter);
    } else {
        const customOpWithBias = (0, _gradients.customGrad)((x4D, filter, bias, save)=>{
            // tslint:disable-next-line: no-unnecessary-type-assertion
            let res = (0, _engine.ENGINE).runKernel((0, _kernelNames.FusedDepthwiseConv2D), inputs, attrs);
            save([
                filter,
                x4D,
                res,
                bias
            ]);
            if (reshapedTo4D) // tslint:disable-next-line: no-unnecessary-type-assertion
            res = (0, _reshape.reshape)(res, [
                res.shape[1],
                res.shape[2],
                res.shape[3]
            ]);
            return {
                value: res,
                gradFunc: grad
            };
        });
        return customOpWithBias(x4D, $filter, $bias);
    }
}
const depthwiseConv2d = /* @__PURE__ */ (0, _operation.op)({
    fusedDepthwiseConv2d_
});

},{"../../engine":"1g2qo","../../gradients":"dDHpg","../../kernel_names":"hniYO","../../tensor_util":"8jzJG","../../tensor_util_env":"6vfHI","../../util":"ctavo","../add":"aynh7","../broadcast_util":"eLo4J","../conv_util":"bf4MH","../depthwise_conv2d":"cZHpb","../depthwise_conv2d_native_backprop_filter":"c0q2C","../depthwise_conv2d_native_backprop_input":"b4Cpx","../fused_util":"8847K","../operation":"ip64W","../reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c0q2C":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropFilter", ()=>depthwiseConv2dNativeBackpropFilter);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _operation = require("./operation");
var _reshape = require("./reshape");
function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations = [
    1,
    1
], dimRoundingMode) {
    let x4D = x;
    if (x.rank === 3) x4D = (0, _reshape.reshape)(x, [
        1,
        x.shape[0],
        x.shape[1],
        x.shape[2]
    ]);
    let dy4D = dy;
    if (dy4D.rank === 3) dy4D = (0, _reshape.reshape)(dy, [
        1,
        dy.shape[0],
        dy.shape[1],
        dy.shape[2]
    ]);
    const inputs = {
        x: x4D,
        dy: dy4D
    };
    const attrs = {
        strides,
        pad,
        dimRoundingMode,
        dilations,
        filterShape
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.DepthwiseConv2dNativeBackpropFilter), inputs, attrs);
}
const depthwiseConv2dNativeBackpropFilter = (0, _operation.op)({
    depthwiseConv2dNativeBackpropFilter_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b4Cpx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropInput", ()=>depthwiseConv2dNativeBackpropInput);
var _engine = require("../engine");
var _kernelNames = require("../kernel_names");
var _operation = require("./operation");
var _reshape = require("./reshape");
function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations = [
    1,
    1
], dimRoundingMode) {
    let dy4D = dy;
    let reshapedTo4D = false;
    if (dy.rank === 3) {
        reshapedTo4D = true;
        dy4D = (0, _reshape.reshape)(dy, [
            1,
            dy.shape[0],
            dy.shape[1],
            dy.shape[2]
        ]);
    }
    const inputs = {
        dy: dy4D,
        filter
    };
    const attrs = {
        strides,
        pad,
        dimRoundingMode,
        dilations,
        inputShape: xShape
    };
    const res = // tslint:disable-next-line: no-unnecessary-type-assertion
    (0, _engine.ENGINE).runKernel((0, _kernelNames.DepthwiseConv2dNativeBackpropInput), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const depthwiseConv2dNativeBackpropInput = (0, _operation.op)({
    depthwiseConv2dNativeBackpropInput_
});

},{"../engine":"1g2qo","../kernel_names":"hniYO","./operation":"ip64W","./reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6BB2f":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "matMul", ()=>matMul);
var _engine = require("../../engine");
var _gradients = require("../../gradients");
var _kernelNames = require("../../kernel_names");
var _tensorUtil = require("../../tensor_util");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _add = require("../add");
var _broadcastUtil = require("../broadcast_util");
var _fusedUtil = require("../fused_util");
var _matMul = require("../mat_mul");
var _operation = require("../operation");
var _reshape = require("../reshape");
/**
 * Computes the dot product of two matrices with optional activation and bias.
 *
 * ```js
 * const a = tf.tensor2d([-1, -2], [1, 2]);
 * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
 * const bias = tf.tensor2d([1, 2], [1, 2]);
 *
 * tf.fused.matMul({a, b, bias, activation: 'relu'}).print();
 * ```
 *
 * @param obj An object with the following properties:
 * - `a` First matrix in dot product operation.
 * - `b` Second matrix in dot product operation.
 * - `transposeA` If true, `a` is transposed before multiplication.
 * - `transposeB` If true, `b` is transposed before multiplication.
 * - `bias` Matrix to be added to the result.
 * - `activation` Name of activation kernel (defaults to `linear`).
 * - `preluActivationWeights` Tensor of prelu weights.
 * - `leakyreluAlpha` Alpha of leakyrelu.
 */ function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = "linear", preluActivationWeights, leakyreluAlpha = 0.2 }) {
    if ((0, _fusedUtil.shouldFuse)((0, _engine.ENGINE).state.gradientDepth, activation) === false) {
        let result = (0, _matMul.matMul)(a, b, transposeA, transposeB);
        if (bias != null) result = (0, _add.add)(result, bias);
        return (0, _fusedUtil.applyActivation)(result, activation, preluActivationWeights, leakyreluAlpha);
    }
    let $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "fused matMul");
    let $b = (0, _tensorUtilEnv.convertToTensor)(b, "b", "fused matMul");
    [$a, $b] = (0, _tensorUtil.makeTypesMatch)($a, $b);
    const innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
    const innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
    const outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
    const outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
    const outerDimsA = $a.shape.slice(0, -2);
    const outerDimsB = $b.shape.slice(0, -2);
    const batchDimA = _util.sizeFromShape(outerDimsA);
    const batchDimB = _util.sizeFromShape(outerDimsB);
    _util.assert(innerShapeA === innerShapeB, ()=>`Error in fused matMul: inner shapes (${innerShapeA}) and (` + `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` + `${$b.shape} and transposeA=${transposeA}` + ` and transposeB=${transposeB} must match.`);
    const outShapeOuterDims = _broadcastUtil.assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));
    const outShape = outShapeOuterDims.concat([
        outerShapeA,
        outerShapeB
    ]);
    const a3D = transposeA ? (0, _reshape.reshape)($a, [
        batchDimA,
        innerShapeA,
        outerShapeA
    ]) : (0, _reshape.reshape)($a, [
        batchDimA,
        outerShapeA,
        innerShapeA
    ]);
    const b3D = transposeB ? (0, _reshape.reshape)($b, [
        batchDimB,
        outerShapeB,
        innerShapeB
    ]) : (0, _reshape.reshape)($b, [
        batchDimB,
        innerShapeB,
        outerShapeB
    ]);
    let $bias;
    if (bias != null) {
        $bias = (0, _tensorUtilEnv.convertToTensor)(bias, "bias", "fused matMul");
        [$bias] = (0, _tensorUtil.makeTypesMatch)($bias, $a);
        _broadcastUtil.assertAndGetBroadcastShape(outShape, $bias.shape);
    }
    let $preluActivationWeights;
    if (preluActivationWeights != null) $preluActivationWeights = (0, _tensorUtilEnv.convertToTensor)(preluActivationWeights, "prelu weights", "fused matMul");
    const grad = (dy, saved)=>{
        const [a3D, b3D, y, $bias] = saved;
        // we reshape dy because the result of the forward is not
        // necessarily going to be a 3d tensor due to a reshape done at the end of
        // the customOp.
        const dyActivation = (0, _fusedUtil.getFusedDyActivation)((0, _reshape.reshape)(dy, y.shape), y, activation);
        let aDer;
        let bDer;
        if (!transposeA && !transposeB) {
            aDer = (0, _matMul.matMul)(dyActivation, b3D, false, true);
            bDer = (0, _matMul.matMul)(a3D, dyActivation, true, false);
        } else if (!transposeA && transposeB) {
            aDer = (0, _matMul.matMul)(dyActivation, b3D, false, false);
            bDer = (0, _matMul.matMul)(dyActivation, a3D, true, false);
        } else if (transposeA && !transposeB) {
            aDer = (0, _matMul.matMul)(b3D, dyActivation, false, true);
            bDer = (0, _matMul.matMul)(a3D, dyActivation, false, false);
        } else {
            aDer = (0, _matMul.matMul)(b3D, dyActivation, true, true);
            bDer = (0, _matMul.matMul)(dyActivation, a3D, true, true);
        }
        if (bias != null) {
            const biasDer = (0, _fusedUtil.getFusedBiasGradient)($bias, dyActivation);
            return [
                aDer,
                bDer,
                biasDer
            ];
        } else return [
            aDer,
            bDer
        ];
    };
    const inputs = {
        a: a3D,
        b: b3D,
        bias: $bias,
        preluActivationWeights: $preluActivationWeights
    };
    const attrs = {
        transposeA,
        transposeB,
        activation,
        leakyreluAlpha
    };
    // Depending on the the params passed in we will have different number of
    // inputs and thus a a different number of elements in the gradient.
    if (bias == null) {
        const customOp = (0, _gradients.customGrad)((a3D, b3D, save)=>{
            const res = // tslint:disable-next-line: no-unnecessary-type-assertion
            (0, _engine.ENGINE).runKernel((0, _kernelNames._FusedMatMul), inputs, attrs);
            save([
                a3D,
                b3D,
                res
            ]);
            return {
                value: (0, _reshape.reshape)(res, outShape),
                gradFunc: grad
            };
        });
        return customOp(a3D, b3D);
    } else {
        const customOpWithBias = (0, _gradients.customGrad)((a3D, b3D, $bias, save)=>{
            const res = // tslint:disable-next-line: no-unnecessary-type-assertion
            (0, _engine.ENGINE).runKernel((0, _kernelNames._FusedMatMul), inputs, attrs);
            save([
                a3D,
                b3D,
                res,
                $bias
            ]);
            return {
                value: (0, _reshape.reshape)(res, outShape),
                gradFunc: grad
            };
        });
        return customOpWithBias(a3D, b3D, $bias);
    }
}
const matMul = /* @__PURE__ */ (0, _operation.op)({
    fusedMatMul_
});

},{"../../engine":"1g2qo","../../gradients":"dDHpg","../../kernel_names":"hniYO","../../tensor_util":"8jzJG","../../tensor_util_env":"6vfHI","../../util":"ctavo","../add":"aynh7","../broadcast_util":"eLo4J","../fused_util":"8847K","../mat_mul":"fiMp4","../operation":"ip64W","../reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"crMwx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "hammingWindow", ()=>hammingWindow);
var _operation = require("../operation");
var _signalOpsUtil = require("../signal_ops_util");
/**
 * Generate a hamming window.
 *
 * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
 *
 * ```js
 * tf.signal.hammingWindow(10).print();
 * ```
 * @param The length of window
 *
 * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
 */ function hammingWindow_(windowLength) {
    return (0, _signalOpsUtil.cosineWindow)(windowLength, 0.54, 0.46);
}
const hammingWindow = /* @__PURE__ */ (0, _operation.op)({
    hammingWindow_
});

},{"../operation":"ip64W","../signal_ops_util":"1EuF4","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lwQEl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "hannWindow", ()=>hannWindow);
var _operation = require("../operation");
var _signalOpsUtil = require("../signal_ops_util");
/**
 * Generate a Hann window.
 *
 * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows
 *
 * ```js
 * tf.signal.hannWindow(10).print();
 * ```
 * @param The length of window
 *
 * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
 */ function hannWindow_(windowLength) {
    return (0, _signalOpsUtil.cosineWindow)(windowLength, 0.5, 0.5);
}
const hannWindow = /* @__PURE__ */ (0, _operation.op)({
    hannWindow_
});

},{"../operation":"ip64W","../signal_ops_util":"1EuF4","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3ntgK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "frame", ()=>frame);
var _concat = require("../concat");
var _fill = require("../fill");
var _operation = require("../operation");
var _reshape = require("../reshape");
var _slice = require("../slice");
var _tensor2D = require("../tensor2d");
/**
 * Expands input into frames of frameLength.
 * Slides a window size with frameStep.
 *
 * ```js
 * tf.signal.frame([1, 2, 3], 2, 1).print();
 * ```
 * @param signal The input tensor to be expanded
 * @param frameLength Length of each frame
 * @param frameStep The frame hop size in samples.
 * @param padEnd Whether to pad the end of signal with padValue.
 * @param padValue A number to use where the input signal does
 *     not exist when padEnd is True.
 *
 * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
 */ function frame_(signal, frameLength, frameStep, padEnd = false, padValue = 0) {
    let start = 0;
    const output = [];
    while(start + frameLength <= signal.size){
        output.push((0, _slice.slice)(signal, start, frameLength));
        start += frameStep;
    }
    if (padEnd) while(start < signal.size){
        const padLen = start + frameLength - signal.size;
        const pad = (0, _concat.concat)([
            (0, _slice.slice)(signal, start, frameLength - padLen),
            (0, _fill.fill)([
                padLen
            ], padValue)
        ]);
        output.push(pad);
        start += frameStep;
    }
    if (output.length === 0) return (0, _tensor2D.tensor2d)([], [
        0,
        frameLength
    ]);
    return (0, _reshape.reshape)((0, _concat.concat)(output), [
        output.length,
        frameLength
    ]);
}
const frame = /* @__PURE__ */ (0, _operation.op)({
    frame_
});

},{"../concat":"1NOyL","../fill":"i7goe","../operation":"ip64W","../reshape":"ew3wZ","../slice":"dr7Gq","../tensor2d":"faX5H","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gUBf5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stft", ()=>stft);
var _mul = require("../mul");
var _operation = require("../operation");
var _signalOpsUtil = require("../signal_ops_util");
var _rfft = require("../spectral/rfft");
var _frame = require("./frame");
var _hannWindow = require("./hann_window");
/**
 * Computes the Short-time Fourier Transform of signals
 * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
 *
 * ```js
 * const input = tf.tensor1d([1, 1, 1, 1, 1])
 * tf.signal.stft(input, 3, 1).print();
 * ```
 * @param signal 1-dimensional real value tensor.
 * @param frameLength The window length of samples.
 * @param frameStep The number of samples to step.
 * @param fftLength The size of the FFT to apply.
 * @param windowFn A callable that takes a window length and returns 1-d tensor.
 *
 * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'}
 */ function stft_(signal, frameLength, frameStep, fftLength, windowFn = (0, _hannWindow.hannWindow)) {
    if (fftLength == null) fftLength = (0, _signalOpsUtil.enclosingPowerOfTwo)(frameLength);
    const framedSignal = (0, _frame.frame)(signal, frameLength, frameStep);
    const windowedSignal = (0, _mul.mul)(framedSignal, windowFn(frameLength));
    return (0, _rfft.rfft)(windowedSignal, fftLength);
}
const stft = /* @__PURE__ */ (0, _operation.op)({
    stft_
});

},{"../mul":"fOWrC","../operation":"ip64W","../signal_ops_util":"1EuF4","../spectral/rfft":"hnd5A","./frame":"3ntgK","./hann_window":"lwQEl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kQOfI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cropAndResize", ()=>cropAndResize);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _operation = require("../operation");
/**
 * Extracts crops from the input image tensor and resizes them using bilinear
 * sampling or nearest neighbor sampling (possibly with aspect ratio change)
 * to a common output size specified by cropSize.
 *
 * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,
 *     where imageHeight and imageWidth must be positive, specifying the
 *     batch of images from which to take crops
 * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized
 *     coordinates of the box in the `boxInd[i]`th image in the batch
 * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range
 *     `[0, batch)` that specifies the image that the `i`-th box refers to.
 * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`
 *     specifying the size to which all crops are resized to.
 * @param method Optional string from `'bilinear' | 'nearest'`,
 *     defaults to bilinear, which specifies the sampling method for resizing
 * @param extrapolationValue A threshold for deciding when to remove boxes based
 *     on score. Defaults to 0.
 * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function cropAndResize_(image, boxes, boxInd, cropSize, method = "bilinear", extrapolationValue = 0) {
    const $image = (0, _tensorUtilEnv.convertToTensor)(image, "image", "cropAndResize");
    const $boxes = (0, _tensorUtilEnv.convertToTensor)(boxes, "boxes", "cropAndResize", "float32");
    const $boxInd = (0, _tensorUtilEnv.convertToTensor)(boxInd, "boxInd", "cropAndResize", "int32");
    const numBoxes = $boxes.shape[0];
    _util.assert($image.rank === 4, ()=>"Error in cropAndResize: image must be rank 4," + `but got rank ${$image.rank}.`);
    _util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, ()=>`Error in cropAndResize: boxes must be have size [${numBoxes},4] ` + `but had shape ${$boxes.shape}.`);
    _util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, ()=>`Error in cropAndResize: boxInd must be have size [${numBoxes}] ` + `but had shape ${$boxes.shape}.`);
    _util.assert(cropSize.length === 2, ()=>`Error in cropAndResize: cropSize must be of length 2, but got ` + `length ${cropSize.length}.`);
    _util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, ()=>`cropSize must be atleast [1,1], but was ${cropSize}`);
    _util.assert(method === "bilinear" || method === "nearest", ()=>`method must be bilinear or nearest, but was ${method}`);
    const inputs = {
        image: $image,
        boxes: $boxes,
        boxInd: $boxInd
    };
    const attrs = {
        method,
        extrapolationValue,
        cropSize
    };
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.CropAndResize), inputs, attrs);
    return res;
}
const cropAndResize = /* @__PURE__ */ (0, _operation.op)({
    cropAndResize_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../../util":"ctavo","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7xdFd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "flipLeftRight", ()=>flipLeftRight);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _operation = require("../operation");
/**
 * Flips the image left to right. Currently available in the CPU, WebGL, and
 * WASM backends.
 *
 * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
 */ /** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ function flipLeftRight_(image) {
    const $image = (0, _tensorUtilEnv.convertToTensor)(image, "image", "flipLeftRight", "float32");
    _util.assert($image.rank === 4, ()=>"Error in flipLeftRight: image must be rank 4," + `but got rank ${$image.rank}.`);
    const inputs = {
        image: $image
    };
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.FlipLeftRight), inputs, {});
    return res;
}
const flipLeftRight = /* @__PURE__ */ (0, _operation.op)({
    flipLeftRight_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../../util":"ctavo","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hbmqN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "grayscaleToRGB", ()=>grayscaleToRGB);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _operation = require("../operation");
var _tile = require("../tile");
/**
 * Converts images from grayscale to RGB format.
 *
 * @param image A grayscale tensor to convert. The `image`'s last dimension must
 *     be size 1 with at least a two-dimensional shape.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function grayscaleToRGB_(image) {
    const $image = (0, _tensorUtilEnv.convertToTensor)(image, "image", "grayscaleToRGB");
    const lastDimsIdx = $image.rank - 1;
    const lastDims = $image.shape[lastDimsIdx];
    _util.assert($image.rank >= 2, ()=>"Error in grayscaleToRGB: images must be at least rank 2, " + `but got rank ${$image.rank}.`);
    _util.assert(lastDims === 1, ()=>"Error in grayscaleToRGB: last dimension of a grayscale image " + `should be size 1, but got size ${lastDims}.`);
    const reps = new Array($image.rank);
    reps.fill(1, 0, lastDimsIdx);
    reps[lastDimsIdx] = 3;
    return (0, _tile.tile)($image, reps);
}
const grayscaleToRGB = /* @__PURE__ */ (0, _operation.op)({
    grayscaleToRGB_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../operation":"ip64W","../tile":"gzqG2","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hrZfi":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rgbToGrayscale", ()=>rgbToGrayscale);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _cast = require("../cast");
var _einsum = require("../einsum");
var _expandDims = require("../expand_dims");
var _operation = require("../operation");
var _tensor1D = require("../tensor1d");
/**
 * Converts images from RGB format to grayscale.
 *
 * @param image A RGB tensor to convert. The `image`'s last dimension must
 *     be size 3 with at least a two-dimensional shape.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function rgbToGrayscale_(image) {
    const $image = (0, _tensorUtilEnv.convertToTensor)(image, "image", "RGBToGrayscale");
    const lastDimsIdx = $image.rank - 1;
    const lastDims = $image.shape[lastDimsIdx];
    _util.assert($image.rank >= 2, ()=>"Error in RGBToGrayscale: images must be at least rank 2, " + `but got rank ${$image.rank}.`);
    _util.assert(lastDims === 3, ()=>"Error in RGBToGrayscale: last dimension of an RGB image " + `should be size 3, but got size ${lastDims}.`);
    // Remember original dtype so we can convert back if needed
    const origDtype = $image.dtype;
    const fltImage = (0, _cast.cast)($image, "float32");
    const rgbWeights = (0, _tensor1D.tensor1d)([
        0.2989,
        0.5870,
        0.1140
    ]);
    let grayFloat;
    switch($image.rank){
        case 2:
            grayFloat = (0, _einsum.einsum)("ij,j->i", fltImage, rgbWeights);
            break;
        case 3:
            grayFloat = (0, _einsum.einsum)("ijk,k->ij", fltImage, rgbWeights);
            break;
        case 4:
            grayFloat = (0, _einsum.einsum)("ijkl,l->ijk", fltImage, rgbWeights);
            break;
        case 5:
            grayFloat = (0, _einsum.einsum)("ijklm,m->ijkl", fltImage, rgbWeights);
            break;
        case 6:
            grayFloat = (0, _einsum.einsum)("ijklmn,n->ijklm", fltImage, rgbWeights);
            break;
        default:
            throw new Error("Not a valid tensor rank.");
    }
    grayFloat = (0, _expandDims.expandDims)(grayFloat, -1);
    return (0, _cast.cast)(grayFloat, origDtype);
}
const rgbToGrayscale = /* @__PURE__ */ (0, _operation.op)({
    rgbToGrayscale_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../cast":"b4VDA","../einsum":"adKPG","../expand_dims":"lc7t7","../operation":"ip64W","../tensor1d":"aEL9e","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"df6QK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rotateWithOffset", ()=>rotateWithOffset);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _operation = require("../operation");
/**
 * Rotates the input image tensor counter-clockwise with an optional offset
 * center of rotation. Currently available in the CPU, WebGL, and WASM backends.
 *
 * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
 * @param radians The amount of rotation.
 * @param fillValue The value to fill in the empty space leftover
 *     after rotation. Can be either a single grayscale value (0-255), or an
 *     array of three numbers `[red, green, blue]` specifying the red, green,
 *     and blue channels. Defaults to `0` (black).
 * @param center The center of rotation. Can be either a single value (0-1), or
 *     an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates
 *     the image around its center).
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function rotateWithOffset_(image, radians, fillValue = 0, center = 0.5) {
    const $image = (0, _tensorUtilEnv.convertToTensor)(image, "image", "rotateWithOffset", "float32");
    _util.assert($image.rank === 4, ()=>"Error in rotateWithOffset: image must be rank 4," + `but got rank ${$image.rank}.`);
    const inputs = {
        image: $image
    };
    const attrs = {
        radians,
        fillValue,
        center
    };
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.RotateWithOffset), inputs, attrs);
    return res;
}
const rotateWithOffset = /* @__PURE__ */ (0, _operation.op)({
    rotateWithOffset_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../../util":"ctavo","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bQBrw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppression", ()=>nonMaxSuppression);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _nonmaxUtil = require("../nonmax_util");
var _operation = require("../operation");
/**
 * Performs non maximum suppression of bounding boxes based on
 * iou (intersection over union).
 *
 * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
 *     the bounding box.
 * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
 * @param maxOutputSize The maximum number of boxes to be selected.
 * @param iouThreshold A float representing the threshold for deciding whether
 *     boxes overlap too much with respect to IOU. Must be between [0, 1].
 *     Defaults to 0.5 (50% box overlap).
 * @param scoreThreshold A threshold for deciding when to remove boxes based
 *     on score. Defaults to -inf, which means any score is accepted.
 * @return A 1D tensor with the selected box indices.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
    const $boxes = (0, _tensorUtilEnv.convertToTensor)(boxes, "boxes", "nonMaxSuppression", "float32");
    const $scores = (0, _tensorUtilEnv.convertToTensor)(scores, "scores", "nonMaxSuppression", "float32");
    const inputs = (0, _nonmaxUtil.nonMaxSuppSanityCheck)($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
    maxOutputSize = inputs.maxOutputSize;
    iouThreshold = inputs.iouThreshold;
    scoreThreshold = inputs.scoreThreshold;
    const attrs = {
        maxOutputSize,
        iouThreshold,
        scoreThreshold
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.NonMaxSuppressionV3), {
        boxes: $boxes,
        scores: $scores
    }, attrs);
}
const nonMaxSuppression = /* @__PURE__ */ (0, _operation.op)({
    nonMaxSuppression_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../nonmax_util":"g968U","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g968U":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppSanityCheck", ()=>nonMaxSuppSanityCheck);
var _util = require("../util");
function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
    if (iouThreshold == null) iouThreshold = 0.5;
    if (scoreThreshold == null) scoreThreshold = Number.NEGATIVE_INFINITY;
    if (softNmsSigma == null) softNmsSigma = 0.0;
    const numBoxes = boxes.shape[0];
    maxOutputSize = Math.min(maxOutputSize, numBoxes);
    _util.assert(0 <= iouThreshold && iouThreshold <= 1, ()=>`iouThreshold must be in [0, 1], but was '${iouThreshold}'`);
    _util.assert(boxes.rank === 2, ()=>`boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);
    _util.assert(boxes.shape[1] === 4, ()=>`boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);
    _util.assert(scores.rank === 1, ()=>"scores must be a 1D tensor");
    _util.assert(scores.shape[0] === numBoxes, ()=>`scores has incompatible shape with boxes. Expected ${numBoxes}, ` + `but was ${scores.shape[0]}`);
    _util.assert(0 <= softNmsSigma && softNmsSigma <= 1, ()=>`softNmsSigma must be in [0, 1], but was '${softNmsSigma}'`);
    return {
        maxOutputSize,
        iouThreshold,
        scoreThreshold,
        softNmsSigma
    };
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8ps8b":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionAsync", ()=>nonMaxSuppressionAsync);
var _nonMaxSuppressionImpl = require("../../backends/non_max_suppression_impl");
var _tensorUtilEnv = require("../../tensor_util_env");
var _nonmaxUtil = require("../nonmax_util");
var _tensor1D = require("../tensor1d");
/**
 * Performs non maximum suppression of bounding boxes based on
 * iou (intersection over union).
 *
 * This is the async version of `nonMaxSuppression`
 *
 * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
 *     the bounding box.
 * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
 * @param maxOutputSize The maximum number of boxes to be selected.
 * @param iouThreshold A float representing the threshold for deciding whether
 *     boxes overlap too much with respect to IOU. Must be between [0, 1].
 *     Defaults to 0.5 (50% box overlap).
 * @param scoreThreshold A threshold for deciding when to remove boxes based
 *     on score. Defaults to -inf, which means any score is accepted.
 * @return A 1D tensor with the selected box indices.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ async function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
    const $boxes = (0, _tensorUtilEnv.convertToTensor)(boxes, "boxes", "nonMaxSuppressionAsync");
    const $scores = (0, _tensorUtilEnv.convertToTensor)(scores, "scores", "nonMaxSuppressionAsync");
    const inputs = (0, _nonmaxUtil.nonMaxSuppSanityCheck)($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
    maxOutputSize = inputs.maxOutputSize;
    iouThreshold = inputs.iouThreshold;
    scoreThreshold = inputs.scoreThreshold;
    const boxesAndScores = await Promise.all([
        $boxes.data(),
        $scores.data()
    ]);
    const boxesVals = boxesAndScores[0];
    const scoresVals = boxesAndScores[1];
    // We call a cpu based impl directly with the typedarray data  here rather
    // than a kernel because all kernels are synchronous (and thus cannot await
    // .data()).
    const { selectedIndices } = (0, _nonMaxSuppressionImpl.nonMaxSuppressionV3Impl)(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
    if ($boxes !== boxes) $boxes.dispose();
    if ($scores !== scores) $scores.dispose();
    return (0, _tensor1D.tensor1d)(selectedIndices, "int32");
}
const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;

},{"../../backends/non_max_suppression_impl":"e0x4w","../../tensor_util_env":"6vfHI","../nonmax_util":"g968U","../tensor1d":"aEL9e","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"e0x4w":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV3Impl", ()=>nonMaxSuppressionV3Impl);
parcelHelpers.export(exports, "nonMaxSuppressionV4Impl", ()=>nonMaxSuppressionV4Impl);
parcelHelpers.export(exports, "nonMaxSuppressionV5Impl", ()=>nonMaxSuppressionV5Impl);
var _nonMaxSuppressionUtil = require("./non_max_suppression_util");
function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */ );
}
function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) {
    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */ , false, padToMaxOutputSize /* padToMaxOutputSize */ , true);
}
function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
    return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true);
}
function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor = false, padToMaxOutputSize = false, returnValidOutputs = false) {
    // The list is sorted in ascending order, so that we can always pop the
    // candidate with the largest score in O(1) time.
    const candidates = [];
    for(let i = 0; i < scores.length; i++)if (scores[i] > scoreThreshold) candidates.push({
        score: scores[i],
        boxIndex: i,
        suppressBeginIndex: 0
    });
    candidates.sort(ascendingComparator);
    // If softNmsSigma is 0, the outcome of this algorithm is exactly same as
    // before.
    const scale = softNmsSigma > 0 ? -0.5 / softNmsSigma : 0.0;
    const selectedIndices = [];
    const selectedScores = [];
    while(selectedIndices.length < maxOutputSize && candidates.length > 0){
        const candidate = candidates.pop();
        const { score: originalScore, boxIndex, suppressBeginIndex } = candidate;
        if (originalScore < scoreThreshold) break;
        // Overlapping boxes are likely to have similar scores, therefore we
        // iterate through the previously selected boxes backwards in order to
        // see if candidate's score should be suppressed. We use
        // suppressBeginIndex to track and ensure a candidate can be suppressed
        // by a selected box no more than once. Also, if the overlap exceeds
        // iouThreshold, we simply ignore the candidate.
        let ignoreCandidate = false;
        for(let j = selectedIndices.length - 1; j >= suppressBeginIndex; --j){
            const iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]);
            if (iou >= iouThreshold) {
                ignoreCandidate = true;
                break;
            }
            candidate.score = candidate.score * suppressWeight(iouThreshold, scale, iou);
            if (candidate.score <= scoreThreshold) break;
        }
        // At this point, if `candidate.score` has not dropped below
        // `scoreThreshold`, then we know that we went through all of the
        // previous selections and can safely update `suppressBeginIndex` to the
        // end of the selected array. Then we can re-insert the candidate with
        // the updated score and suppressBeginIndex back in the candidate list.
        // If on the other hand, `candidate.score` has dropped below the score
        // threshold, we will not add it back to the candidates list.
        candidate.suppressBeginIndex = selectedIndices.length;
        if (!ignoreCandidate) {
            // Candidate has passed all the tests, and is not suppressed, so
            // select the candidate.
            if (candidate.score === originalScore) {
                selectedIndices.push(boxIndex);
                selectedScores.push(candidate.score);
            } else if (candidate.score > scoreThreshold) // Candidate's score is suppressed but is still high enough to be
            // considered, so add back to the candidates list.
            (0, _nonMaxSuppressionUtil.binaryInsert)(candidates, candidate, ascendingComparator);
        }
    }
    // NonMaxSuppressionV4 feature: padding output to maxOutputSize.
    const validOutputs = selectedIndices.length;
    const elemsToPad = maxOutputSize - validOutputs;
    if (padToMaxOutputSize && elemsToPad > 0) {
        selectedIndices.push(...new Array(elemsToPad).fill(0));
        selectedScores.push(...new Array(elemsToPad).fill(0.0));
    }
    const result = {
        selectedIndices
    };
    if (returnScoresTensor) result["selectedScores"] = selectedScores;
    if (returnValidOutputs) result["validOutputs"] = validOutputs;
    return result;
}
function intersectionOverUnion(boxes, i, j) {
    const iCoord = boxes.subarray(i * 4, i * 4 + 4);
    const jCoord = boxes.subarray(j * 4, j * 4 + 4);
    const yminI = Math.min(iCoord[0], iCoord[2]);
    const xminI = Math.min(iCoord[1], iCoord[3]);
    const ymaxI = Math.max(iCoord[0], iCoord[2]);
    const xmaxI = Math.max(iCoord[1], iCoord[3]);
    const yminJ = Math.min(jCoord[0], jCoord[2]);
    const xminJ = Math.min(jCoord[1], jCoord[3]);
    const ymaxJ = Math.max(jCoord[0], jCoord[2]);
    const xmaxJ = Math.max(jCoord[1], jCoord[3]);
    const areaI = (ymaxI - yminI) * (xmaxI - xminI);
    const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
    if (areaI <= 0 || areaJ <= 0) return 0.0;
    const intersectionYmin = Math.max(yminI, yminJ);
    const intersectionXmin = Math.max(xminI, xminJ);
    const intersectionYmax = Math.min(ymaxI, ymaxJ);
    const intersectionXmax = Math.min(xmaxI, xmaxJ);
    const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * Math.max(intersectionXmax - intersectionXmin, 0.0);
    return intersectionArea / (areaI + areaJ - intersectionArea);
}
// A Gaussian penalty function, this method always returns values in [0, 1].
// The weight is a function of similarity, the more overlap two boxes are, the
// smaller the weight is, meaning highly overlapping boxe will be significantly
// penalized. On the other hand, a non-overlapping box will not be penalized.
function suppressWeight(iouThreshold, scale, iou) {
    const weight = Math.exp(scale * iou * iou);
    return iou <= iouThreshold ? weight : 0.0;
}
function ascendingComparator(c1, c2) {
    // For objects with same scores, we make the object with the larger index go
    // first. In an array that pops from the end, this means that the object with
    // the smaller index will be popped first. This ensures the same output as
    // the TensorFlow python version.
    return c1.score - c2.score || c1.score === c2.score && c2.boxIndex - c1.boxIndex;
}

},{"./non_max_suppression_util":"i4jUO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"i4jUO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Inserts a value into a sorted array. This method allows duplicate, meaning it
 * allows inserting duplicate value, in which case, the element will be inserted
 * at the lowest index of the value.
 * @param arr The array to modify.
 * @param element The element to insert.
 * @param comparator Optional. If no comparator is specified, elements are
 * compared using array_util.defaultComparator, which is suitable for Strings
 * and Numbers in ascending arrays. If the array contains multiple instances of
 * the target value, the left-most instance will be returned. To provide a
 * comparator, it should take 2 arguments to compare and return a negative,
 * zero, or a positive number.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "binaryInsert", ()=>binaryInsert);
/**
 * Searches the array for the target using binary search, returns the index
 * of the found element, or position to insert if element not found. If no
 * comparator is specified, elements are compared using array_
 * util.defaultComparator, which is suitable for Strings and Numbers in
 * ascending arrays. If the array contains multiple instances of the target
 * value, the left-most instance will be returned.
 * @param arr The array to be searched in.
 * @param target The target to be searched for.
 * @param comparator Should take 2 arguments to compare and return a negative,
 *    zero, or a positive number.
 * @return Lowest index of the target value if found, otherwise the insertion
 *    point where the target should be inserted, in the form of
 *    (-insertionPoint - 1).
 */ parcelHelpers.export(exports, "binarySearch", ()=>binarySearch);
function binaryInsert(arr, element, comparator) {
    const index = binarySearch(arr, element, comparator);
    const insertionPoint = index < 0 ? -(index + 1) : index;
    arr.splice(insertionPoint, 0, element);
}
function binarySearch(arr, target, comparator) {
    return binarySearch_(arr, target, comparator || defaultComparator);
}
/**
 * Compares its two arguments for order.
 * @param a The first element to be compared.
 * @param b The second element to be compared.
 * @return A negative number, zero, or a positive number as the first
 *     argument is less than, equal to, or greater than the second.
 */ function defaultComparator(a, b) {
    return a > b ? 1 : a < b ? -1 : 0;
}
function binarySearch_(arr, target, comparator) {
    let left = 0;
    let right = arr.length;
    let middle = 0;
    let found = false;
    while(left < right){
        middle = left + (right - left >>> 1);
        const compareResult = comparator(target, arr[middle]);
        if (compareResult > 0) left = middle + 1;
        else {
            right = middle;
            // If compareResult is 0, the value is found. We record it is found,
            // and then keep looking because there may be duplicate.
            found = !compareResult;
        }
    }
    return found ? left : -left - 1;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"khN9i":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionWithScore", ()=>nonMaxSuppressionWithScore);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _nonmaxUtil = require("../nonmax_util");
var _operation = require("../operation");
/**
 * Performs non maximum suppression of bounding boxes based on
 * iou (intersection over union).
 *
 * This op also supports a Soft-NMS mode (cf.
 * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
 * of other overlapping boxes, therefore favoring different regions of the image
 * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
 * parameter to be larger than 0.
 *
 * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
 *     the bounding box.
 * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
 * @param maxOutputSize The maximum number of boxes to be selected.
 * @param iouThreshold A float representing the threshold for deciding whether
 *     boxes overlap too much with respect to IOU. Must be between [0, 1].
 *     Defaults to 0.5 (50% box overlap).
 * @param scoreThreshold A threshold for deciding when to remove boxes based
 *     on score. Defaults to -inf, which means any score is accepted.
 * @param softNmsSigma A float representing the sigma parameter for Soft NMS.
 *     When sigma is 0, it falls back to nonMaxSuppression.
 * @return A map with the following properties:
 *     - selectedIndices: A 1D tensor with the selected box indices.
 *     - selectedScores: A 1D tensor with the corresponding scores for each
 *       selected box.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {
    const $boxes = (0, _tensorUtilEnv.convertToTensor)(boxes, "boxes", "nonMaxSuppression");
    const $scores = (0, _tensorUtilEnv.convertToTensor)(scores, "scores", "nonMaxSuppression");
    const params = (0, _nonmaxUtil.nonMaxSuppSanityCheck)($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
    maxOutputSize = params.maxOutputSize;
    iouThreshold = params.iouThreshold;
    scoreThreshold = params.scoreThreshold;
    softNmsSigma = params.softNmsSigma;
    const inputs = {
        boxes: $boxes,
        scores: $scores
    };
    const attrs = {
        maxOutputSize,
        iouThreshold,
        scoreThreshold,
        softNmsSigma
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.NonMaxSuppressionV5), inputs, attrs);
    return {
        selectedIndices: result[0],
        selectedScores: result[1]
    };
}
const nonMaxSuppressionWithScore = /* @__PURE__ */ (0, _operation.op)({
    nonMaxSuppressionWithScore_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../nonmax_util":"g968U","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bH9TE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionWithScoreAsync", ()=>nonMaxSuppressionWithScoreAsync);
var _nonMaxSuppressionImpl = require("../../backends/non_max_suppression_impl");
var _tensorUtilEnv = require("../../tensor_util_env");
var _nonmaxUtil = require("../nonmax_util");
var _tensor1D = require("../tensor1d");
/**
 * Asynchronously performs non maximum suppression of bounding boxes based on
 * iou (intersection over union).
 *
 * This op also supports a Soft-NMS mode (cf.
 * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score
 * of other overlapping boxes, therefore favoring different regions of the image
 * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma`
 * parameter to be larger than 0.
 *
 * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
 *     the bounding box.
 * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
 * @param maxOutputSize The maximum number of boxes to be selected.
 * @param iouThreshold A float representing the threshold for deciding whether
 *     boxes overlap too much with respect to IOU. Must be between [0, 1].
 *     Defaults to 0.5 (50% box overlap).
 * @param scoreThreshold A threshold for deciding when to remove boxes based
 *     on score. Defaults to -inf, which means any score is accepted.
 * @param softNmsSigma A float representing the sigma parameter for Soft NMS.
 *     When sigma is 0, it falls back to nonMaxSuppression.
 * @return A map with the following properties:
 *     - selectedIndices: A 1D tensor with the selected box indices.
 *     - selectedScores: A 1D tensor with the corresponding scores for each
 *       selected box.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ async function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, softNmsSigma = 0.0) {
    const $boxes = (0, _tensorUtilEnv.convertToTensor)(boxes, "boxes", "nonMaxSuppressionAsync");
    const $scores = (0, _tensorUtilEnv.convertToTensor)(scores, "scores", "nonMaxSuppressionAsync");
    const params = (0, _nonmaxUtil.nonMaxSuppSanityCheck)($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
    maxOutputSize = params.maxOutputSize;
    iouThreshold = params.iouThreshold;
    scoreThreshold = params.scoreThreshold;
    softNmsSigma = params.softNmsSigma;
    const boxesAndScores = await Promise.all([
        $boxes.data(),
        $scores.data()
    ]);
    const boxesVals = boxesAndScores[0];
    const scoresVals = boxesAndScores[1];
    // We call a cpu based impl directly with the typedarray data  here rather
    // than a kernel because all kernels are synchronous (and thus cannot await
    // .data()).
    const { selectedIndices, selectedScores } = (0, _nonMaxSuppressionImpl.nonMaxSuppressionV5Impl)(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
    if ($boxes !== boxes) $boxes.dispose();
    if ($scores !== scores) $scores.dispose();
    return {
        selectedIndices: (0, _tensor1D.tensor1d)(selectedIndices, "int32"),
        selectedScores: (0, _tensor1D.tensor1d)(selectedScores)
    };
}
const nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;

},{"../../backends/non_max_suppression_impl":"e0x4w","../../tensor_util_env":"6vfHI","../nonmax_util":"g968U","../tensor1d":"aEL9e","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3Hdah":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionPadded", ()=>nonMaxSuppressionPadded);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _nonmaxUtil = require("../nonmax_util");
var _operation = require("../operation");
/**
 * Asynchronously performs non maximum suppression of bounding boxes based on
 * iou (intersection over union), with an option to pad results.
 *
 * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
 *     the bounding box.
 * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
 * @param maxOutputSize The maximum number of boxes to be selected.
 * @param iouThreshold A float representing the threshold for deciding whether
 *     boxes overlap too much with respect to IOU. Must be between [0, 1].
 *     Defaults to 0.5 (50% box overlap).
 * @param scoreThreshold A threshold for deciding when to remove boxes based
 *     on score. Defaults to -inf, which means any score is accepted.
 * @param padToMaxOutputSize Defaults to false. If true, size of output
 *     `selectedIndices` is padded to maxOutputSize.
 * @return A map with the following properties:
 *     - selectedIndices: A 1D tensor with the selected box indices.
 *     - validOutputs: A scalar denoting how many elements in `selectedIndices`
 *       are valid. Valid elements occur first, then padding.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
    const $boxes = (0, _tensorUtilEnv.convertToTensor)(boxes, "boxes", "nonMaxSuppression");
    const $scores = (0, _tensorUtilEnv.convertToTensor)(scores, "scores", "nonMaxSuppression");
    const params = (0, _nonmaxUtil.nonMaxSuppSanityCheck)($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
    const $maxOutputSize = params.maxOutputSize;
    const $iouThreshold = params.iouThreshold;
    const $scoreThreshold = params.scoreThreshold;
    const inputs = {
        boxes: $boxes,
        scores: $scores
    };
    const attrs = {
        maxOutputSize: $maxOutputSize,
        iouThreshold: $iouThreshold,
        scoreThreshold: $scoreThreshold,
        padToMaxOutputSize
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.NonMaxSuppressionV4), inputs, attrs);
    return {
        selectedIndices: result[0],
        validOutputs: result[1]
    };
}
const nonMaxSuppressionPadded = /* @__PURE__ */ (0, _operation.op)({
    nonMaxSuppressionPadded_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../nonmax_util":"g968U","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dJg2S":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionPaddedAsync", ()=>nonMaxSuppressionPaddedAsync);
var _nonMaxSuppressionImpl = require("../../backends/non_max_suppression_impl");
var _tensorUtilEnv = require("../../tensor_util_env");
var _nonmaxUtil = require("../nonmax_util");
var _scalar = require("../scalar");
var _tensor1D = require("../tensor1d");
/**
 * Asynchronously performs non maximum suppression of bounding boxes based on
 * iou (intersection over union), with an option to pad results.
 *
 * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is
 *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of
 *     the bounding box.
 * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.
 * @param maxOutputSize The maximum number of boxes to be selected.
 * @param iouThreshold A float representing the threshold for deciding whether
 *     boxes overlap too much with respect to IOU. Must be between [0, 1].
 *     Defaults to 0.5 (50% box overlap).
 * @param scoreThreshold A threshold for deciding when to remove boxes based
 *     on score. Defaults to -inf, which means any score is accepted.
 * @param padToMaxOutputSize Defaults to false. If true, size of output
 *     `selectedIndices` is padded to maxOutputSize.
 * @return A map with the following properties:
 *     - selectedIndices: A 1D tensor with the selected box indices.
 *     - validOutputs: A scalar denoting how many elements in `selectedIndices`
 *       are valid. Valid elements occur first, then padding.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ async function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY, padToMaxOutputSize = false) {
    const $boxes = (0, _tensorUtilEnv.convertToTensor)(boxes, "boxes", "nonMaxSuppressionAsync");
    const $scores = (0, _tensorUtilEnv.convertToTensor)(scores, "scores", "nonMaxSuppressionAsync");
    const params = (0, _nonmaxUtil.nonMaxSuppSanityCheck)($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null);
    const $maxOutputSize = params.maxOutputSize;
    const $iouThreshold = params.iouThreshold;
    const $scoreThreshold = params.scoreThreshold;
    const [boxesVals, scoresVals] = await Promise.all([
        $boxes.data(),
        $scores.data()
    ]);
    // We call a cpu based impl directly with the typedarray data here rather
    // than a kernel because all kernels are synchronous (and thus cannot await
    // .data()).
    const { selectedIndices, validOutputs } = (0, _nonMaxSuppressionImpl.nonMaxSuppressionV4Impl)(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
    if ($boxes !== boxes) $boxes.dispose();
    if ($scores !== scores) $scores.dispose();
    return {
        selectedIndices: (0, _tensor1D.tensor1d)(selectedIndices, "int32"),
        validOutputs: (0, _scalar.scalar)(validOutputs, "int32")
    };
}
const nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;

},{"../../backends/non_max_suppression_impl":"e0x4w","../../tensor_util_env":"6vfHI","../nonmax_util":"g968U","../scalar":"7lhC8","../tensor1d":"aEL9e","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"h5fgY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeBilinear", ()=>resizeBilinear);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _operation = require("../operation");
var _reshape = require("../reshape");
/**
 * Bilinear resize a single 3D image or a batch of 3D images to a new shape.
 *
 * @param images The images, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
 * @param size The new shape `[newHeight, newWidth]` to resize the
 *     images to. Each channel is resized individually.
 * @param alignCorners Defaults to `false`. If true, rescale
 *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
 *     corners of images and resized images. If false, rescale by
 *     `new_height / height`. Treat similarly the width dimension.
 * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers
 *     are at 0.5, which would make the floating point coordinates of the top
 *     left pixel 0.5, 0.5.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = false) {
    const $images = (0, _tensorUtilEnv.convertToTensor)(images, "images", "resizeBilinear");
    _util.assert($images.rank === 3 || $images.rank === 4, ()=>`Error in resizeBilinear: x must be rank 3 or 4, but got ` + `rank ${$images.rank}.`);
    _util.assert(size.length === 2, ()=>`Error in resizeBilinear: new shape must 2D, but got shape ` + `${size}.`);
    _util.assert(halfPixelCenters === false || alignCorners === false, ()=>`Error in resizeBilinear: If halfPixelCenters is true, ` + `alignCorners must be false.`);
    let batchImages = $images;
    let reshapedTo4D = false;
    if ($images.rank === 3) {
        reshapedTo4D = true;
        batchImages = (0, _reshape.reshape)($images, [
            1,
            $images.shape[0],
            $images.shape[1],
            $images.shape[2]
        ]);
    }
    const [] = size;
    const inputs = {
        images: batchImages
    };
    const attrs = {
        alignCorners,
        halfPixelCenters,
        size
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.ResizeBilinear), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const resizeBilinear = /* @__PURE__ */ (0, _operation.op)({
    resizeBilinear_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../../util":"ctavo","../operation":"ip64W","../reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6vytD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeNearestNeighbor", ()=>resizeNearestNeighbor);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _operation = require("../operation");
var _reshape = require("../reshape");
/**
 * NearestNeighbor resize a batch of 3D images to a new shape.
 *
 * @param images The images, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
 * @param size The new shape `[newHeight, newWidth]` to resize the
 *     images to. Each channel is resized individually.
 * @param alignCorners Defaults to False. If true, rescale
 *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4
 *     corners of images and resized images. If false, rescale by
 *     `new_height / height`. Treat similarly the width dimension.
 * @param halfPixelCenters Defaults to `false`. Whether to assume pixels are of
 *      half the actual dimensions, and yield more accurate resizes. This flag
 *      would also make the floating point coordinates of the top left pixel
 *      0.5, 0.5.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = false) {
    const $images = (0, _tensorUtilEnv.convertToTensor)(images, "images", "resizeNearestNeighbor");
    _util.assert($images.rank === 3 || $images.rank === 4, ()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` + `rank ${$images.rank}.`);
    _util.assert(size.length === 2, ()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ` + `${size}.`);
    _util.assert($images.dtype === "float32" || $images.dtype === "int32", ()=>"`images` must have `int32` or `float32` as dtype");
    _util.assert(halfPixelCenters === false || alignCorners === false, ()=>`Error in resizeNearestNeighbor: If halfPixelCenters is true, ` + `alignCorners must be false.`);
    let batchImages = $images;
    let reshapedTo4D = false;
    if ($images.rank === 3) {
        reshapedTo4D = true;
        batchImages = (0, _reshape.reshape)($images, [
            1,
            $images.shape[0],
            $images.shape[1],
            $images.shape[2]
        ]);
    }
    const [] = size;
    const inputs = {
        images: batchImages
    };
    const attrs = {
        alignCorners,
        halfPixelCenters,
        size
    };
    // tslint:disable-next-line: no-unnecessary-type-assertion
    const res = (0, _engine.ENGINE).runKernel((0, _kernelNames.ResizeNearestNeighbor), inputs, attrs);
    if (reshapedTo4D) return (0, _reshape.reshape)(res, [
        res.shape[1],
        res.shape[2],
        res.shape[3]
    ]);
    return res;
}
const resizeNearestNeighbor = /* @__PURE__ */ (0, _operation.op)({
    resizeNearestNeighbor_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../../util":"ctavo","../operation":"ip64W","../reshape":"ew3wZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d58R4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "threshold", ()=>threshold);
var _tensor1D = require("../tensor1d");
var _operation = require("../operation");
var _cast = require("../cast");
var _split = require("../split");
var _bincount = require("../bincount");
var _lessEqual = require("../less_equal");
var _greater = require("../greater");
var _sum = require("../sum");
var _add = require("../add");
var _mul = require("../mul");
var _div = require("../div");
var _sub = require("../sub");
var _round = require("../round");
var _where = require("../where");
var _fill = require("../fill");
var _slice = require("../slice");
var _range = require("../range");
var _tensor = require("../tensor");
var _util = require("../../util");
var _tensorUtilEnv = require("../../tensor_util_env");
/**
 * Performs image binarization with corresponding threshold
 * (depends on the method)value, which creates a binary image from a grayscale.
 * @param image 3d tensor of shape [imageHeight,imageWidth, depth],
 * where imageHeight and imageWidth must be positive.The image color
 * range should be [0, 255].
 * @param method Optional string from `'binary' | 'otsu'`
 * which specifies the method for thresholding. Defaults to 'binary'.
 * @param inverted Optional boolean whichspecifies
 * if colours should be inverted. Defaults to false.
 * @param threshValue Optional number which defines threshold value from 0 to 1.
 * Defaults to 0.5.
 * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which
 * contains binarized image.
 */ function threshold_(image, method = "binary", inverted = false, threshValue = 0.5) {
    const $image = (0, _tensorUtilEnv.convertToTensor)(image, "image", "threshold");
    /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601.
    Reference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29  */ const RED_INTENCITY_COEF = 0.2989;
    const GREEN_INTENCITY_COEF = 0.5870;
    const BLUE_INTENCITY_COEF = 0.1140;
    const totalPixelsInImage = $image.shape[0] * $image.shape[1];
    let $threshold = (0, _mul.mul)((0, _tensor1D.tensor1d)([
        threshValue
    ]), 255);
    let r, g, b, grayscale;
    _util.assert($image.rank === 3, ()=>"Error in threshold: image must be rank 3," + `but got rank ${$image.rank}.`);
    _util.assert($image.shape[2] === 3 || $image.shape[2] === 1, ()=>"Error in threshold: image color channel must be equal to 3 or 1" + `but got ${$image.shape[2]}.`);
    _util.assert($image.dtype === "int32" || $image.dtype === "float32", ()=>"Error in dtype: image dtype must be int32 or float32," + `but got dtype ${$image.dtype}.`);
    _util.assert(method === "otsu" || method === "binary", ()=>`Method must be binary or otsu, but was ${method}`);
    if ($image.shape[2] === 3) {
        [r, g, b] = (0, _split.split)($image, [
            1,
            1,
            1
        ], -1);
        const $r = (0, _mul.mul)(r, RED_INTENCITY_COEF);
        const $g = (0, _mul.mul)(g, GREEN_INTENCITY_COEF);
        const $b = (0, _mul.mul)(b, BLUE_INTENCITY_COEF);
        grayscale = (0, _add.add)((0, _add.add)($r, $g), $b);
    } else grayscale = image;
    if (method === "otsu") {
        const $histogram = (0, _bincount.bincount)((0, _cast.cast)((0, _round.round)(grayscale), "int32"), (0, _tensor.tensor)([]), 256);
        $threshold = otsu($histogram, totalPixelsInImage);
    }
    const invCondition = inverted ? (0, _lessEqual.lessEqual)(grayscale, $threshold) : (0, _greater.greater)(grayscale, $threshold);
    const result = (0, _cast.cast)((0, _mul.mul)(invCondition, 255), "int32");
    return result;
}
function otsu(histogram, total) {
    let bestThresh = (0, _tensor1D.tensor1d)([
        -1
    ]);
    let bestInBetVar = (0, _tensor1D.tensor1d)([
        0
    ]);
    let cInBetVar = (0, _tensor1D.tensor1d)([
        0
    ]);
    let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;
    for(let index = 0; index < histogram.size - 1; index++){
        classFirst = (0, _slice.slice)(histogram, 0, index + 1);
        classSecond = (0, _slice.slice)(histogram, index + 1);
        weightForeground = (0, _div.div)((0, _sum.sum)(classFirst), total);
        weightBack = (0, _div.div)((0, _sum.sum)(classSecond), total);
        const meanFirstDivA = (0, _sum.sum)((0, _mul.mul)(classFirst, (0, _range.range)(0, classFirst.size)));
        meanFirst = (0, _div.div)(meanFirstDivA, (0, _sum.sum)(classFirst));
        const meanSecFill = (0, _fill.fill)(classSecond.shape, classFirst.size);
        const meanSecAdd = (0, _add.add)((0, _range.range)(0, classSecond.size), meanSecFill);
        const meanSecMul = (0, _mul.mul)(classSecond, meanSecAdd);
        meanSec = (0, _div.div)((0, _sum.sum)(meanSecMul), (0, _sum.sum)(classSecond));
        const cInBetVarSubA = (0, _sub.sub)(meanFirst, meanSec);
        const cInBetVarSubB = (0, _sub.sub)(meanFirst, meanSec);
        const cInBetVarMul = (0, _mul.mul)(weightForeground, weightBack);
        cInBetVar = (0, _mul.mul)((0, _mul.mul)(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);
        const condition = (0, _greater.greater)(cInBetVar, bestInBetVar);
        bestInBetVar = (0, _where.where)(condition, cInBetVar, bestInBetVar);
        bestThresh = (0, _where.where)(condition, (0, _tensor1D.tensor1d)([
            index
        ]), bestThresh);
    }
    return bestThresh;
}
const threshold = /* @__PURE__ */ (0, _operation.op)({
    threshold_
});

},{"../tensor1d":"aEL9e","../operation":"ip64W","../cast":"b4VDA","../split":"l0HF2","../bincount":"gW00L","../less_equal":"j9Jhy","../greater":"3Nh8O","../sum":"5awd0","../add":"aynh7","../mul":"fOWrC","../div":"5ZT1h","../sub":"jBBQ2","../round":"4aCvQ","../where":"47cKc","../fill":"i7goe","../slice":"dr7Gq","../range":"3seHd","../tensor":"jdbb1","../../util":"ctavo","../../tensor_util_env":"6vfHI","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"sijU0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transform", ()=>transform);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _operation = require("../operation");
/**
 * Applies the given transform(s) to the image(s).
 *
 * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`.
 * @param transforms Projective transform matrix/matrices. A tensor1d of length
 *     8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0,
 *     b1, b2, c0, c1], then it maps the output point (x, y) to a transformed
 *     input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k),
 *     where k = c0 x + c1 y + 1. The transforms are inverted compared to the
 *     transform mapping input points to output points.
 * @param interpolation Interpolation mode.
 *     Supported values: 'nearest', 'bilinear'. Default to 'nearest'.
 * @param fillMode Points outside the boundaries of the input are filled
 *     according to the given mode, one of 'constant', 'reflect', 'wrap',
 *     'nearest'. Default to 'constant'.
 *     'reflect': (d c b a | a b c d | d c b a ) The input is extended by
 *     reflecting about the edge of the last pixel.
 *     'constant': (k k k k | a b c d | k k k k) The input is extended by
 *     filling all values beyond the edge with the same constant value k.
 *     'wrap': (a b c d | a b c d | a b c d) The input is extended by
 *     wrapping around to the opposite edge.
 *     'nearest': (a a a a | a b c d | d d d d) The input is extended by
 *     the nearest pixel.
 * @param fillValue A float represents the value to be filled outside the
 *     boundaries when fillMode is 'constant'.
 * @param Output dimension after the transform, [height, width]. If undefined,
 *     output is the same size as input image.
 *
 * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'}
 */ function transform_(image, transforms, interpolation = "nearest", fillMode = "constant", fillValue = 0, outputShape) {
    const $image = (0, _tensorUtilEnv.convertToTensor)(image, "image", "transform", "float32");
    const $transforms = (0, _tensorUtilEnv.convertToTensor)(transforms, "transforms", "transform", "float32");
    _util.assert($image.rank === 4, ()=>"Error in transform: image must be rank 4," + `but got rank ${$image.rank}.`);
    _util.assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, ()=>`Error in transform: Input transform should be batch x 8 or 1 x 8`);
    _util.assert(outputShape == null || outputShape.length === 2, ()=>"Error in transform: outputShape must be [height, width] or null, " + `but got ${outputShape}.`);
    const inputs = {
        image: $image,
        transforms: $transforms
    };
    const attrs = {
        interpolation,
        fillMode,
        fillValue,
        outputShape
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.Transform), inputs, attrs);
}
const transform = /* @__PURE__ */ (0, _operation.op)({
    transform_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../../util":"ctavo","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cLsM5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "bandPart", ()=>bandPart);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _greaterEqual = require("../greater_equal");
var _less = require("../less");
var _lessEqual = require("../less_equal");
var _logicalAnd = require("../logical_and");
var _minimum = require("../minimum");
var _neg = require("../neg");
var _operation = require("../operation");
var _range = require("../range");
var _reshape = require("../reshape");
var _stack = require("../stack");
var _sub = require("../sub");
var _unstack = require("../unstack");
var _where = require("../where");
var _zeros = require("../zeros");
/**
 * Copy a tensor setting everything outside a central band in each innermost
 * matrix to zero.
 *
 * The band part is computed as follows: Assume input has `k` dimensions
 * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where
 * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
 * The indicator function
 * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)`
 * `&& (num_upper < 0 || (n-m) <= num_upper)`
 *
 * ```js
 * const x = tf.tensor2d([[ 0,  1,  2, 3],
 *                        [-1,  0,  1, 2],
 *                        [-2, -1,  0, 1],
 *                        [-3, -2, -1, 0]]);
 * let y = tf.linalg.bandPart(x, 1, -1);
 * y.print(); // [[ 0,  1,  2, 3],
 *            //  [-1,  0,  1, 2],
 *            //  [ 0, -1,  0, 1],
 *            //  [ 0, 0 , -1, 0]]
 * let z = tf.linalg.bandPart(x, 2, 1);
 * z.print(); // [[ 0,  1,  0, 0],
 *            //  [-1,  0,  1, 0],
 *            //  [-2, -1,  0, 1],
 *            //  [ 0, -2, -1, 0]]
 * ```
 *
 * @param x Rank `k` tensor
 * @param numLower Number of subdiagonals to keep.
 *   If negative, keep entire lower triangle.
 * @param numUpper Number of subdiagonals to keep.
 *   If negative, keep entire upper triangle.
 * @returns Rank `k` tensor of the same shape as input.
 *   The extracted banded tensor.
 *
 * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}
 */ function bandPart_(a, numLower, numUpper) {
    const $a = (0, _tensorUtilEnv.convertToTensor)(a, "a", "bandPart");
    (0, _util.assert)($a.rank >= 2, ()=>`bandPart(): Rank must be at least 2, got ${$a.rank}.`);
    const shape = $a.shape;
    const [M, N] = $a.shape.slice(-2);
    let $numLower;
    let $numUpper;
    if (typeof numLower === "number") {
        (0, _util.assert)(numLower % 1 === 0, ()=>`bandPart(): numLower must be an integer, got ${numLower}.`);
        (0, _util.assert)(numLower <= M, ()=>`bandPart(): numLower (${numLower})` + ` must not be greater than the number of rows (${M}).`);
        $numLower = (0, _tensorUtilEnv.convertToTensor)(numLower < 0 ? M : numLower, "numLower", "bandPart");
    } else {
        (0, _util.assert)(numLower.dtype === "int32", ()=>`bandPart(): numLower's dtype must be an int32.`);
        // If numLower is a Scalar, checking `numLower <= M` could hurt performance,
        // but minimum(numLower, M) could avoid unexpected results.
        $numLower = (0, _where.where)((0, _less.less)(numLower, 0), M, (0, _minimum.minimum)(numLower, M));
    }
    if (typeof numUpper === "number") {
        (0, _util.assert)(numUpper % 1 === 0, ()=>`bandPart(): numUpper must be an integer, got ${numUpper}.`);
        (0, _util.assert)(numUpper <= N, ()=>`bandPart(): numUpper (${numUpper})` + ` must not be greater than the number of columns (${N}).`);
        $numUpper = (0, _tensorUtilEnv.convertToTensor)(numUpper < 0 ? N : numUpper, "numUpper", "bandPart");
    } else {
        (0, _util.assert)(numUpper.dtype === "int32", ()=>`bandPart(): numUpper's dtype must be an int32.`);
        $numUpper = (0, _where.where)((0, _less.less)(numUpper, 0), N, (0, _minimum.minimum)(numUpper, N));
    }
    const i = (0, _reshape.reshape)((0, _range.range)(0, M, 1, "int32"), [
        -1,
        1
    ]);
    const j = (0, _range.range)(0, N, 1, "int32");
    const ij = (0, _sub.sub)(i, j);
    const inBand = (0, _logicalAnd.logicalAnd)((0, _lessEqual.lessEqual)(ij, $numLower), (0, _greaterEqual.greaterEqual)(ij, (0, _neg.neg)($numUpper)));
    const zero = (0, _zeros.zeros)([
        M,
        N
    ], $a.dtype);
    return (0, _reshape.reshape)((0, _stack.stack)((0, _unstack.unstack)((0, _reshape.reshape)($a, [
        -1,
        M,
        N
    ])).map((mat)=>(0, _where.where)(inBand, mat, zero))), shape);
}
const bandPart = /* @__PURE__ */ (0, _operation.op)({
    bandPart_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../greater_equal":"f7S2c","../less":"hCGN5","../less_equal":"j9Jhy","../logical_and":"6zYjJ","../minimum":"guwat","../neg":"3jQfI","../operation":"ip64W","../range":"3seHd","../reshape":"ew3wZ","../stack":"jeh7M","../sub":"jBBQ2","../unstack":"90Kbb","../where":"47cKc","../zeros":"fcPHR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dmmpK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gramSchmidt", ()=>gramSchmidt);
var _engine = require("../../engine");
var _util = require("../../util");
var _div = require("../div");
var _mul = require("../mul");
var _norm = require("../norm");
var _operation = require("../operation");
var _split = require("../split");
var _squeeze = require("../squeeze");
var _stack = require("../stack");
var _sub = require("../sub");
var _sum = require("../sum");
/**
 * Gram-Schmidt orthogonalization.
 *
 * ```js
 * const x = tf.tensor2d([[1, 2], [3, 4]]);
 * let y = tf.linalg.gramSchmidt(x);
 * y.print();
 * console.log('Orthogonalized:');
 * y.dot(y.transpose()).print();  // should be nearly the identity matrix.
 * console.log('First row direction maintained:');
 * const data = await y.array();
 * console.log(data[0][1] / data[0][0]);  // should be nearly 2.
 * ```
 *
 * @param xs The vectors to be orthogonalized, in one of the two following
 *   formats:
 *   - An Array of `tf.Tensor1D`.
 *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows
 *     of `xs`.
 *   In each case, all the vectors must have the same length and the length
 *   must be greater than or equal to the number of vectors.
 * @returns The orthogonalized and normalized vectors or matrix.
 *   Orthogonalization means that the vectors or the rows of the matrix
 *   are orthogonal (zero inner products). Normalization means that each
 *   vector or each row of the matrix has an L2 norm that equals `1`.
 *
 * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}
 */ function gramSchmidt_(xs) {
    let inputIsTensor2D;
    if (Array.isArray(xs)) {
        inputIsTensor2D = false;
        (0, _util.assert)(xs != null && xs.length > 0, ()=>"Gram-Schmidt process: input must not be null, undefined, or empty");
        const dim = xs[0].shape[0];
        for(let i = 1; i < xs.length; ++i)(0, _util.assert)(xs[i].shape[0] === dim, ()=>"Gram-Schmidt: Non-unique lengths found in the input vectors: " + `(${xs[i].shape[0]} vs. ${dim})`);
    } else {
        inputIsTensor2D = true;
        xs = (0, _split.split)(xs, xs.shape[0], 0).map((x)=>(0, _squeeze.squeeze)(x, [
                0
            ]));
    }
    (0, _util.assert)(xs.length <= xs[0].shape[0], ()=>`Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` + `number of dimensions (${xs[0].shape[0]}).`);
    const ys = [];
    const xs1d = xs;
    for(let i = 0; i < xs.length; ++i)ys.push((0, _engine.ENGINE).tidy(()=>{
        let x = xs1d[i];
        if (i > 0) for(let j = 0; j < i; ++j){
            const proj = (0, _mul.mul)((0, _sum.sum)((0, _mul.mul)(ys[j], x)), ys[j]);
            x = (0, _sub.sub)(x, proj);
        }
        return (0, _div.div)(x, (0, _norm.norm)(x, "euclidean"));
    }));
    if (inputIsTensor2D) return (0, _stack.stack)(ys, 0);
    else return ys;
}
const gramSchmidt = /* @__PURE__ */ (0, _operation.op)({
    gramSchmidt_
});

},{"../../engine":"1g2qo","../../util":"ctavo","../div":"5ZT1h","../mul":"fOWrC","../norm":"eOnCA","../operation":"ip64W","../split":"l0HF2","../squeeze":"VbY6Z","../stack":"jeh7M","../sub":"jBBQ2","../sum":"5awd0","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dhxAc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "qr", ()=>qr);
var _engine = require("../../engine");
var _globals = require("../../globals");
var _util = require("../../util");
var _clone = require("../clone");
var _concat = require("../concat");
var _div = require("../div");
var _eye = require("../eye");
var _greater = require("../greater");
var _matMul = require("../mat_mul");
var _mul = require("../mul");
var _neg = require("../neg");
var _norm = require("../norm");
var _operation = require("../operation");
var _reshape = require("../reshape");
var _slice = require("../slice");
var _stack = require("../stack");
var _sub = require("../sub");
var _tensor2D = require("../tensor2d");
var _transpose = require("../transpose");
var _unstack = require("../unstack");
var _where = require("../where");
/**
 * Compute QR decomposition of m-by-n matrix using Householder transformation.
 *
 * Implementation based on
 *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]
 * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)
 *
 * ```js
 * const a = tf.tensor2d([[1, 2], [3, 4]]);
 * let [q, r] = tf.linalg.qr(a);
 * console.log('Q');
 * q.print();
 * console.log('R');
 * r.print();
 * console.log('Orthogonalized');
 * q.dot(q.transpose()).print()  // should be nearly the identity matrix.
 * console.log('Reconstructed');
 * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];
 * ```
 *
 * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose
 *   it has the shape `[..., M, N]`.
 * @param fullMatrices An optional boolean parameter. Defaults to `false`.
 *   If `true`, compute full-sized `Q`. If `false` (the default),
 *   compute only the leading N columns of `Q` and `R`.
 * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,
 *   i.e., its columns all have unit norm and are mutually orthogonal.
 *   If `M >= N`,
 *     If `fullMatrices` is `false` (default),
 *       - `Q` has a shape of `[..., M, N]`,
 *       - `R` has a shape of `[..., N, N]`.
 *     If `fullMatrices` is `true` (default),
 *       - `Q` has a shape of `[..., M, M]`,
 *       - `R` has a shape of `[..., M, N]`.
 *   If `M < N`,
 *     - `Q` has a shape of `[..., M, M]`,
 *     - `R` has a shape of `[..., M, N]`.
 * @throws If the rank of `x` is less than 2.
 *
 * @doc {heading:'Operations',
 *       subheading:'Linear Algebra',
 *       namespace:'linalg'}
 */ function qr_(x, fullMatrices = false) {
    (0, _util.assert)(x.rank >= 2, ()=>`qr() requires input tensor to have a rank >= 2, but got rank ${x.rank}`);
    if (x.rank === 2) return qr2d(x, fullMatrices);
    else {
        // Rank > 2.
        // TODO(cais): Below we split the input into individual 2D tensors,
        //   perform QR decomposition on them and then stack the results back
        //   together. We should explore whether this can be parallelized.
        const outerDimsProd = x.shape.slice(0, x.shape.length - 2).reduce((value, prev)=>value * prev);
        const x2ds = (0, _unstack.unstack)((0, _reshape.reshape)(x, [
            outerDimsProd,
            x.shape[x.shape.length - 2],
            x.shape[x.shape.length - 1]
        ]), 0);
        const q2ds = [];
        const r2ds = [];
        x2ds.forEach((x2d)=>{
            const [q2d, r2d] = qr2d(x2d, fullMatrices);
            q2ds.push(q2d);
            r2ds.push(r2d);
        });
        const q = (0, _reshape.reshape)((0, _stack.stack)(q2ds, 0), x.shape);
        const r = (0, _reshape.reshape)((0, _stack.stack)(r2ds, 0), x.shape);
        return [
            q,
            r
        ];
    }
}
function qr2d(x, fullMatrices = false) {
    return (0, _engine.ENGINE).tidy(()=>{
        (0, _util.assert)(x.shape.length === 2, ()=>`qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);
        const m = x.shape[0];
        const n = x.shape[1];
        let q = (0, _eye.eye)(m); // Orthogonal transform so far.
        let r = (0, _clone.clone)(x); // Transformed matrix so far.
        const one2D = (0, _tensor2D.tensor2d)([
            [
                1
            ]
        ], [
            1,
            1
        ]);
        let w = (0, _clone.clone)(one2D);
        const iters = m >= n ? n : m;
        for(let j = 0; j < iters; ++j){
            // This tidy within the for-loop ensures we clean up temporary
            // tensors as soon as they are no longer needed.
            const rTemp = r;
            const wTemp = w;
            const qTemp = q;
            [w, r, q] = (0, _engine.ENGINE).tidy(()=>{
                // Find H = I - tau * w * w', to put zeros below R(j, j).
                const rjEnd1 = (0, _slice.slice)(r, [
                    j,
                    j
                ], [
                    m - j,
                    1
                ]);
                const normX = (0, _norm.norm)(rjEnd1);
                const rjj = (0, _slice.slice)(r, [
                    j,
                    j
                ], [
                    1,
                    1
                ]);
                // The sign() function returns 0 on 0, which causes division by zero.
                const s = (0, _where.where)((0, _greater.greater)(rjj, 0), (0, _tensor2D.tensor2d)([
                    [
                        -1
                    ]
                ]), (0, _tensor2D.tensor2d)([
                    [
                        1
                    ]
                ]));
                const u1 = (0, _sub.sub)(rjj, (0, _mul.mul)(s, normX));
                const wPre = (0, _div.div)(rjEnd1, u1);
                if (wPre.shape[0] === 1) w = (0, _clone.clone)(one2D);
                else w = (0, _concat.concat)([
                    one2D,
                    (0, _slice.slice)(wPre, [
                        1,
                        0
                    ], [
                        wPre.shape[0] - 1,
                        wPre.shape[1]
                    ])
                ], 0);
                const tau = (0, _neg.neg)((0, _div.div)((0, _matMul.matMul)(s, u1), normX));
                // -- R := HR, Q := QH.
                const rjEndAll = (0, _slice.slice)(r, [
                    j,
                    0
                ], [
                    m - j,
                    n
                ]);
                const tauTimesW = (0, _mul.mul)(tau, w);
                const wT = (0, _transpose.transpose)(w);
                if (j === 0) r = (0, _sub.sub)(rjEndAll, (0, _matMul.matMul)(tauTimesW, (0, _matMul.matMul)(wT, rjEndAll)));
                else {
                    const rTimesTau = (0, _sub.sub)(rjEndAll, (0, _matMul.matMul)(tauTimesW, (0, _matMul.matMul)(wT, rjEndAll)));
                    r = (0, _concat.concat)([
                        (0, _slice.slice)(r, [
                            0,
                            0
                        ], [
                            j,
                            n
                        ]),
                        rTimesTau
                    ], 0);
                }
                const tawTimesWT = (0, _transpose.transpose)(tauTimesW);
                const qAllJEnd = (0, _slice.slice)(q, [
                    0,
                    j
                ], [
                    m,
                    q.shape[1] - j
                ]);
                if (j === 0) q = (0, _sub.sub)(qAllJEnd, (0, _matMul.matMul)((0, _matMul.matMul)(qAllJEnd, w), tawTimesWT));
                else {
                    const qTimesTau = (0, _sub.sub)(qAllJEnd, (0, _matMul.matMul)((0, _matMul.matMul)(qAllJEnd, w), tawTimesWT));
                    q = (0, _concat.concat)([
                        (0, _slice.slice)(q, [
                            0,
                            0
                        ], [
                            m,
                            j
                        ]),
                        qTimesTau
                    ], 1);
                }
                return [
                    w,
                    r,
                    q
                ];
            });
            (0, _globals.dispose)([
                rTemp,
                wTemp,
                qTemp
            ]);
        }
        if (!fullMatrices && m > n) {
            q = (0, _slice.slice)(q, [
                0,
                0
            ], [
                m,
                n
            ]);
            r = (0, _slice.slice)(r, [
                0,
                0
            ], [
                n,
                n
            ]);
        }
        return [
            q,
            r
        ];
    });
}
const qr = /* @__PURE__ */ (0, _operation.op)({
    qr_
});

},{"../../engine":"1g2qo","../../globals":"eO1Ja","../../util":"ctavo","../clone":"jmQ7S","../concat":"1NOyL","../div":"5ZT1h","../eye":"hDQ1U","../greater":"3Nh8O","../mat_mul":"fiMp4","../mul":"fOWrC","../neg":"3jQfI","../norm":"eOnCA","../operation":"ip64W","../reshape":"ew3wZ","../slice":"dr7Gq","../stack":"jeh7M","../sub":"jBBQ2","../tensor2d":"faX5H","../transpose":"2bqbS","../unstack":"90Kbb","../where":"47cKc","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5sJon":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "absoluteDifference", ()=>absoluteDifference);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _abs = require("../abs");
var _lossOpsUtils = require("../loss_ops_utils");
var _operation = require("../operation");
var _sub = require("../sub");
var _computeWeightedLoss = require("./compute_weighted_loss");
/**
 * Computes the absolute difference loss between two tensors.
 *
 * @param labels The ground truth output tensor, same dimensions as
 *    'predictions'.
 * @param predictions The predicted outputs.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
 *    must be either `1`, or the same as the corresponding `losses`
 *    dimension).
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */ function absoluteDifference_(labels, predictions, weights, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    const $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "absoluteDifference");
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "absoluteDifference");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "absoluteDifference");
    (0, _util.assertShapesMatch)($labels.shape, $predictions.shape, "Error in absoluteDifference: ");
    const losses = (0, _abs.abs)((0, _sub.sub)($labels, $predictions));
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const absoluteDifference = /* @__PURE__ */ (0, _operation.op)({
    absoluteDifference_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../abs":"iZQ4e","../loss_ops_utils":"6wY9M","../operation":"ip64W","../sub":"jBBQ2","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6wY9M":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Reduction", ()=>Reduction);
var Reduction;
(function(Reduction) {
    Reduction[Reduction["NONE"] = 0] = "NONE";
    Reduction[Reduction["MEAN"] = 1] = "MEAN";
    Reduction[Reduction["SUM"] = 2] = "SUM";
    Reduction[Reduction["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
})(Reduction || (Reduction = {}));

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1agzr":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "computeWeightedLoss", ()=>computeWeightedLoss);
var _tensorUtilEnv = require("../../tensor_util_env");
var _cast = require("../cast");
var _div = require("../div");
var _lossOpsUtils = require("../loss_ops_utils");
var _mean = require("../mean");
var _mul = require("../mul");
var _notEqual = require("../not_equal");
var _ones = require("../ones");
var _operation = require("../operation");
var _scalar = require("../scalar");
var _sum = require("../sum");
/**
 * Computes the weighted loss between two tensors.
 *
 * @param losses Tensor of shape `[batch_size, d1, ..., dN]`.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `losses`, and must be broadcastable to `losses` (i.e., all
 *    dimensions must be either `1`, or the same as the corresponding
 *    `losses` dimension).
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */ function computeWeightedLoss_(losses, weights, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    const $losses = (0, _tensorUtilEnv.convertToTensor)(losses, "losses", "computeWeightedLoss");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "computeWeightedLoss");
    const weightedLoss = $weights == null ? $losses : (0, _mul.mul)($losses, $weights);
    if (reduction === (0, _lossOpsUtils.Reduction).NONE) return weightedLoss;
    if (reduction === (0, _lossOpsUtils.Reduction).SUM) return (0, _sum.sum)(weightedLoss);
    if (reduction === (0, _lossOpsUtils.Reduction).MEAN) {
        if ($weights == null) return (0, _mean.mean)(weightedLoss);
        else {
            const broadcastFactor = $losses.size / $weights.size;
            const result = (0, _div.div)((0, _sum.sum)(weightedLoss), (0, _sum.sum)($weights));
            return broadcastFactor > 1 ? (0, _div.div)(result, (0, _scalar.scalar)(broadcastFactor)) : result;
        }
    }
    if (reduction === (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
        if ($weights == null) return (0, _div.div)((0, _sum.sum)(weightedLoss), (0, _scalar.scalar)($losses.size));
        else {
            const broadcastedWeights = (0, _mul.mul)($weights, (0, _ones.ones)($losses.shape));
            const numNonZeros = (0, _cast.cast)((0, _sum.sum)((0, _notEqual.notEqual)(broadcastedWeights, (0, _scalar.scalar)(0))), "float32");
            return (0, _div.div)((0, _sum.sum)(weightedLoss), numNonZeros);
        }
    }
    throw Error(`Unknown reduction: ${reduction}`);
}
const computeWeightedLoss = /* @__PURE__ */ (0, _operation.op)({
    computeWeightedLoss_
});

},{"../../tensor_util_env":"6vfHI","../cast":"b4VDA","../div":"5ZT1h","../loss_ops_utils":"6wY9M","../mean":"hzzLs","../mul":"fOWrC","../not_equal":"gUKb0","../ones":"2gdnE","../operation":"ip64W","../scalar":"7lhC8","../sum":"5awd0","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"enn6x":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cosineDistance", ()=>cosineDistance);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _lossOpsUtils = require("../loss_ops_utils");
var _mul = require("../mul");
var _operation = require("../operation");
var _scalar = require("../scalar");
var _sub = require("../sub");
var _sum = require("../sum");
var _computeWeightedLoss = require("./compute_weighted_loss");
/**
 * Computes the cosine distance loss between two tensors.
 *
 * @param labels The ground truth output tensor, same dimensions as
 *    'predictions'.
 * @param predictions The predicted outputs.
 * @param axis The dimension along which the cosine distance is computed.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
 *    must be either `1`, or the same as the corresponding `losses`
 *    dimension).
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */ function cosineDistance_(labels, predictions, axis, weights, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    const $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "cosineDistance");
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "cosineDistance");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "cosineDistance");
    (0, _util.assertShapesMatch)($labels.shape, $predictions.shape, "Error in cosineDistance: ");
    const one = (0, _scalar.scalar)(1);
    const losses = (0, _sub.sub)(one, (0, _sum.sum)((0, _mul.mul)($labels, $predictions), axis, true));
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const cosineDistance = /* @__PURE__ */ (0, _operation.op)({
    cosineDistance_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../loss_ops_utils":"6wY9M","../mul":"fOWrC","../operation":"ip64W","../scalar":"7lhC8","../sub":"jBBQ2","../sum":"5awd0","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1aaXc":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "hingeLoss", ()=>hingeLoss);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _lossOpsUtils = require("../loss_ops_utils");
var _mul = require("../mul");
var _operation = require("../operation");
var _relu = require("../relu");
var _scalar = require("../scalar");
var _sub = require("../sub");
var _computeWeightedLoss = require("./compute_weighted_loss");
/**
 * Computes the Hinge loss between two tensors.
 *
 * @param labels The ground truth output tensor, same dimensions as
 *    'predictions'.
 * @param predictions The predicted outputs.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
 *    must be either `1`, or the same as the corresponding `losses`
 *    dimension).
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */ function hingeLoss_(labels, predictions, weights, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    let $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "hingeLoss");
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "hingeLoss");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "hingeLoss");
    (0, _util.assertShapesMatch)($labels.shape, $predictions.shape, "Error in hingeLoss: ");
    const one = (0, _scalar.scalar)(1);
    // Convert binary labels to (-1, 1)
    $labels = (0, _sub.sub)((0, _mul.mul)((0, _scalar.scalar)(2), $labels), one);
    const losses = (0, _relu.relu)((0, _sub.sub)(one, (0, _mul.mul)($labels, $predictions)));
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const hingeLoss = /* @__PURE__ */ (0, _operation.op)({
    hingeLoss_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../loss_ops_utils":"6wY9M","../mul":"fOWrC","../operation":"ip64W","../relu":"lcv3E","../scalar":"7lhC8","../sub":"jBBQ2","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1c06w":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "huberLoss", ()=>huberLoss);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _abs = require("../abs");
var _add = require("../add");
var _lossOpsUtils = require("../loss_ops_utils");
var _minimum = require("../minimum");
var _mul = require("../mul");
var _operation = require("../operation");
var _scalar = require("../scalar");
var _square = require("../square");
var _sub = require("../sub");
var _computeWeightedLoss = require("./compute_weighted_loss");
/**
 * Computes the Huber loss between two tensors.
 *
 * @param labels The ground truth output tensor, same dimensions as
 *    'predictions'.
 * @param predictions The predicted outputs.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
 *    must be either `1`, or the same as the corresponding `losses`
 *    dimension).
 * @param delta Point where Huber loss changes from quadratic to linear.
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`.
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */ function huberLoss_(labels, predictions, weights, delta = 1.0, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    const $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "huberLoss");
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "huberLoss");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "huberLoss");
    (0, _util.assertShapesMatch)($labels.shape, $predictions.shape, "Error in huberLoss: ");
    const deltaScalar = (0, _scalar.scalar)(delta);
    const error = (0, _abs.abs)((0, _sub.sub)($predictions, $labels));
    const quadratic = (0, _minimum.minimum)(error, deltaScalar);
    const linear = (0, _sub.sub)(error, quadratic);
    const losses = (0, _add.add)((0, _mul.mul)((0, _scalar.scalar)(0.5), (0, _square.square)(quadratic)), (0, _mul.mul)(deltaScalar, linear));
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const huberLoss = /* @__PURE__ */ (0, _operation.op)({
    huberLoss_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../abs":"iZQ4e","../add":"aynh7","../loss_ops_utils":"6wY9M","../minimum":"guwat","../mul":"fOWrC","../operation":"ip64W","../scalar":"7lhC8","../square":"cOosW","../sub":"jBBQ2","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"UdKNC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logLoss", ()=>logLoss);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _add = require("../add");
var _log = require("../log");
var _lossOpsUtils = require("../loss_ops_utils");
var _mul = require("../mul");
var _neg = require("../neg");
var _operation = require("../operation");
var _scalar = require("../scalar");
var _sub = require("../sub");
var _computeWeightedLoss = require("./compute_weighted_loss");
/**
 * Computes the log loss between two tensors.
 *
 * @param labels The ground truth output tensor, same dimensions as
 *    'predictions'.
 * @param predictions The predicted outputs.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
 *    must be either `1`, or the same as the corresponding `losses`
 *    dimension).
 * @param epsilon A small increment to avoid taking log of zero
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */ function logLoss_(labels, predictions, weights, epsilon = 1e-7, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    const $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "logLoss");
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "logLoss");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "logLoss");
    (0, _util.assertShapesMatch)($labels.shape, $predictions.shape, "Error in logLoss: ");
    const one = (0, _scalar.scalar)(1);
    const epsilonScalar = (0, _scalar.scalar)(epsilon);
    const l1 = (0, _neg.neg)((0, _mul.mul)($labels, (0, _log.log)((0, _add.add)($predictions, epsilonScalar))));
    const l2 = (0, _mul.mul)((0, _sub.sub)(one, $labels), (0, _log.log)((0, _add.add)((0, _sub.sub)(one, $predictions), epsilonScalar)));
    const losses = (0, _sub.sub)(l1, l2);
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const logLoss = /* @__PURE__ */ (0, _operation.op)({
    logLoss_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../add":"aynh7","../log":"iex5O","../loss_ops_utils":"6wY9M","../mul":"fOWrC","../neg":"3jQfI","../operation":"ip64W","../scalar":"7lhC8","../sub":"jBBQ2","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8Mugj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "meanSquaredError", ()=>meanSquaredError);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _lossOpsUtils = require("../loss_ops_utils");
var _operation = require("../operation");
var _squaredDifference = require("../squared_difference");
var _computeWeightedLoss = require("./compute_weighted_loss");
/**
 * Computes the mean squared error between two tensors.
 *
 * @param labels The ground truth output tensor, same dimensions as
 *    'predictions'.
 * @param predictions The predicted outputs.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
 *    must be either `1`, or the same as the corresponding `losses`
 *    dimension).
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */ function meanSquaredError_(labels, predictions, weights, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    const $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "meanSquaredError");
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "meanSquaredError");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "meanSquaredError");
    (0, _util.assertShapesMatch)($labels.shape, $predictions.shape, "Error in meanSquaredError: ");
    const losses = (0, _squaredDifference.squaredDifference)($labels, $predictions);
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const meanSquaredError = /* @__PURE__ */ (0, _operation.op)({
    meanSquaredError_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../loss_ops_utils":"6wY9M","../operation":"ip64W","../squared_difference":"iHBgt","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"77Q3C":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sigmoidCrossEntropy", ()=>sigmoidCrossEntropy);
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _abs = require("../abs");
var _add = require("../add");
var _exp = require("../exp");
var _log1P = require("../log1p");
var _lossOpsUtils = require("../loss_ops_utils");
var _mul = require("../mul");
var _neg = require("../neg");
var _operation = require("../operation");
var _relu = require("../relu");
var _scalar = require("../scalar");
var _sub = require("../sub");
var _computeWeightedLoss = require("./compute_weighted_loss");
function sigmoidCrossEntropyWithLogits_(labels, logits) {
    const $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "sigmoidCrossEntropyWithLogits");
    const $logits = (0, _tensorUtilEnv.convertToTensor)(logits, "logits", "sigmoidCrossEntropyWithLogits");
    (0, _util.assertShapesMatch)($labels.shape, $logits.shape, "Error in sigmoidCrossEntropyWithLogits: ");
    /**
     * Implementation Details:
     *
     * For brevity, let `x = logits`, `z = labels`.  The logistic loss is
     *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
     *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
     *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
     *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
     *   = (1 - z) * x + log(1 + exp(-x))
     *   = x - x * z + log(1 + exp(-x))
     *
     *   For x < 0, to avoid overflow in exp(-x), we reformulate the above
     *     x - x * z + log(1 + exp(-x))
     *   = log(exp(x)) - x * z + log(1 + exp(-x))
     *   = - x * z + log(1 + exp(x))
     *
     * Hence, to ensure stability and avoid overflow, the implementation uses
     * this equivalent formulation:
     *     max(x, 0) - x * z + log(1 + exp(-abs(x)))
     */ const maxOutput = (0, _relu.relu)($logits);
    const outputXTarget = (0, _mul.mul)($logits, $labels);
    const sigmoidOutput = (0, _log1P.log1p)((0, _exp.exp)((0, _neg.neg)((0, _abs.abs)($logits))));
    return (0, _add.add)((0, _sub.sub)(maxOutput, outputXTarget), sigmoidOutput);
}
/**
 * Computes the sigmoid cross entropy loss between two tensors.
 *
 * If labelSmoothing is nonzero, smooth the labels towards 1/2:
 *
 *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)
 *                         + 0.5 * labelSmoothing
 *
 * @param multiClassLabels The ground truth output tensor of shape
 * [batch_size, num_classes], same dimensions as 'predictions'.
 * @param logits The predicted outputs.
 * @param weights Tensor whose rank is either 0, or the same rank as
 *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions
 *    must be either `1`, or the same as the corresponding `losses`
 *    dimension).
 * @param labelSmoothing If greater than 0, then smooth the labels.
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
 */ function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing = 0, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    let $multiClassLabels = (0, _tensorUtilEnv.convertToTensor)(multiClassLabels, "multiClassLabels", "sigmoidCrossEntropy");
    const $logits = (0, _tensorUtilEnv.convertToTensor)(logits, "logits", "sigmoidCrossEntropy");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "sigmoidCrossEntropy");
    (0, _util.assertShapesMatch)($multiClassLabels.shape, $logits.shape, "Error in sigmoidCrossEntropy: ");
    if (labelSmoothing > 0) {
        const labelSmoothingScalar = (0, _scalar.scalar)(labelSmoothing);
        const one = (0, _scalar.scalar)(1);
        const half = (0, _scalar.scalar)(0.5);
        $multiClassLabels = (0, _add.add)((0, _mul.mul)($multiClassLabels, (0, _sub.sub)(one, labelSmoothingScalar)), (0, _mul.mul)(half, labelSmoothingScalar));
    }
    const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const sigmoidCrossEntropy = /* @__PURE__ */ (0, _operation.op)({
    sigmoidCrossEntropy_
});

},{"../../tensor_util_env":"6vfHI","../../util":"ctavo","../abs":"iZQ4e","../add":"aynh7","../exp":"1CsGu","../log1p":"jKhH4","../loss_ops_utils":"6wY9M","../mul":"fOWrC","../neg":"3jQfI","../operation":"ip64W","../relu":"lcv3E","../scalar":"7lhC8","../sub":"jBBQ2","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cGUog":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "softmaxCrossEntropy", ()=>softmaxCrossEntropy);
var _gradients = require("../../gradients");
var _tensorUtilEnv = require("../../tensor_util_env");
var _util = require("../../util");
var _add = require("../add");
var _axisUtil = require("../axis_util");
var _cast = require("../cast");
var _div = require("../div");
var _exp = require("../exp");
var _logSumExp = require("../log_sum_exp");
var _lossOpsUtils = require("../loss_ops_utils");
var _mul = require("../mul");
var _neg = require("../neg");
var _operation = require("../operation");
var _reshape = require("../reshape");
var _scalar = require("../scalar");
var _sub = require("../sub");
var _sum = require("../sum");
var _computeWeightedLoss = require("./compute_weighted_loss");
/**
 * Computes softmax cross entropy between logits and labels.
 *
 * Measures the probability error in discrete classification tasks in which
 * the classes are mutually exclusive (each entry is in exactly one class).
 * For example, each CIFAR-10 image is labeled with one and only one label: an
 * image can be a dog or a truck, but not both.
 *
 * `NOTE`: While the classes are mutually exclusive, their probabilities need
 * not be. All that is required is that each row of labels is a valid
 * probability distribution. If they are not, the computation of the gradient
 * will be incorrect.
 *
 * `WARNING`: This op expects unscaled logits, since it performs a softmax on
 * logits internally for efficiency. Do not call this op with the output of
 * softmax, as it will produce incorrect results.
 *
 * logits and labels must have the same shape, e.g. [batch_size, num_classes]
 * and the same dtype.
 * @param labels The labels array.
 * @param logits The logits array.
 * @param dim The dimension softmax would be performed on. Defaults to `-1`
 *     which indicates the last dimension.
 */ function softmaxCrossEntropyWithLogits_(labels, logits, dim = -1) {
    if (dim === -1) dim = logits.rank - 1;
    if (dim !== logits.rank - 1) throw Error(`Softmax cross entropy along a non-last dimension is not yet ` + `supported. Labels / logits was rank ${logits.rank} ` + `and dim was ${dim}`);
    // Use a custom gradient for numerical stability.
    const customOp = (0, _gradients.customGrad)((labels, logits, save)=>{
        // Reference:
        //   1. http://cs231n.github.io/linear-classify/#softmax
        //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/
        const keepDims = true;
        const lse = (0, _logSumExp.logSumExp)(logits, [
            dim
        ], keepDims);
        const logResult = (0, _sub.sub)((0, _cast.cast)(logits, "float32"), lse);
        save([
            labels,
            logResult
        ]);
        const costVector = (0, _neg.neg)((0, _mul.mul)(logResult, labels));
        const value = (0, _sum.sum)(costVector, [
            dim
        ]);
        const gradFunc = (dy, saved)=>{
            const [labels, logResult] = saved;
            const dyShape = (0, _axisUtil.expandShapeToKeepDim)(dy.shape, [
                dim
            ]);
            return [
                (0, _mul.mul)((0, _reshape.reshape)(dy, dyShape), (0, _sub.sub)((0, _cast.cast)(labels, "float32"), (0, _exp.exp)(logResult))),
                (0, _mul.mul)((0, _reshape.reshape)(dy, dyShape), (0, _sub.sub)((0, _exp.exp)(logResult), (0, _cast.cast)(labels, "float32")))
            ];
        };
        return {
            value,
            gradFunc
        };
    });
    return customOp(labels, logits);
}
/**
 * Computes the softmax cross entropy loss between two tensors.
 *
 * If labelSmoothing is nonzero, smooth the labels towards 1/2:
 *
 *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)
 *                         + labelSmoothing / numClasses
 *
 * @param onehotLabels One hot encoded labels
 *    [batch_size, num_classes], same dimensions as 'predictions'.
 * @param logits The predicted outputs.
 * @param weights Tensor whose rank is either 0, or 1, and must be
 *    broadcastable to `loss`  of shape [batch_size]
 * @param labelSmoothing If greater than 0, then smooth the labels.
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
 */ function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing = 0, reduction = (0, _lossOpsUtils.Reduction).SUM_BY_NONZERO_WEIGHTS) {
    let $onehotLabels = (0, _tensorUtilEnv.convertToTensor)(onehotLabels, "onehotLabels", "softmaxCrossEntropy");
    const $logits = (0, _tensorUtilEnv.convertToTensor)(logits, "logits", "softmaxCrossEntropy");
    let $weights = null;
    if (weights != null) $weights = (0, _tensorUtilEnv.convertToTensor)(weights, "weights", "softmaxCrossEntropy");
    (0, _util.assertShapesMatch)($onehotLabels.shape, $logits.shape, "Error in softmaxCrossEntropy: ");
    if (labelSmoothing > 0) {
        const labelSmoothingScalar = (0, _scalar.scalar)(labelSmoothing);
        const one = (0, _scalar.scalar)(1);
        const numClasses = (0, _scalar.scalar)($onehotLabels.shape[1]);
        $onehotLabels = (0, _add.add)((0, _mul.mul)($onehotLabels, (0, _sub.sub)(one, labelSmoothingScalar)), (0, _div.div)(labelSmoothingScalar, numClasses));
    }
    const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
    return (0, _computeWeightedLoss.computeWeightedLoss)(losses, $weights, reduction);
}
const softmaxCrossEntropy = /* @__PURE__ */ (0, _operation.op)({
    softmaxCrossEntropy_
});

},{"../../gradients":"dDHpg","../../tensor_util_env":"6vfHI","../../util":"ctavo","../add":"aynh7","../axis_util":"hzS4B","../cast":"b4VDA","../div":"5ZT1h","../exp":"1CsGu","../log_sum_exp":"feRCM","../loss_ops_utils":"6wY9M","../mul":"fOWrC","../neg":"3jQfI","../operation":"ip64W","../reshape":"ew3wZ","../scalar":"7lhC8","../sub":"jBBQ2","../sum":"5awd0","./compute_weighted_loss":"1agzr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1MyfF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseFillEmptyRows", ()=>sparseFillEmptyRows);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * The input SparseTensor is represented via the map of inputs {`indices`,
 * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`
 * but with indices `outputIndices` and values `outputValues`. This op inserts a
 * single entry for every row that doesn't have any values. The index is created
 * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.
 *
 * For example, suppose `spInput` has shape [5, 6] and non-empty values:
 * [0, 1]: a
 * [0, 3]: b
 * [2, 0]: c
 * [3, 1]: d
 *
 * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:
 * [0, 1]: a
 * [0, 3]: b
 * [1, 0]: `defaultValue`
 * [2, 0]: c
 * [3, 1]: d
 * [4, 0]: `defaultValue`
 *
 * The output SparseTensor will be in row-major order and will have the same
 * shape as the input.
 *
 * This op also returns an indicator vector shaped [dense_shape[0]] such that
 * emptyRowIndicator[i] = True iff row i was an empty row.
 *
 * And a reverse index map vector shaped [indices.shape[0]] that is used during
 * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==
 * outputIndices[outi, j] for all j
 *
 * ```js
 * const result = tf.sparse.sparseFillEmptyRows(
 *   [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],
 *   [0, 10, 13, 14, 32, 33], [5, 6], -1);
 * console.log(result);
 * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],
 *                                  //  [2, 0], [3, 2], [3, 3], [4, 0]]
 * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]
 * result['emptyRowIndicator'].print(); // [false, false, true, false, true]
 * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]
 * ```
 * @param indices: 2-D. The indices of the sparse tensor.
 * @param values: 1-D. The values of the sparse tensor.
 * @param denseShape: 1-D. The shape of the sparse tensor.
 * @param defaultValue: 0-D. Default value to insert into location [row, 0, ...,
 *     0] for rows missing from the input sparse tensor.
 * @return A map with the following properties:
 *     - outputIndices
 *     - outputValues: 1-D. The values of the filled sparse tensor.
 *     - emptyRowIndicator: 1-D. Whether the dense row was missing in the input
 * sparse tensor.
 *     - reverseIndexMap: 1-D. A map from the input indices to the output
 * indices.
 * @doc {heading: 'Operations', subheading: 'Sparse'}
 */ function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "sparseFillEmptyRows", "int32");
    const $values = (0, _tensorUtilEnv.convertToTensor)(values, "values", "sparseFillEmptyRows");
    const $denseShape = (0, _tensorUtilEnv.convertToTensor)(denseShape, "denseShape", "sparseFillEmptyRows", "int32");
    const $defaultValue = (0, _tensorUtilEnv.convertToTensor)(defaultValue, "defaultValue", "sparseFillEmptyRows", $values.dtype);
    if ($indices.rank !== 2) throw new Error(`Indices should be Tensor2D but received shape
        ${$indices.shape}`);
    if ($values.rank !== 1) throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);
    if ($denseShape.rank !== 1) throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);
    if ($defaultValue.rank !== 0) throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);
    const inputs = {
        indices: $indices,
        values: $values,
        denseShape: $denseShape,
        defaultValue: $defaultValue
    };
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseFillEmptyRows), inputs);
    return {
        outputIndices: result[0],
        outputValues: result[1],
        emptyRowIndicator: result[2],
        reverseIndexMap: result[3]
    };
}
const sparseFillEmptyRows = /* @__PURE__ */ (0, _operation.op)({
    sparseFillEmptyRows_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"f6fUL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseReshape", ()=>sparseReshape);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * This operation has the same semantics as reshape on the represented dense
 * tensor. The `inputIndices` are recomputed based on the requested `newShape`.
 * If one component of `newShape` is the special value -1, the size of that
 * dimension is computed so that the total dense size remains constant. At most
 * one component of `newShape` can be -1. The number of dense elements implied
 * by `newShape` must be the same as the number of dense elements originally
 * implied by `inputShape`. Reshaping does not affect the order of values in the
 * SparseTensor. If the input tensor has rank R_in and N non-empty values, and
 * `newShape` has length R_out, then `inputIndices` has shape [N, R_in],
 * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and
 * `outputShape` has length R_out.
 *
 * ```js
 * const result = tf.sparse.sparseReshape(
 *   [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],
 *   [2, 3, 6], [9, -1]);
 * console.log(result);
 * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]
 * result['outputShape'].print(); // [9, 4]
 * ```
 * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty
 * values in a SparseTensor.
 * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense
 * shape.
 * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.
 * @return A map with the following properties:
 *     - outputIndices: 2-D. N x R_out matrix with the updated indices of
 *       non-empty values in the output SparseTensor.
 *     - outputShape: 1-D. R_out vector with the full dense shape of the output
 *       SparseTensor. This is the same as newShape but with any -1 dimensions
 *        filled in.
 * @doc {heading: 'Operations', subheading: 'Sparse'}
 */ function sparseReshape_(inputIndices, inputShape, newShape) {
    const $inputIndices = (0, _tensorUtilEnv.convertToTensor)(inputIndices, "inputIndices", "sparseReshape", "int32");
    const $inputShape = (0, _tensorUtilEnv.convertToTensor)(inputShape, "inputShape", "sparseReshape", "int32");
    const $newShape = (0, _tensorUtilEnv.convertToTensor)(newShape, "newShape", "sparseReshape", "int32");
    if ($inputIndices.rank !== 2) throw new Error(`Input indices should be Tensor2D but received shape
        ${$inputIndices.shape}`);
    if ($inputShape.rank !== 1) throw new Error(`Input shape should be Tensor1D but received shape ${$inputShape.shape}`);
    if ($newShape.rank !== 1) throw new Error(`New shape should be Tensor1D but received shape ${$newShape.shape}`);
    const inputs = {
        inputIndices: $inputIndices,
        inputShape: $inputShape,
        newShape: $newShape
    };
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseReshape), inputs);
    return {
        outputIndices: result[0],
        outputShape: result[1]
    };
}
const sparseReshape = /* @__PURE__ */ (0, _operation.op)({
    sparseReshape_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d0IYG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseSegmentMean", ()=>sparseSegmentMean);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * Computes the mean along sparse segments of a tensor.
 *
 * ```js
 * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]);
 * // Select two rows, one segment.
 * const result1 = tf.sparse.sparseSegmentMean(c,
 *                                           tf.tensor1d([0, 1], 'int32'),
 *                                           tf.tensor1d([0, 0], 'int32'));
 * result1.print(); // [[0, 0, 0, 0]]
 *
 * // Select two rows, two segments.
 * const result2 = tf.sparse.sparseSegmentMean(c,
 *                                             tf.tensor1d([0, 1], 'int32'),
 *                                             tf.tensor1d([0, 1], 'int32'));
 * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]
 *
 * // Select all rows, two segments.
 * const result3 = tf.sparse.sparseSegmentMean(c,
 *                                             tf.tensor1d([0, 1, 2], 'int32'),
 *                                             tf.tensor1d([0, 1, 1], 'int32'));
 * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]]
 * ```
 * @param data: A Tensor of at least one dimension with data that will be
 *     assembled in the output.
 * @param indices: A 1-D Tensor with indices into data. Has same rank as
 *     segmentIds.
 * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values
 *     should be sorted and can be repeated.
 * @return Has same shape as data, except for dimension 0 which has equal to
 *         the number of segments.
 *
 * @doc {heading: 'Operations', subheading: 'Sparse'}
 */ function sparseSegmentMean_(data, indices, segmentIds) {
    const $data = (0, _tensorUtilEnv.convertToTensor)(data, "data", "sparseSegmentMean");
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "sparseSegmentMean", "int32");
    const $segmentIds = (0, _tensorUtilEnv.convertToTensor)(segmentIds, "segmentIds", "sparseSegmentMean", "int32");
    if ($data.rank < 1) throw new Error(`Data should be at least 1 dimensional but received scalar`);
    if ($indices.rank !== 1) throw new Error(`Indices should be Tensor1D but received shape
          ${$indices.shape}`);
    if ($segmentIds.rank !== 1) throw new Error(`Segment ids should be Tensor1D but received shape
          ${$segmentIds.shape}`);
    const inputs = {
        data: $data,
        indices: $indices,
        segmentIds: $segmentIds
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseSegmentMean), inputs);
}
const sparseSegmentMean = /* @__PURE__ */ (0, _operation.op)({
    sparseSegmentMean_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7MtJg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseSegmentSum", ()=>sparseSegmentSum);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * Computes the sum along sparse segments of a tensor.
 *
 * ```js
 * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]);
 * // Select two rows, one segment.
 * const result1 = tf.sparse.sparseSegmentSum(c,
 *                                           tf.tensor1d([0, 1], 'int32'),
 *                                           tf.tensor1d([0, 0], 'int32'));
 * result1.print(); // [[0, 0, 0, 0]]
 *
 * // Select two rows, two segments.
 * const result2 = tf.sparse.sparseSegmentSum(c,
 *                                           tf.tensor1d([0, 1], 'int32'),
 *                                           tf.tensor1d([0, 1], 'int32'));
 * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]
 *
 * // Select all rows, two segments.
 * const result3 = tf.sparse.sparseSegmentSum(c,
 *                                           tf.tensor1d([0, 1, 2], 'int32'),
 *                                           tf.tensor1d([0, 0, 1], 'int32'));
 * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]]
 * ```
 * @param data: A Tensor of at least one dimension with data that will be
 *     assembled in the output.
 * @param indices: A 1-D Tensor with indices into data. Has same rank as
 *     segmentIds.
 * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values
 *     should be sorted and can be repeated.
 * @return Has same shape as data, except for dimension 0 which has equal to
 *         the number of segments.
 *
 * @doc {heading: 'Operations', subheading: 'Sparse'}
 */ function sparseSegmentSum_(data, indices, segmentIds) {
    const $data = (0, _tensorUtilEnv.convertToTensor)(data, "data", "sparseSegmentSum");
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "sparseSegmentSum", "int32");
    const $segmentIds = (0, _tensorUtilEnv.convertToTensor)(segmentIds, "segmentIds", "sparseSegmentSum", "int32");
    if ($data.rank < 1) throw new Error(`Data should be at least 1 dimensional but received scalar`);
    if ($indices.rank !== 1) throw new Error(`Indices should be Tensor1D but received shape
         ${$indices.shape}`);
    if ($segmentIds.rank !== 1) throw new Error(`Segment ids should be Tensor1D but received shape
         ${$segmentIds.shape}`);
    const inputs = {
        data: $data,
        indices: $indices,
        segmentIds: $segmentIds
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseSegmentSum), inputs);
}
const sparseSegmentSum = /* @__PURE__ */ (0, _operation.op)({
    sparseSegmentSum_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gELNE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringNGrams", ()=>stringNGrams);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * Creates ngrams from ragged string data.
 *
 * This op accepts a ragged tensor with 1 ragged dimension containing only
 * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams
 * of that string, joined along the innermost axis.
 *
 * ```js
 * const result = tf.string.stringNGrams(
 *   ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'),
 *   '|', [1, 2], 'LP', 'RP', -1, false);
 * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP',
 *                           //  'c', 'd', 'LP|c', 'c|d', 'd|RP']
 * result['nGramsSplits'].print(); // [0, 5, 10]
 * ```
 * @param data: The values tensor of the ragged string tensor to make ngrams out
 *     of. Must be a 1D string tensor.
 * @param dataSplits: The splits tensor of the ragged string tensor to make
 *     ngrams out of.
 * @param separator: The string to append between elements of the token. Use ""
 *     for no separator.
 * @param nGramWidths: The sizes of the ngrams to create.
 * @param leftPad: The string to use to pad the left side of the ngram sequence.
 *     Only used if pad_width !== 0.
 * @param rightPad: The string to use to pad the right side of the ngram
 *     sequence. Only used if pad_width !== 0.
 * @param padWidth: The number of padding elements to add to each side of each
 *     sequence. Note that padding will never be greater than `nGramWidths`-1
 *     regardless of this value. If `padWidth`=-1, then add max(`nGramWidths`)-1
 *     elements.
 * @param preserveShortSequences: If true, then ensure that at least one ngram
 *     is generated for each input sequence. In particular, if an input sequence
 *     is shorter than min(ngramWidth) + 2*padWidth, then generate a single
 *     ngram containing the entire sequence. If false, then no ngrams are
 *     generated for these short input sequences.
 * @return A map with the following properties:
 *     - nGrams: The values tensor of the output ngrams ragged tensor.
 *     - nGramsSplits: The splits tensor of the output ngrams ragged tensor.
 *
 * @doc {heading: 'Operations', subheading: 'String'}
 */ function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {
    const $data = (0, _tensorUtilEnv.convertToTensor)(data, "data", "stringNGrams", "string");
    if ($data.dtype !== "string") throw new Error("Data must be of datatype string");
    if ($data.shape.length !== 1) throw new Error(`Data must be a vector, saw: ${$data.shape}`);
    const $dataSplits = (0, _tensorUtilEnv.convertToTensor)(dataSplits, "dataSplits", "stringNGrams");
    if ($dataSplits.dtype !== "int32") throw new Error("Data splits must be of datatype int32");
    const attrs = {
        separator,
        nGramWidths,
        leftPad,
        rightPad,
        padWidth,
        preserveShortSequences
    };
    const inputs = {
        data: $data,
        dataSplits: $dataSplits
    };
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.StringNGrams), inputs, attrs);
    return {
        nGrams: result[0],
        nGramsSplits: result[1]
    };
}
const stringNGrams = /* @__PURE__ */ (0, _operation.op)({
    stringNGrams_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3z1cj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringSplit", ()=>stringSplit);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * Split elements of `input` based on `delimiter` into a SparseTensor .
 *
 * Let N be the size of source (typically N will be the batch size). Split each
 * element of `input` based on `delimiter` and return a SparseTensor containing
 * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True.
 *
 * `delimiter` can be empty, or a string of split characters. If `delimiter` is
 * an empty string, each element of `input` is split into individual
 * character strings. Otherwise every character of `delimiter` is a potential
 * split point.
 *
 * ```js
 * const result = tf.string.stringSplit(['hello world',  'a b c'], ' ');
 * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]]
 * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c']
 * result['shape'].print(); // [2, 3]
 * ```
 * @param input: 1-D. Strings to split.
 * @param delimiter: 0-D. Delimiter characters, or empty string.
 * @param skipEmpty: Optional. If true, skip the empty strings from the result.
 *     Defaults to true.
 * @return A map with the following properties:
 *     - indices: A dense matrix of int32 representing the indices of the sparse
 *       tensor.
 *     - values: A vector of strings corresponding to the splited values.
 *     - shape: a length-2 vector of int32 representing the shape of the sparse
 * tensor, where the first value is N and the second value is the maximum number
 * of tokens in a single input entry.
 *
 * @doc {heading: 'Operations', subheading: 'String'}
 */ function stringSplit_(input, delimiter, skipEmpty = true) {
    const $input = (0, _tensorUtilEnv.convertToTensor)(input, "input", "stringSplit", "string");
    const $delimiter = (0, _tensorUtilEnv.convertToTensor)(delimiter, "delimiter", "stringSplit", "string");
    if ($input.rank !== 1) throw new Error(`Input should be Tensor1D but received shape ${$input.shape}`);
    if ($delimiter.rank !== 0) throw new Error(`Delimiter should be a scalar but received shape ${$delimiter.shape}`);
    const attrs = {
        skipEmpty
    };
    const inputs = {
        input: $input,
        delimiter: $delimiter
    };
    const result = (0, _engine.ENGINE).runKernel((0, _kernelNames.StringSplit), inputs, attrs);
    return {
        indices: result[0],
        values: result[1],
        shape: result[2]
    };
}
const stringSplit = /* @__PURE__ */ (0, _operation.op)({
    stringSplit_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8mVop":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringToHashBucketFast", ()=>stringToHashBucketFast);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * Converts each string in the input Tensor to its hash mod by a number of
 * buckets.
 *
 * The hash function is deterministic on the content of the string within the
 * process and will never change. However, it is not suitable for cryptography.
 * This function may be used when CPU time is scarce and inputs are trusted or
 * unimportant. There is a risk of adversaries constructing inputs that all hash
 * to the same bucket.
 *
 * ```js
 * const result = tf.string.stringToHashBucketFast(
 *   ['Hello', 'TensorFlow', '2.x'], 3);
 * result.print(); // [0, 2, 2]
 * ```
 * @param input: The strings to assign a hash bucket.
 * @param numBuckets: The number of buckets.
 * @return A Tensor of the same shape as the input tensor.
 *
 * @doc {heading: 'Operations', subheading: 'String'}
 */ function stringToHashBucketFast_(input, numBuckets) {
    const $input = (0, _tensorUtilEnv.convertToTensor)(input, "input", "stringToHashBucketFast", "string");
    const attrs = {
        numBuckets
    };
    if (numBuckets <= 0) throw new Error(`Number of buckets must be at least 1`);
    const inputs = {
        input: $input
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.StringToHashBucketFast), inputs, attrs);
}
const stringToHashBucketFast = /* @__PURE__ */ (0, _operation.op)({
    stringToHashBucketFast_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dLgGV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "staticRegexReplace", ()=>staticRegexReplace);
var _engine = require("../../engine");
var _kernelNames = require("../../kernel_names");
var _tensorUtilEnv = require("../../tensor_util_env");
var _operation = require("../operation");
/**
 * Replace the match of a `pattern` in `input` with `rewrite`.
 *
 * ```js
 * const result = tf.string.staticRegexReplace(
 *     ['format       this   spacing      better'], ' +', ' ');
 * result.print(); // ['format this spacing better']
 * ```
 * @param input: A Tensor of type string. The text to be processed.
 * @param pattern: A string. The regular expression to match the input.
 * @param rewrite: A string. The rewrite to be applied to the matched
 *     expression.
 * @param replaceGlobal: An optional bool. Defaults to True. If True, the
 *     replacement is global, otherwise the replacement is done only on the
 *     first match.
 * @return A Tensor of type string.
 *
 * @doc {heading: 'Operations', subheading: 'String'}
 */ function staticRegexReplace_(input, pattern, rewrite, replaceGlobal = true) {
    const $input = (0, _tensorUtilEnv.convertToTensor)(input, "input", "staticRegexReplace", "string");
    const attrs = {
        pattern,
        rewrite,
        replaceGlobal
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.StaticRegexReplace), {
        x: $input
    }, attrs);
}
const staticRegexReplace = /* @__PURE__ */ (0, _operation.op)({
    staticRegexReplace_
});

},{"../../engine":"1g2qo","../../kernel_names":"hniYO","../../tensor_util_env":"6vfHI","../operation":"ip64W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1b69z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */ parcelHelpers.export(exports, "Optimizer", ()=>Optimizer);
var _globals = require("../globals");
var _gradients = require("../gradients");
var _ops = require("../ops/ops");
var _serialization = require("../serialization");
class Optimizer extends (0, _serialization.Serializable) {
    /**
     * Executes `f()` and minimizes the scalar output of `f()` by computing
     * gradients of y with respect to the list of trainable variables provided by
     * `varList`. If no list is provided, it defaults to all trainable variables.
     *
     * @param f The function to execute and whose output to minimize.
     * @param returnCost Whether to return the scalar cost value produced by
     * executing `f()`.
     * @param varList An optional list of variables to update. If specified, only
     * the trainable variables in varList will be updated by minimize. Defaults to
     * all trainable variables.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers'}
     */ minimize(f, returnCost = false, varList) {
        const { value, grads } = this.computeGradients(f, varList);
        if (varList != null) {
            const gradArray = varList.map((v)=>({
                    name: v.name,
                    tensor: grads[v.name]
                }));
            this.applyGradients(gradArray);
        } else this.applyGradients(grads);
        // Dispose gradients.
        (0, _globals.dispose)(grads);
        if (returnCost) return value;
        else {
            value.dispose();
            return null;
        }
    }
    /**
     * The number of iterations that this optimizer instance has been invoked for.
     */ get iterations() {
        if (this.iterations_ == null) this.iterations_ = 0;
        return this.iterations_;
    }
    incrementIterations() {
        this.iterations_ = this.iterations + 1;
    }
    /**
     * Executes f() and computes the gradient of the scalar output of f() with
     * respect to the list of trainable variables provided by `varList`. If no
     * list is provided, it defaults to all trainable variables.
     *
     * @param f The function to execute and whose output to use for computing
     * gradients with respect to variables.
     * @param varList An optional list of variables to compute gradients with
     * respect to. If specified, only the trainable variables in varList will have
     * gradients computed with respect to. Defaults to all trainable variables.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers'}
     */ computeGradients(f, varList) {
        return (0, _gradients.variableGrads)(f, varList);
    }
    /**
     * Dispose the variables (if any) owned by this optimizer instance.
     */ dispose() {
        if (this.iterations_ != null) (0, _globals.dispose)(this.iterations_);
    }
    async saveIterations() {
        if (this.iterations_ == null) this.iterations_ = 0;
        return {
            name: "iter",
            // TODO(cais): Use 'int64' type when available.
            tensor: (0, _ops.scalar)(this.iterations_, "int32")
        };
    }
    async getWeights() {
        throw new Error("getWeights() is not implemented for this optimizer yet.");
    }
    async setWeights(weightValues) {
        throw new Error(`setWeights() is not implemented for this optimizer class ` + `${this.getClassName()}`);
    }
    /**
     * Extract the first element of the weight values and set it
     * as the iterations counter variable of this instance of optimizer.
     *
     * @param weightValues
     * @returns Weight values with the first element consumed and excluded.
     */ async extractIterations(weightValues) {
        this.iterations_ = (await weightValues[0].tensor.data())[0];
        return weightValues.slice(1);
    }
}
Object.defineProperty(Optimizer, Symbol.hasInstance, {
    value: (instance)=>{
        return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
    }
});

},{"../globals":"eO1Ja","../gradients":"dDHpg","../ops/ops":"13k1e","../serialization":"dv6JX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dv6JX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Serializable defines the serialization contract.
 *
 * TFJS requires serializable classes to return their className when asked
 * to avoid issues with minification.
 */ parcelHelpers.export(exports, "Serializable", ()=>Serializable);
/**
 * Maps string keys to class constructors.
 *
 * Used during (de)serialization from the cross-language JSON format, which
 * requires the class name in the serialization format matches the class
 * names as used in Python, should it exist.
 */ parcelHelpers.export(exports, "SerializationMap", ()=>SerializationMap);
/**
 * Register a class with the serialization map of TensorFlow.js.
 *
 * This is often used for registering custom Layers, so they can be
 * serialized and deserialized.
 *
 * Example 1. Register the class without package name and specified name.
 *
 * ```js
 * class MyCustomLayer extends tf.layers.Layer {
 *   static className = 'MyCustomLayer';
 *
 *   constructor(config) {
 *     super(config);
 *   }
 * }
 * tf.serialization.registerClass(MyCustomLayer);
 * console.log(tf.serialization.GLOBALCUSTOMOBJECT.get("Custom>MyCustomLayer"));
 * console.log(tf.serialization.GLOBALCUSTOMNAMES.get(MyCustomLayer));
 * ```
 *
 * Example 2. Register the class with package name: "Package" and specified
 * name: "MyLayer".
 * ```js
 * class MyCustomLayer extends tf.layers.Layer {
 *   static className = 'MyCustomLayer';
 *
 *   constructor(config) {
 *     super(config);
 *   }
 * }
 * tf.serialization.registerClass(MyCustomLayer, "Package", "MyLayer");
 * console.log(tf.serialization.GLOBALCUSTOMOBJECT.get("Package>MyLayer"));
 * console.log(tf.serialization.GLOBALCUSTOMNAMES.get(MyCustomLayer));
 * ```
 *
 * Example 3. Register the class with specified name: "MyLayer".
 * ```js
 * class MyCustomLayer extends tf.layers.Layer {
 *   static className = 'MyCustomLayer';
 *
 *   constructor(config) {
 *     super(config);
 *   }
 * }
 * tf.serialization.registerClass(MyCustomLayer, undefined, "MyLayer");
 * console.log(tf.serialization.GLOBALCUSTOMOBJECT.get("Custom>MyLayer"));
 * console.log(tf.serialization.GLOBALCUSTOMNAMES.get(MyCustomLayer));
 * ```
 *
 * Example 4. Register the class with specified package name: "Package".
 * ```js
 * class MyCustomLayer extends tf.layers.Layer {
 *   static className = 'MyCustomLayer';
 *
 *   constructor(config) {
 *     super(config);
 *   }
 * }
 * tf.serialization.registerClass(MyCustomLayer, "Package");
 * console.log(tf.serialization.GLOBALCUSTOMOBJECT
 * .get("Package>MyCustomLayer"));
 * console.log(tf.serialization.GLOBALCUSTOMNAMES
 * .get(MyCustomLayer));
 * ```
 *
 * @param cls The class to be registered. It must have a public static member
 *   called `className` defined and the value must be a non-empty string.
 * @param pkg The pakcage name that this class belongs to. This used to define
 *     the key in GlobalCustomObject. If not defined, it defaults to `Custom`.
 * @param name The name that user specified. It defaults to the actual name of
 *     the class as specified by its static `className` property.
 * @doc {heading: 'Models', subheading: 'Serialization', ignoreCI: true}
 */ parcelHelpers.export(exports, "registerClass", ()=>registerClass);
/**
 * Get the registered name of a class. If the class has not been registered,
 * return the class name.
 *
 * @param cls The class we want to get register name for. It must have a public
 *     static member called `className` defined.
 * @returns registered name or class name.
 */ parcelHelpers.export(exports, "getRegisteredName", ()=>getRegisteredName);
var _util = require("./util");
/**
 * Maps to mapping between the custom object and its name.
 *
 * After registering a custom class, these two maps will add key-value pairs
 * for the class object and the registered name.
 *
 * Therefore we can get the relative registered name by calling
 * getRegisteredName() function.
 *
 * For example:
 * GLOBAL_CUSTOM_OBJECT: {key=registeredName: value=corresponding
 * CustomObjectClass}
 *
 * GLOBAL_CUSTOM_NAMES: {key=CustomObjectClass: value=corresponding
 * registeredName}
 *
 */ const GLOBAL_CUSTOM_OBJECT = new Map();
const GLOBAL_CUSTOM_NAMES = new Map();
class Serializable {
    /**
     * Return the class name for this class to use in serialization contexts.
     *
     * Generally speaking this will be the same thing that constructor.name
     * would have returned.  However, the class name needs to be robust
     * against minification for serialization/deserialization to work properly.
     *
     * There's also places such as initializers.VarianceScaling, where
     * implementation details between different languages led to different
     * class hierarchies and a non-leaf node is used for serialization purposes.
     */ getClassName() {
        return this.constructor.className;
    }
    /**
     * Creates an instance of T from a ConfigDict.
     *
     * This works for most descendants of serializable.  A few need to
     * provide special handling.
     * @param cls A Constructor for the class to instantiate.
     * @param config The Configuration for the object.
     */ /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config);
    }
}
class SerializationMap {
    constructor(){
        this.classNameMap = {};
    }
    /**
     * Returns the singleton instance of the map.
     */ static getMap() {
        if (SerializationMap.instance == null) SerializationMap.instance = new SerializationMap();
        return SerializationMap.instance;
    }
    /**
     * Registers the class as serializable.
     */ static register(cls) {
        SerializationMap.getMap().classNameMap[cls.className] = [
            cls,
            cls.fromConfig
        ];
    }
}
function registerClass(cls, pkg, name) {
    (0, _util.assert)(cls.className != null, ()=>`Class being registered does not have the static className ` + `property defined.`);
    (0, _util.assert)(typeof cls.className === "string", ()=>`className is required to be a string, but got type ` + typeof cls.className);
    (0, _util.assert)(cls.className.length > 0, ()=>`Class being registered has an empty-string as its className, ` + `which is disallowed.`);
    if (typeof pkg === "undefined") pkg = "Custom";
    if (typeof name === "undefined") name = cls.className;
    const className = name;
    const registerName = pkg + ">" + className;
    SerializationMap.register(cls);
    GLOBAL_CUSTOM_OBJECT.set(registerName, cls);
    GLOBAL_CUSTOM_NAMES.set(cls, registerName);
    return cls;
}
function getRegisteredName(cls) {
    if (GLOBAL_CUSTOM_NAMES.has(cls)) return GLOBAL_CUSTOM_NAMES.get(cls);
    else return cls.className;
}

},{"./util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ddRDd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/** @doclink Optimizer */ parcelHelpers.export(exports, "AdagradOptimizer", ()=>AdagradOptimizer);
var _engine = require("../engine");
var _globals = require("../globals");
var _add = require("../ops/add");
var _div = require("../ops/div");
var _fill = require("../ops/fill");
var _mul = require("../ops/mul");
var _sqrt = require("../ops/sqrt");
var _square = require("../ops/square");
var _optimizer = require("./optimizer");
class AdagradOptimizer extends (0, _optimizer.Optimizer) {
    /** @nocollapse */ static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return "Adagrad";
    }
    constructor(learningRate, initialAccumulatorValue = 0.1){
        super();
        this.learningRate = learningRate;
        this.initialAccumulatorValue = initialAccumulatorValue;
        this.accumulatedGrads = [];
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item)=>item.name) : Object.keys(variableGradients);
        variableNames.forEach((name, i)=>{
            const value = (0, _engine.ENGINE).registeredVariables[name];
            if (this.accumulatedGrads[i] == null) {
                const trainable = false;
                this.accumulatedGrads[i] = {
                    originalName: `${name}/accumulator`,
                    variable: (0, _globals.tidy)(()=>(0, _fill.fill)(value.shape, this.initialAccumulatorValue).variable(trainable))
                };
            }
            const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
            if (gradient == null) return;
            const accumulatedGrad = this.accumulatedGrads[i].variable;
            (0, _globals.tidy)(()=>{
                const newAccumulatedGrad = (0, _add.add)(accumulatedGrad, (0, _square.square)(gradient));
                accumulatedGrad.assign(newAccumulatedGrad);
                const newValue = (0, _add.add)((0, _mul.mul)((0, _div.div)(gradient, (0, _sqrt.sqrt)((0, _add.add)(newAccumulatedGrad, (0, _engine.ENGINE).backend.epsilon()))), -this.learningRate), value);
                value.assign(newValue);
            });
        });
        this.incrementIterations();
    }
    dispose() {
        if (this.accumulatedGrads != null) (0, _globals.dispose)(this.accumulatedGrads.map((v)=>v.variable));
    }
    async getWeights() {
        // Order matters for Python compatibility.
        return [
            await this.saveIterations()
        ].concat(this.accumulatedGrads.map((v)=>({
                name: v.originalName,
                tensor: v.variable
            })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        const trainable = false;
        this.accumulatedGrads = weightValues.map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
    }
    getConfig() {
        return {
            "learningRate": this.learningRate,
            "initialAccumulatorValue": this.initialAccumulatorValue
        };
    }
    /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config["learningRate"], config["initialAccumulatorValue"]);
    }
}

},{"../engine":"1g2qo","../globals":"eO1Ja","../ops/add":"aynh7","../ops/div":"5ZT1h","../ops/fill":"i7goe","../ops/mul":"fOWrC","../ops/sqrt":"cOGi9","../ops/square":"cOosW","./optimizer":"1b69z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5uefH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "AdamOptimizer", ()=>AdamOptimizer);
var _engine = require("../engine");
var _globals = require("../globals");
var _add = require("../ops/add");
var _div = require("../ops/div");
var _mul = require("../ops/mul");
var _pow = require("../ops/pow");
var _scalar = require("../ops/scalar");
var _sqrt = require("../ops/sqrt");
var _square = require("../ops/square");
var _sub = require("../ops/sub");
var _zerosLike = require("../ops/zeros_like");
var _optimizer = require("./optimizer");
class AdamOptimizer extends (0, _optimizer.Optimizer) {
    /** @nocollapse */ static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return "Adam";
    }
    constructor(learningRate, beta1, beta2, epsilon = null){
        super();
        this.learningRate = learningRate;
        this.beta1 = beta1;
        this.beta2 = beta2;
        this.epsilon = epsilon;
        this.accumulatedFirstMoment = [];
        this.accumulatedSecondMoment = [];
        (0, _globals.tidy)(()=>{
            // accB* will be updated by batch.
            this.accBeta1 = (0, _scalar.scalar)(beta1).variable();
            this.accBeta2 = (0, _scalar.scalar)(beta2).variable();
        });
        if (epsilon == null) this.epsilon = (0, _engine.ENGINE).backend.epsilon();
    }
    applyGradients(variableGradients) {
        const varNames = Array.isArray(variableGradients) ? variableGradients.map((v)=>v.name) : Object.keys(variableGradients);
        (0, _globals.tidy)(()=>{
            const oneMinusAccBeta1 = (0, _sub.sub)(1, this.accBeta1);
            const oneMinusAccBeta2 = (0, _sub.sub)(1, this.accBeta2);
            varNames.forEach((name, i)=>{
                const value = (0, _engine.ENGINE).registeredVariables[name];
                const trainable = false;
                if (this.accumulatedFirstMoment[i] == null) this.accumulatedFirstMoment[i] = {
                    originalName: `${name}/m`,
                    variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
                };
                if (this.accumulatedSecondMoment[i] == null) this.accumulatedSecondMoment[i] = {
                    originalName: `${name}/v`,
                    variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
                };
                const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
                if (gradient == null) return;
                const firstMoment = this.accumulatedFirstMoment[i].variable;
                const secondMoment = this.accumulatedSecondMoment[i].variable;
                const newFirstMoment = (0, _add.add)((0, _mul.mul)(firstMoment, this.beta1), (0, _mul.mul)(gradient, 1 - this.beta1));
                const newSecondMoment = (0, _add.add)((0, _mul.mul)(secondMoment, this.beta2), (0, _mul.mul)((0, _square.square)(gradient), 1 - this.beta2));
                const biasCorrectedFirstMoment = (0, _div.div)(newFirstMoment, oneMinusAccBeta1);
                const biasCorrectedSecondMoment = (0, _div.div)(newSecondMoment, oneMinusAccBeta2);
                firstMoment.assign(newFirstMoment);
                secondMoment.assign(newSecondMoment);
                const newValue = (0, _add.add)((0, _mul.mul)((0, _div.div)(biasCorrectedFirstMoment, (0, _add.add)((0, _sqrt.sqrt)(biasCorrectedSecondMoment), this.epsilon)), -this.learningRate), value);
                value.assign(newValue);
            });
            this.accBeta1.assign((0, _mul.mul)(this.accBeta1, this.beta1));
            this.accBeta2.assign((0, _mul.mul)(this.accBeta2, this.beta2));
        });
        this.incrementIterations();
    }
    dispose() {
        this.accBeta1.dispose();
        this.accBeta2.dispose();
        if (this.accumulatedFirstMoment != null) (0, _globals.dispose)(this.accumulatedFirstMoment.map((v)=>v.variable));
        if (this.accumulatedSecondMoment != null) (0, _globals.dispose)(this.accumulatedSecondMoment.map((v)=>v.variable));
    }
    async getWeights() {
        // Order matters for Python compatibility.
        const variables = [
            ...this.accumulatedFirstMoment,
            ...this.accumulatedSecondMoment
        ];
        return [
            await this.saveIterations()
        ].concat(variables.map((v)=>({
                name: v.originalName,
                tensor: v.variable
            })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        (0, _globals.tidy)(()=>{
            this.accBeta1.assign((0, _pow.pow)(this.beta1, this.iterations_ + 1));
            this.accBeta2.assign((0, _pow.pow)(this.beta2, this.iterations_ + 1));
        });
        const variableCount = weightValues.length / 2;
        const trainable = false;
        this.accumulatedFirstMoment = weightValues.slice(0, variableCount).map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        this.accumulatedSecondMoment = weightValues.slice(variableCount, variableCount * 2).map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
    }
    getConfig() {
        return {
            "learningRate": this.learningRate,
            "beta1": this.beta1,
            "beta2": this.beta2,
            "epsilon": this.epsilon
        };
    }
    /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"]);
    }
}

},{"../engine":"1g2qo","../globals":"eO1Ja","../ops/add":"aynh7","../ops/div":"5ZT1h","../ops/mul":"fOWrC","../ops/pow":"gezpC","../ops/scalar":"7lhC8","../ops/sqrt":"cOGi9","../ops/square":"cOosW","../ops/sub":"jBBQ2","../ops/zeros_like":"1Xjct","./optimizer":"1b69z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"72DSu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "AdamaxOptimizer", ()=>AdamaxOptimizer);
var _engine = require("../engine");
var _globals = require("../globals");
var _abs = require("../ops/abs");
var _add = require("../ops/add");
var _div = require("../ops/div");
var _maximum = require("../ops/maximum");
var _mul = require("../ops/mul");
var _scalar = require("../ops/scalar");
var _sub = require("../ops/sub");
var _zerosLike = require("../ops/zeros_like");
var _optimizer = require("./optimizer");
class AdamaxOptimizer extends (0, _optimizer.Optimizer) {
    /** @nocollapse */ static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return "Adamax";
    }
    constructor(learningRate, beta1, beta2, epsilon = null, decay = 0.0){
        super();
        this.learningRate = learningRate;
        this.beta1 = beta1;
        this.beta2 = beta2;
        this.epsilon = epsilon;
        this.decay = decay;
        this.accumulatedFirstMoment = [];
        this.accumulatedWeightedInfNorm = [];
        (0, _globals.tidy)(()=>{
            this.iteration = (0, _scalar.scalar)(0).variable();
            this.accBeta1 = (0, _scalar.scalar)(beta1).variable();
        });
        if (epsilon == null) this.epsilon = (0, _engine.ENGINE).backend.epsilon();
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item)=>item.name) : Object.keys(variableGradients);
        (0, _globals.tidy)(()=>{
            const oneMinusAccBeta1 = (0, _sub.sub)(1, this.accBeta1);
            const lr = (0, _div.div)(-this.learningRate, (0, _add.add)((0, _mul.mul)(this.iteration, this.decay), 1));
            variableNames.forEach((name, i)=>{
                const value = (0, _engine.ENGINE).registeredVariables[name];
                const trainable = false;
                if (this.accumulatedFirstMoment[i] == null) this.accumulatedFirstMoment[i] = {
                    originalName: `${name}/m`,
                    variable: (0, _zerosLike.zerosLike)(value).variable(trainable)
                };
                if (this.accumulatedWeightedInfNorm[i] == null) this.accumulatedWeightedInfNorm[i] = {
                    originalName: `${name}/v`,
                    variable: (0, _zerosLike.zerosLike)(value).variable(trainable)
                };
                const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
                if (gradient == null) return;
                const firstMoment = this.accumulatedFirstMoment[i].variable;
                const weightedInfNorm = this.accumulatedWeightedInfNorm[i].variable;
                const newFirstMoment = (0, _add.add)((0, _mul.mul)(firstMoment, this.beta1), (0, _mul.mul)(gradient, 1 - this.beta1));
                const ut0 = (0, _mul.mul)(weightedInfNorm, this.beta2);
                const ut1 = (0, _abs.abs)(gradient);
                const newWeightedInfNorm = (0, _maximum.maximum)(ut0, ut1);
                firstMoment.assign(newFirstMoment);
                weightedInfNorm.assign(newWeightedInfNorm);
                const newValue = (0, _add.add)((0, _mul.mul)((0, _div.div)(lr, oneMinusAccBeta1), (0, _div.div)(newFirstMoment, (0, _add.add)(newWeightedInfNorm, this.epsilon))), value);
                value.assign(newValue);
            });
            this.iteration.assign((0, _add.add)(this.iteration, 1));
            this.accBeta1.assign((0, _mul.mul)(this.accBeta1, this.beta1));
        });
        this.incrementIterations();
    }
    dispose() {
        this.accBeta1.dispose();
        this.iteration.dispose();
        if (this.accumulatedFirstMoment != null) (0, _globals.dispose)(this.accumulatedFirstMoment.map((v)=>v.variable));
        if (this.accumulatedWeightedInfNorm != null) (0, _globals.dispose)(this.accumulatedWeightedInfNorm.map((v)=>v.variable));
    }
    async getWeights() {
        throw new Error("getWeights() is not implemented for Adamax yet.");
    }
    async setWeights(weightValues) {
        throw new Error("setWeights() is not implemented for Adamax yet.");
    }
    getConfig() {
        return {
            "learningRate": this.learningRate,
            "beta1": this.beta1,
            "beta2": this.beta2,
            "epsilon": this.epsilon,
            "decay": this.decay
        };
    }
    /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"], config["decay"]);
    }
}

},{"../engine":"1g2qo","../globals":"eO1Ja","../ops/abs":"iZQ4e","../ops/add":"aynh7","../ops/div":"5ZT1h","../ops/maximum":"1nFpL","../ops/mul":"fOWrC","../ops/scalar":"7lhC8","../ops/sub":"jBBQ2","../ops/zeros_like":"1Xjct","./optimizer":"1b69z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bKhX1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/** @doclink Optimizer */ parcelHelpers.export(exports, "MomentumOptimizer", ()=>MomentumOptimizer);
var _engine = require("../engine");
var _globals = require("../globals");
var _add = require("../ops/add");
var _mul = require("../ops/mul");
var _scalar = require("../ops/scalar");
var _zerosLike = require("../ops/zeros_like");
var _sgdOptimizer = require("./sgd_optimizer");
class MomentumOptimizer extends (0, _sgdOptimizer.SGDOptimizer) {
    /** @nocollapse */ // Name matters for Python compatibility.
    static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return "Momentum";
    }
    constructor(learningRate, momentum, useNesterov = false){
        super(learningRate);
        this.learningRate = learningRate;
        this.momentum = momentum;
        this.useNesterov = useNesterov;
        this.accumulations = [];
        this.m = (0, _scalar.scalar)(this.momentum);
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item)=>item.name) : Object.keys(variableGradients);
        variableNames.forEach((name, i)=>{
            const value = (0, _engine.ENGINE).registeredVariables[name];
            if (this.accumulations[i] == null) {
                const trainable = false;
                this.accumulations[i] = {
                    originalName: `${name}/momentum`,
                    variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
                };
            }
            const accumulation = this.accumulations[i].variable;
            const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
            if (gradient == null) return;
            (0, _globals.tidy)(()=>{
                let newValue;
                const newAccumulation = (0, _add.add)((0, _mul.mul)(this.m, accumulation), gradient);
                if (this.useNesterov) newValue = (0, _add.add)((0, _mul.mul)(this.c, (0, _add.add)(gradient, (0, _mul.mul)(newAccumulation, this.m))), value);
                else newValue = (0, _add.add)((0, _mul.mul)(this.c, newAccumulation), value);
                accumulation.assign(newAccumulation);
                value.assign(newValue);
            });
        });
        this.incrementIterations();
    }
    dispose() {
        this.m.dispose();
        if (this.accumulations != null) (0, _globals.dispose)(this.accumulations.map((v)=>v.variable));
    }
    /**
     * Sets the momentum of the optimizer.
     *
     * @param momentum
     */ setMomentum(momentum) {
        this.momentum = momentum;
    }
    async getWeights() {
        // Order matters for Python compatibility.
        return [
            await this.saveIterations()
        ].concat(this.accumulations.map((v)=>({
                name: v.originalName,
                tensor: v.variable
            })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        const trainable = false;
        this.accumulations = weightValues.map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
    }
    getConfig() {
        return {
            "learningRate": this.learningRate,
            "momentum": this.momentum,
            "useNesterov": this.useNesterov
        };
    }
    /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config["learningRate"], config["momentum"], config["useNesterov"]);
    }
}

},{"../engine":"1g2qo","../globals":"eO1Ja","../ops/add":"aynh7","../ops/mul":"fOWrC","../ops/scalar":"7lhC8","../ops/zeros_like":"1Xjct","./sgd_optimizer":"4dxnr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4dxnr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/** @doclink Optimizer */ parcelHelpers.export(exports, "SGDOptimizer", ()=>SGDOptimizer);
var _engine = require("../engine");
var _globals = require("../globals");
var _add = require("../ops/add");
var _mul = require("../ops/mul");
var _scalar = require("../ops/scalar");
var _optimizer = require("./optimizer");
class SGDOptimizer extends (0, _optimizer.Optimizer) {
    /** @nocollapse */ static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return "SGD";
    }
    constructor(learningRate){
        super();
        this.learningRate = learningRate;
        this.setLearningRate(learningRate);
    }
    applyGradients(variableGradients) {
        const varNames = Array.isArray(variableGradients) ? variableGradients.map((v)=>v.name) : Object.keys(variableGradients);
        varNames.forEach((name, i)=>{
            const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
            if (gradient == null) return;
            const value = (0, _engine.ENGINE).registeredVariables[name];
            (0, _globals.tidy)(()=>{
                const newValue = (0, _add.add)((0, _mul.mul)(this.c, gradient), value);
                value.assign(newValue);
            });
        });
        this.incrementIterations();
    }
    /**
     * Sets the learning rate of the optimizer.
     */ setLearningRate(learningRate) {
        this.learningRate = learningRate;
        if (this.c != null) this.c.dispose();
        this.c = (0, _globals.keep)((0, _scalar.scalar)(-learningRate));
    }
    dispose() {
        this.c.dispose();
    }
    async getWeights() {
        return [
            await this.saveIterations()
        ];
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        if (weightValues.length !== 0) throw new Error("SGD optimizer does not have settable weights.");
    }
    getConfig() {
        return {
            "learningRate": this.learningRate
        };
    }
    /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config["learningRate"]);
    }
}

},{"../engine":"1g2qo","../globals":"eO1Ja","../ops/add":"aynh7","../ops/mul":"fOWrC","../ops/scalar":"7lhC8","./optimizer":"1b69z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"66Wvy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/** @doclink Optimizer */ parcelHelpers.export(exports, "RMSPropOptimizer", ()=>RMSPropOptimizer);
var _engine = require("../engine");
var _globals = require("../globals");
var _add = require("../ops/add");
var _div = require("../ops/div");
var _mul = require("../ops/mul");
var _sqrt = require("../ops/sqrt");
var _square = require("../ops/square");
var _sub = require("../ops/sub");
var _zerosLike = require("../ops/zeros_like");
var _optimizer = require("./optimizer");
class RMSPropOptimizer extends (0, _optimizer.Optimizer) {
    /** @nocollapse */ static get className() {
        // Name matters for Python compatibility.
        // This is a getter instead of a property because when it's a property, it
        // prevents the entire class from being tree-shaken.
        return "RMSProp";
    }
    constructor(learningRate, decay = 0.9, momentum = 0.0, epsilon = null, centered = false){
        super();
        this.learningRate = learningRate;
        this.decay = decay;
        this.momentum = momentum;
        this.epsilon = epsilon;
        this.accumulatedMeanSquares = [];
        this.accumulatedMoments = [];
        this.accumulatedMeanGrads = [];
        this.centered = centered;
        if (epsilon == null) this.epsilon = (0, _engine.ENGINE).backend.epsilon();
        if (learningRate == null) throw new Error(`learningRate for RMSPropOptimizer must be defined.`);
    }
    applyGradients(variableGradients) {
        const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item)=>item.name) : Object.keys(variableGradients);
        variableNames.forEach((name, i)=>{
            const value = (0, _engine.ENGINE).registeredVariables[name];
            const trainable = false;
            if (this.accumulatedMeanSquares[i] == null) this.accumulatedMeanSquares[i] = {
                originalName: `${name}/rms`,
                variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
            };
            if (this.accumulatedMoments[i] == null) this.accumulatedMoments[i] = {
                originalName: `${name}/momentum`,
                variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
            };
            if (this.accumulatedMeanGrads[i] == null && this.centered) this.accumulatedMeanGrads[i] = {
                originalName: `${name}/mg`,
                variable: (0, _globals.tidy)(()=>(0, _zerosLike.zerosLike)(value).variable(trainable))
            };
            const gradient = Array.isArray(variableGradients) ? variableGradients[i].tensor : variableGradients[name];
            if (gradient == null) return;
            const accumulatedMeanSquare = this.accumulatedMeanSquares[i].variable;
            const accumulatedMoments = this.accumulatedMoments[i].variable;
            (0, _globals.tidy)(()=>{
                const newAccumulatedMeanSquare = (0, _add.add)((0, _mul.mul)(accumulatedMeanSquare, this.decay), (0, _mul.mul)((0, _square.square)(gradient), 1 - this.decay));
                if (this.centered) {
                    const accumulatedMeanGrad = this.accumulatedMeanGrads[i].variable;
                    // Centered gradient
                    const newAccumulatedMeanGrad = (0, _add.add)((0, _mul.mul)(accumulatedMeanGrad, this.decay), (0, _mul.mul)(gradient, 1 - this.decay));
                    const gradContribution = (0, _div.div)((0, _mul.mul)(gradient, this.learningRate), (0, _sqrt.sqrt)((0, _sub.sub)(newAccumulatedMeanSquare, (0, _add.add)((0, _square.square)(newAccumulatedMeanGrad), this.epsilon))));
                    const newAccumulatedMoments = (0, _add.add)((0, _mul.mul)(accumulatedMoments, this.momentum), gradContribution);
                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
                    accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
                    accumulatedMoments.assign(newAccumulatedMoments);
                    const newValue = (0, _sub.sub)(value, newAccumulatedMoments);
                    value.assign(newValue);
                } else {
                    // Plain gradient
                    const newAccumulatedMeanSquare = (0, _add.add)((0, _mul.mul)(accumulatedMeanSquare, this.decay), (0, _mul.mul)((0, _square.square)(gradient), 1 - this.decay));
                    const newAccumulatedMoments = (0, _add.add)((0, _mul.mul)(accumulatedMoments, this.momentum), (0, _div.div)((0, _mul.mul)(gradient, this.learningRate), (0, _sqrt.sqrt)((0, _add.add)(newAccumulatedMeanSquare, this.epsilon))));
                    accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
                    accumulatedMoments.assign(newAccumulatedMoments);
                    const newValue = (0, _sub.sub)(value, newAccumulatedMoments);
                    value.assign(newValue);
                }
            });
        });
        this.incrementIterations();
    }
    dispose() {
        if (this.accumulatedMeanSquares != null) (0, _globals.dispose)(this.accumulatedMeanSquares.map((v)=>v.variable));
        if (this.accumulatedMeanGrads != null && this.centered) (0, _globals.dispose)(this.accumulatedMeanGrads.map((v)=>v.variable));
        if (this.accumulatedMoments != null) (0, _globals.dispose)(this.accumulatedMoments.map((v)=>v.variable));
    }
    async getWeights() {
        // Order matters for Python compatibility.
        const variables = [
            ...this.accumulatedMeanSquares,
            ...this.accumulatedMoments
        ];
        if (this.centered) variables.push(...this.accumulatedMeanGrads);
        return [
            await this.saveIterations()
        ].concat(variables.map((v)=>({
                name: v.originalName,
                tensor: v.variable
            })));
    }
    async setWeights(weightValues) {
        weightValues = await this.extractIterations(weightValues);
        const variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
        const trainable = false;
        this.accumulatedMeanSquares = weightValues.slice(0, variableCount).map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        this.accumulatedMoments = weightValues.slice(variableCount, variableCount * 2).map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
        if (this.centered) this.accumulatedMeanGrads = weightValues.slice(variableCount * 2, variableCount * 3).map((v)=>({
                originalName: v.name,
                variable: v.tensor.variable(trainable)
            }));
    }
    getConfig() {
        return {
            "learningRate": this.learningRate,
            "decay": this.decay,
            "momentum": this.momentum,
            "epsilon": this.epsilon,
            "centered": this.centered
        };
    }
    /** @nocollapse */ static fromConfig(cls, config) {
        return new cls(config["learningRate"], config["decay"], config["momentum"], config["epsilon"], config["centered"]);
    }
}

},{"../engine":"1g2qo","../globals":"eO1Ja","../ops/add":"aynh7","../ops/div":"5ZT1h","../ops/mul":"fOWrC","../ops/sqrt":"cOGi9","../ops/square":"cOosW","../ops/sub":"jBBQ2","../ops/zeros_like":"1Xjct","./optimizer":"1b69z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ATETN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // base.ts is tfjs-core without auto registration of things like flags,
// gradients, chained ops or the opHandler. See base_side_effects.ts for parts
// tfjs core that are required side effects.
/**
 * @fileoverview
 * @suppress {partialAlias} Optimization disabled due to passing the module
 * object into a function below:
 *
 *   import * as ops from './ops/ops';
 *   setOpHandler(ops);
 */ // Serialization.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "AdadeltaOptimizer", ()=>(0, _adadeltaOptimizer.AdadeltaOptimizer));
parcelHelpers.export(exports, "AdagradOptimizer", ()=>(0, _adagradOptimizer.AdagradOptimizer));
parcelHelpers.export(exports, "AdamOptimizer", ()=>(0, _adamOptimizer.AdamOptimizer));
parcelHelpers.export(exports, "AdamaxOptimizer", ()=>(0, _adamaxOptimizer.AdamaxOptimizer));
parcelHelpers.export(exports, "MomentumOptimizer", ()=>(0, _momentumOptimizer.MomentumOptimizer));
parcelHelpers.export(exports, "Optimizer", ()=>(0, _optimizer.Optimizer));
// Optimizers.
parcelHelpers.export(exports, "OptimizerConstructors", ()=>(0, _optimizerConstructors.OptimizerConstructors));
parcelHelpers.export(exports, "RMSPropOptimizer", ()=>(0, _rmspropOptimizer.RMSPropOptimizer));
parcelHelpers.export(exports, "SGDOptimizer", ()=>(0, _sgdOptimizer.SGDOptimizer));
parcelHelpers.export(exports, "Tensor", ()=>(0, _tensor.Tensor));
parcelHelpers.export(exports, "TensorBuffer", ()=>(0, _tensor.TensorBuffer));
parcelHelpers.export(exports, "Variable", ()=>(0, _tensor.Variable));
parcelHelpers.export(exports, "Rank", ()=>(0, _types.Rank));
parcelHelpers.export(exports, "sumOutType", ()=>(0, _types.sumOutType));
parcelHelpers.export(exports, "upcastType", ()=>(0, _types.upcastType));
parcelHelpers.export(exports, "Reduction", ()=>(0, _lossOpsUtils.Reduction));
parcelHelpers.export(exports, "customGrad", ()=>(0, _gradients.customGrad));
parcelHelpers.export(exports, "grad", ()=>(0, _gradients.grad));
parcelHelpers.export(exports, "grads", ()=>(0, _gradients.grads));
parcelHelpers.export(exports, "valueAndGrad", ()=>(0, _gradients.valueAndGrad));
parcelHelpers.export(exports, "valueAndGrads", ()=>(0, _gradients.valueAndGrads));
parcelHelpers.export(exports, "variableGrads", ()=>(0, _gradients.variableGrads));
parcelHelpers.export(exports, "Environment", ()=>(0, _environment.Environment));
parcelHelpers.export(exports, "env", ()=>(0, _environment.env));
parcelHelpers.export(exports, "ENV", ()=>(0, _environment.ENV));
// Top-level method exports.
parcelHelpers.export(exports, "nextFrame", ()=>(0, _browserUtil.nextFrame));
// Backend specific.
parcelHelpers.export(exports, "KernelBackend", ()=>(0, _backend.KernelBackend));
parcelHelpers.export(exports, "DataStorage", ()=>(0, _backend.DataStorage));
parcelHelpers.export(exports, "version_core", ()=>(0, _version.version));
parcelHelpers.export(exports, "browser", ()=>_browser);
parcelHelpers.export(exports, "io", ()=>_io);
parcelHelpers.export(exports, "math", ()=>_math);
parcelHelpers.export(exports, "serialization", ()=>_serialization);
parcelHelpers.export(exports, "test_util", ()=>_testUtil);
parcelHelpers.export(exports, "util", ()=>_util);
parcelHelpers.export(exports, "backend_util", ()=>_backendUtil);
parcelHelpers.export(exports, "broadcast_util", ()=>_broadcastUtil);
parcelHelpers.export(exports, "tensor_util", ()=>_tensorUtil);
parcelHelpers.export(exports, "slice_util", ()=>_sliceUtil);
parcelHelpers.export(exports, "gather_util", ()=>_gatherNdUtil);
parcelHelpers.export(exports, "scatter_util", ()=>_scatterNdUtil);
parcelHelpers.export(exports, "device_util", ()=>_deviceUtil);
parcelHelpers.export(exports, "kernel_impls", ()=>_kernelImpls);
var _io = require("./io/io");
var _math = require("./math");
var _broadcastUtil = require("./ops/broadcast_util");
var _browser = require("./ops/browser");
var _gatherNdUtil = require("./ops/gather_nd_util");
var _scatterNdUtil = require("./ops/scatter_nd_util");
var _sliceUtil = require("./ops/slice_util");
var _serialization = require("./serialization");
var _tensorUtil = require("./tensor_util");
var _testUtil = require("./test_util");
var _util = require("./util");
var _version = require("./version");
var _adadeltaOptimizer = require("./optimizers/adadelta_optimizer");
var _adagradOptimizer = require("./optimizers/adagrad_optimizer");
var _adamOptimizer = require("./optimizers/adam_optimizer");
var _adamaxOptimizer = require("./optimizers/adamax_optimizer");
var _momentumOptimizer = require("./optimizers/momentum_optimizer");
var _optimizer = require("./optimizers/optimizer");
var _optimizerConstructors = require("./optimizers/optimizer_constructors");
var _rmspropOptimizer = require("./optimizers/rmsprop_optimizer");
var _sgdOptimizer = require("./optimizers/sgd_optimizer");
var _tensor = require("./tensor");
var _types = require("./types");
var _ops = require("./ops/ops");
parcelHelpers.exportAll(_ops, exports);
var _lossOpsUtils = require("./ops/loss_ops_utils");
var _train = require("./train");
parcelHelpers.exportAll(_train, exports);
var _globals = require("./globals");
parcelHelpers.exportAll(_globals, exports);
var _kernelRegistry = require("./kernel_registry");
parcelHelpers.exportAll(_kernelRegistry, exports);
var _gradients = require("./gradients");
var _environment = require("./environment");
var _browserUtil = require("./browser_util");
// Second level exports.
var _backendUtil = require("./backends/backend_util");
var _deviceUtil = require("./device_util");
var _kernelImpls = require("./backends/kernel_impls");
var _backend = require("./backends/backend");
// Export all kernel names / info.
var _kernelNames = require("./kernel_names");
parcelHelpers.exportAll(_kernelNames, exports);

},{"./io/io":"d1chw","./math":"8ony7","./ops/broadcast_util":"eLo4J","./ops/browser":"iaaSH","./ops/gather_nd_util":"4RUOy","./ops/scatter_nd_util":"dDUQ5","./ops/slice_util":"3A9Ql","./serialization":"dv6JX","./tensor_util":"8jzJG","./test_util":"3IEFi","./util":"ctavo","./version":"5jZGL","./optimizers/adadelta_optimizer":"a2jsK","./optimizers/adagrad_optimizer":"ddRDd","./optimizers/adam_optimizer":"5uefH","./optimizers/adamax_optimizer":"72DSu","./optimizers/momentum_optimizer":"bKhX1","./optimizers/optimizer":"1b69z","./optimizers/optimizer_constructors":"eaK8y","./optimizers/rmsprop_optimizer":"66Wvy","./optimizers/sgd_optimizer":"4dxnr","./tensor":"cJNoh","./types":"10R8n","./ops/ops":"13k1e","./ops/loss_ops_utils":"6wY9M","./train":"cWOs6","./globals":"eO1Ja","./kernel_registry":"aG7g9","./gradients":"dDHpg","./environment":"dXmHm","./browser_util":"dz6Y8","./backends/backend_util":"8IvgS","./device_util":"5fozk","./backends/kernel_impls":"cylCc","./backends/backend":"dL2i3","./kernel_names":"hniYO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d1chw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Importing local_storage and indexed_db is necessary for the routers to be
// registered.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "copyModel", ()=>(0, _modelManagement.copyModel));
parcelHelpers.export(exports, "listModels", ()=>(0, _modelManagement.listModels));
parcelHelpers.export(exports, "moveModel", ()=>(0, _modelManagement.moveModel));
parcelHelpers.export(exports, "removeModel", ()=>(0, _modelManagement.removeModel));
parcelHelpers.export(exports, "browserFiles", ()=>(0, _browserFiles.browserFiles));
parcelHelpers.export(exports, "browserHTTPRequest", ()=>(0, _http.browserHTTPRequest));
parcelHelpers.export(exports, "CompositeArrayBuffer", ()=>(0, _compositeArrayBuffer.CompositeArrayBuffer));
parcelHelpers.export(exports, "concatenateArrayBuffers", ()=>(0, _ioUtils.concatenateArrayBuffers));
parcelHelpers.export(exports, "decodeWeights", ()=>(0, _ioUtils.decodeWeights));
parcelHelpers.export(exports, "decodeWeightsStream", ()=>(0, _ioUtils.decodeWeightsStream));
parcelHelpers.export(exports, "encodeWeights", ()=>(0, _ioUtils.encodeWeights));
parcelHelpers.export(exports, "fromMemory", ()=>(0, _passthrough.fromMemory));
parcelHelpers.export(exports, "fromMemorySync", ()=>(0, _passthrough.fromMemorySync));
parcelHelpers.export(exports, "getLoadHandlers", ()=>(0, _routerRegistry.getLoadHandlers));
parcelHelpers.export(exports, "getModelArtifactsForJSON", ()=>(0, _ioUtils.getModelArtifactsForJSON));
parcelHelpers.export(exports, "getModelArtifactsForJSONSync", ()=>(0, _ioUtils.getModelArtifactsForJSONSync));
parcelHelpers.export(exports, "getModelArtifactsInfoForJSON", ()=>(0, _ioUtils.getModelArtifactsInfoForJSON));
parcelHelpers.export(exports, "getSaveHandlers", ()=>(0, _routerRegistry.getSaveHandlers));
parcelHelpers.export(exports, "getWeightSpecs", ()=>(0, _ioUtils.getWeightSpecs));
parcelHelpers.export(exports, "http", ()=>(0, _http.http));
parcelHelpers.export(exports, "isHTTPScheme", ()=>(0, _http.isHTTPScheme));
parcelHelpers.export(exports, "loadWeights", ()=>(0, _weightsLoader.loadWeights));
parcelHelpers.export(exports, "registerLoadRouter", ()=>(0, _routerRegistry.registerLoadRouter));
parcelHelpers.export(exports, "registerSaveRouter", ()=>(0, _routerRegistry.registerSaveRouter));
parcelHelpers.export(exports, "weightsLoaderFactory", ()=>(0, _weightsLoader.weightsLoaderFactory));
parcelHelpers.export(exports, "withSaveHandler", ()=>(0, _passthrough.withSaveHandler));
parcelHelpers.export(exports, "withSaveHandlerSync", ()=>(0, _passthrough.withSaveHandlerSync));
var _indexedDb = require("./indexed_db");
var _localStorage = require("./local_storage");
var _browserFiles = require("./browser_files");
var _http = require("./http");
var _ioUtils = require("./io_utils");
var _passthrough = require("./passthrough");
var _routerRegistry = require("./router_registry");
var _weightsLoader = require("./weights_loader");
var _compositeArrayBuffer = require("./composite_array_buffer");
var _modelManagement = require("./model_management");

},{"./indexed_db":"aWBtv","./local_storage":"c3TPR","./browser_files":"ijOTx","./http":"aHrPE","./io_utils":"hW5iQ","./passthrough":"aUTFH","./router_registry":"btpsw","./weights_loader":"9BLSt","./composite_array_buffer":"gHH7C","./model_management":"iB0Mw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ijOTx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * IOHandlers related to files, such as browser-triggered file downloads,
 * user-selected files in browser.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "BrowserDownloads", ()=>BrowserDownloads);
parcelHelpers.export(exports, "browserDownloadsRouter", ()=>browserDownloadsRouter);
/**
 * Creates an IOHandler that triggers file downloads from the browser.
 *
 * The returned `IOHandler` instance can be used as model exporting methods such
 * as `tf.Model.save` and supports only saving.
 *
 * ```js
 * const model = tf.sequential();
 * model.add(tf.layers.dense(
 *     {units: 1, inputShape: [10], activation: 'sigmoid'}));
 * const saveResult = await model.save('downloads://mymodel');
 * // This will trigger downloading of two files:
 * //   'mymodel.json' and 'mymodel.weights.bin'.
 * console.log(saveResult);
 * ```
 *
 * @param fileNamePrefix Prefix name of the files to be downloaded. For use with
 *   `tf.Model`, `fileNamePrefix` should follow either of the following two
 *   formats:
 *   1. `null` or `undefined`, in which case the default file
 *      names will be used:
 *      - 'model.json' for the JSON file containing the model topology and
 *        weights manifest.
 *      - 'model.weights.bin' for the binary file containing the binary weight
 *        values.
 *   2. A single string or an Array of a single string, as the file name prefix.
 *      For example, if `'foo'` is provided, the downloaded JSON
 *      file and binary weights file will be named 'foo.json' and
 *      'foo.weights.bin', respectively.
 * @param config Additional configuration for triggering downloads.
 * @returns An instance of `BrowserDownloads` `IOHandler`.
 *
 * @doc {
 *   heading: 'Models',
 *   subheading: 'Loading',
 *   namespace: 'io',
 *   ignoreCI: true
 * }
 */ parcelHelpers.export(exports, "browserDownloads", ()=>browserDownloads);
/**
 * Creates an IOHandler that loads model artifacts from user-selected files.
 *
 * This method can be used for loading from files such as user-selected files
 * in the browser.
 * When used in conjunction with `tf.loadLayersModel`, an instance of
 * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
 *
 * ```js
 * // Note: This code snippet won't run properly without the actual file input
 * //   elements in the HTML DOM.
 *
 * // Suppose there are two HTML file input (`<input type="file" ...>`)
 * // elements.
 * const uploadJSONInput = document.getElementById('upload-json');
 * const uploadWeightsInput = document.getElementById('upload-weights');
 * const model = await tf.loadLayersModel(tf.io.browserFiles(
 *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
 * ```
 *
 * @param files `File`s to load from. Currently, this function supports only
 *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for
 *   which an `Array` of `File`s is expected (in that order):
 *   - A JSON file containing the model topology and weight manifest.
 *   - Optionally, one or more binary files containing the binary weights.
 *     These files must have names that match the paths in the `weightsManifest`
 *     contained by the aforementioned JSON file, or errors will be thrown
 *     during loading. These weights files have the same format as the ones
 *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`
 *     Python PIP package. If no weights files are provided, only the model
 *     topology will be loaded from the JSON file above.
 * @returns An instance of `Files` `IOHandler`.
 *
 * @doc {
 *   heading: 'Models',
 *   subheading: 'Loading',
 *   namespace: 'io',
 *   ignoreCI: true
 * }
 */ parcelHelpers.export(exports, "browserFiles", ()=>browserFiles);
var _flags = require("../flags");
var _environment = require("../environment");
var _ioUtils = require("./io_utils");
var _routerRegistry = require("./router_registry");
var _compositeArrayBuffer = require("./composite_array_buffer");
const DEFAULT_FILE_NAME_PREFIX = "model";
const DEFAULT_JSON_EXTENSION_NAME = ".json";
const DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
function defer(f) {
    return new Promise((resolve)=>setTimeout(resolve)).then(f);
}
class BrowserDownloads {
    constructor(fileNamePrefix){
        if (!(0, _environment.env)().getBool("IS_BROWSER")) // TODO(cais): Provide info on what IOHandlers are available under the
        //   current environment.
        throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
        if (fileNamePrefix == null || fileNamePrefix.length === 0) fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
        this.modelJsonFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
        this.weightDataFileName = fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
    }
    async save(modelArtifacts) {
        if (typeof document === "undefined") throw new Error("Browser downloads are not supported in this environment since `document` is not present");
        // TODO(mattsoulanille): Support saving models over 2GB that exceed
        // Chrome's ArrayBuffer size limit.
        const weightBuffer = (0, _compositeArrayBuffer.CompositeArrayBuffer).join(modelArtifacts.weightData);
        const weightsURL = window.URL.createObjectURL(new Blob([
            weightBuffer
        ], {
            type: "application/octet-stream"
        }));
        if (modelArtifacts.modelTopology instanceof ArrayBuffer) throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");
        else {
            const weightsManifest = [
                {
                    paths: [
                        "./" + this.weightDataFileName
                    ],
                    weights: modelArtifacts.weightSpecs
                }
            ];
            const modelJSON = (0, _ioUtils.getModelJSONForModelArtifacts)(modelArtifacts, weightsManifest);
            const modelJsonURL = window.URL.createObjectURL(new Blob([
                JSON.stringify(modelJSON)
            ], {
                type: "application/json"
            }));
            // If anchor elements are not provided, create them without attaching them
            // to parents, so that the downloaded file names can be controlled.
            const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
            jsonAnchor.download = this.modelJsonFileName;
            jsonAnchor.href = modelJsonURL;
            // Trigger downloads by evoking a click event on the download anchors.
            // When multiple downloads are started synchronously, Firefox will only
            // save the last one.
            await defer(()=>jsonAnchor.dispatchEvent(new MouseEvent("click")));
            if (modelArtifacts.weightData != null) {
                const weightDataAnchor = this.weightDataAnchor == null ? document.createElement("a") : this.weightDataAnchor;
                weightDataAnchor.download = this.weightDataFileName;
                weightDataAnchor.href = weightsURL;
                await defer(()=>weightDataAnchor.dispatchEvent(new MouseEvent("click")));
            }
            return {
                modelArtifactsInfo: (0, _ioUtils.getModelArtifactsInfoForJSON)(modelArtifacts)
            };
        }
    }
}
BrowserDownloads.URL_SCHEME = "downloads://";
class BrowserFiles {
    constructor(files){
        if (files == null || files.length < 1) throw new Error(`When calling browserFiles, at least 1 file is required, ` + `but received ${files}`);
        this.jsonFile = files[0];
        this.weightsFiles = files.slice(1);
    }
    async load() {
        return new Promise((resolve, reject)=>{
            const jsonReader = new FileReader();
            jsonReader.onload = (event)=>{
                // tslint:disable-next-line:no-any
                const modelJSON = JSON.parse(event.target.result);
                const modelTopology = modelJSON.modelTopology;
                if (modelTopology == null) {
                    reject(new Error(`modelTopology field is missing from file ${this.jsonFile.name}`));
                    return;
                }
                const weightsManifest = modelJSON.weightsManifest;
                if (weightsManifest == null) {
                    reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
                    return;
                }
                if (this.weightsFiles.length === 0) {
                    resolve({
                        modelTopology
                    });
                    return;
                }
                const modelArtifactsPromise = (0, _ioUtils.getModelArtifactsForJSON)(modelJSON, (weightsManifest)=>this.loadWeights(weightsManifest));
                resolve(modelArtifactsPromise);
            };
            jsonReader.onerror = (error)=>reject(`Failed to read model topology and weights manifest JSON ` + `from file '${this.jsonFile.name}'. BrowserFiles supports loading ` + `Keras-style tf.Model artifacts only.`);
            jsonReader.readAsText(this.jsonFile);
        });
    }
    loadWeights(weightsManifest) {
        const weightSpecs = [];
        const paths = [];
        for (const entry of weightsManifest){
            weightSpecs.push(...entry.weights);
            paths.push(...entry.paths);
        }
        const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);
        const promises = paths.map((path)=>this.loadWeightsFile(path, pathToFile[path]));
        return Promise.all(promises).then((buffers)=>[
                weightSpecs,
                buffers
            ]);
    }
    loadWeightsFile(path, file) {
        return new Promise((resolve, reject)=>{
            const weightFileReader = new FileReader();
            weightFileReader.onload = (event)=>{
                // tslint:disable-next-line:no-any
                const weightData = event.target.result;
                resolve(weightData);
            };
            weightFileReader.onerror = (error)=>reject(`Failed to weights data from file of path '${path}'.`);
            weightFileReader.readAsArrayBuffer(file);
        });
    }
    /**
     * Check the compatibility between weights manifest and weight files.
     */ checkManifestAndWeightFiles(manifest) {
        const basenames = [];
        const fileNames = this.weightsFiles.map((file)=>(0, _ioUtils.basename)(file.name));
        const pathToFile = {};
        for (const group of manifest)group.paths.forEach((path)=>{
            const pathBasename = (0, _ioUtils.basename)(path);
            if (basenames.indexOf(pathBasename) !== -1) throw new Error(`Duplicate file basename found in weights manifest: ` + `'${pathBasename}'`);
            basenames.push(pathBasename);
            if (fileNames.indexOf(pathBasename) === -1) throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
            else pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];
        });
        if (basenames.length !== this.weightsFiles.length) throw new Error(`Mismatch in the number of files in weights manifest ` + `(${basenames.length}) and the number of weight files provided ` + `(${this.weightsFiles.length}).`);
        return pathToFile;
    }
}
const browserDownloadsRouter = (url)=>{
    if (!(0, _environment.env)().getBool("IS_BROWSER")) return null;
    else {
        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
        else return null;
    }
};
(0, _routerRegistry.IORouterRegistry).registerSaveRouter(browserDownloadsRouter);
function browserDownloads(fileNamePrefix = "model") {
    return new BrowserDownloads(fileNamePrefix);
}
function browserFiles(files) {
    return new BrowserFiles(files);
}

},{"../flags":"5yJGC","../environment":"dXmHm","./io_utils":"hW5iQ","./router_registry":"btpsw","./composite_array_buffer":"gHH7C","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aHrPE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * IOHandler implementations based on HTTP requests in the web browser.
 *
 * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "HTTPRequest", ()=>HTTPRequest);
/**
 * Extract the prefix and suffix of the url, where the prefix is the path before
 * the last file, and suffix is the search params after the last file.
 * ```
 * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'
 * [prefix, suffix] = parseUrl(url)
 * // prefix = 'http://tfhub.dev/model/1/'
 * // suffix = '?tfjs-format=file'
 * ```
 * @param url the model url to be parsed.
 */ parcelHelpers.export(exports, "parseUrl", ()=>parseUrl);
parcelHelpers.export(exports, "isHTTPScheme", ()=>isHTTPScheme);
parcelHelpers.export(exports, "httpRouter", ()=>httpRouter);
/**
 * Creates an IOHandler subtype that sends model artifacts to HTTP server.
 *
 * An HTTP request of the `multipart/form-data` mime type will be sent to the
 * `path` URL. The form data includes artifacts that represent the topology
 * and/or weights of the model. In the case of Keras-style `tf.Model`, two
 * blobs (files) exist in form-data:
 *   - A JSON file consisting of `modelTopology` and `weightsManifest`.
 *   - A binary weights file consisting of the concatenated weight values.
 * These files are in the same format as the one generated by
 * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
 *
 * The following code snippet exemplifies the client-side code that uses this
 * function:
 *
 * ```js
 * const model = tf.sequential();
 * model.add(
 *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
 *
 * const saveResult = await model.save(tf.io.http(
 *     'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
 * console.log(saveResult);
 * ```
 *
 * If the default `POST` method is to be used, without any custom parameters
 * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
 *
 * ```js
 * const saveResult = await model.save('http://model-server:5000/upload');
 * ```
 *
 * The following GitHub Gist
 * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
 * implements a server based on [flask](https://github.com/pallets/flask) that
 * can receive the request. Upon receiving the model artifacts via the requst,
 * this particular server reconstitutes instances of [Keras
 * Models](https://keras.io/models/model/) in memory.
 *
 *
 * @param path A URL path to the model.
 *   Can be an absolute HTTP path (e.g.,
 *   'http://localhost:8000/model-upload)') or a relative path (e.g.,
 *   './model-upload').
 * @param requestInit Request configurations to be used when sending
 *    HTTP request to server using `fetch`. It can contain fields such as
 *    `method`, `credentials`, `headers`, `mode`, etc. See
 *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
 *    for more information. `requestInit` must not have a body, because the
 * body will be set by TensorFlow.js. File blobs representing the model
 * topology (filename: 'model.json') and the weights of the model (filename:
 * 'model.weights.bin') will be appended to the body. If `requestInit` has a
 * `body`, an Error will be thrown.
 * @param loadOptions Optional configuration for the loading. It includes the
 *   following fields:
 *   - weightPathPrefix Optional, this specifies the path prefix for weight
 *     files, by default this is calculated from the path param.
 *   - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
 *     the `fetch` from node-fetch can be used here.
 *   - onProgress Optional, progress callback function, fired periodically
 *     before the load is completed.
 * @returns An instance of `IOHandler`.
 *
 * @doc {
 *   heading: 'Models',
 *   subheading: 'Loading',
 *   namespace: 'io',
 *   ignoreCI: true
 * }
 */ parcelHelpers.export(exports, "http", ()=>http);
/**
 * Deprecated. Use `tf.io.http`.
 * @param path
 * @param loadOptions
 */ parcelHelpers.export(exports, "browserHTTPRequest", ()=>browserHTTPRequest);
var _environment = require("../environment");
var _util = require("../util");
var _ioUtils = require("./io_utils");
var _compositeArrayBuffer = require("./composite_array_buffer");
var _routerRegistry = require("./router_registry");
var _weightsLoader = require("./weights_loader");
const OCTET_STREAM_MIME_TYPE = "application/octet-stream";
const JSON_TYPE = "application/json";
class HTTPRequest {
    constructor(path, loadOptions){
        this.DEFAULT_METHOD = "POST";
        if (loadOptions == null) loadOptions = {};
        this.weightPathPrefix = loadOptions.weightPathPrefix;
        this.weightUrlConverter = loadOptions.weightUrlConverter;
        if (loadOptions.fetchFunc != null) {
            (0, _util.assert)(typeof loadOptions.fetchFunc === "function", ()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)");
            this.fetch = loadOptions.fetchFunc;
        } else this.fetch = (0, _environment.env)().platform.fetch;
        (0, _util.assert)(path != null && path.length > 0, ()=>"URL path for http must not be null, undefined or empty.");
        if (Array.isArray(path)) (0, _util.assert)(path.length === 2, ()=>"URL paths for http must have a length of 2, " + `(actual length is ${path.length}).`);
        this.path = path;
        if (loadOptions.requestInit != null && loadOptions.requestInit.body != null) throw new Error("requestInit is expected to have no pre-existing body, but has one.");
        this.requestInit = loadOptions.requestInit || {};
        this.loadOptions = loadOptions;
    }
    async save(modelArtifacts) {
        if (modelArtifacts.modelTopology instanceof ArrayBuffer) throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
        const init = Object.assign({
            method: this.DEFAULT_METHOD
        }, this.requestInit);
        init.body = new FormData();
        const weightsManifest = [
            {
                paths: [
                    "./model.weights.bin"
                ],
                weights: modelArtifacts.weightSpecs
            }
        ];
        const modelTopologyAndWeightManifest = (0, _ioUtils.getModelJSONForModelArtifacts)(modelArtifacts, weightsManifest);
        init.body.append("model.json", new Blob([
            JSON.stringify(modelTopologyAndWeightManifest)
        ], {
            type: JSON_TYPE
        }), "model.json");
        if (modelArtifacts.weightData != null) {
            // TODO(mattsoulanille): Support saving models over 2GB that exceed
            // Chrome's ArrayBuffer size limit.
            const weightBuffer = (0, _compositeArrayBuffer.CompositeArrayBuffer).join(modelArtifacts.weightData);
            init.body.append("model.weights.bin", new Blob([
                weightBuffer
            ], {
                type: OCTET_STREAM_MIME_TYPE
            }), "model.weights.bin");
        }
        const response = await this.fetch(this.path, init);
        if (response.ok) return {
            modelArtifactsInfo: (0, _ioUtils.getModelArtifactsInfoForJSON)(modelArtifacts),
            responses: [
                response
            ]
        };
        else throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` + `${response.status}.`);
    }
    async loadModelJSON() {
        const modelConfigRequest = await this.fetch(this.path, this.requestInit);
        if (!modelConfigRequest.ok) throw new Error(`Request to ${this.path} failed with status code ` + `${modelConfigRequest.status}. Please verify this URL points to ` + `the model JSON of the model to load.`);
        let modelJSON;
        try {
            modelJSON = await modelConfigRequest.json();
        } catch (e) {
            let message = `Failed to parse model JSON of response from ${this.path}.`;
            // TODO(nsthorat): Remove this after some time when we're comfortable that
            // .pb files are mostly gone.
            if (this.path.endsWith(".pb")) message += " Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. You can re-convert your Python TensorFlow model using the TensorFlow.js 1.0 conversion scripts or you can convert your.pb models with the 'pb2json'NPM script in the tensorflow/tfjs-converter repository.";
            else message += " Please make sure the server is serving valid JSON for this request.";
            throw new Error(message);
        }
        // We do not allow both modelTopology and weightsManifest to be missing.
        const modelTopology = modelJSON.modelTopology;
        const weightsManifest = modelJSON.weightsManifest;
        if (modelTopology == null && weightsManifest == null) throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` + `topology or manifest for weights.`);
        return modelJSON;
    }
    /**
     * Load model artifacts via HTTP request(s).
     *
     * See the documentation to `tf.io.http` for details on the saved
     * artifacts.
     *
     * @returns The loaded model artifacts (if loading succeeds).
     */ async load() {
        if (this.loadOptions.streamWeights) return this.loadStream();
        const modelJSON = await this.loadModelJSON();
        return (0, _ioUtils.getModelArtifactsForJSON)(modelJSON, (weightsManifest)=>this.loadWeights(weightsManifest));
    }
    async loadStream() {
        const modelJSON = await this.loadModelJSON();
        const fetchURLs = await this.getWeightUrls(modelJSON.weightsManifest);
        const weightSpecs = (0, _ioUtils.getWeightSpecs)(modelJSON.weightsManifest);
        const stream = ()=>(0, _weightsLoader.streamWeights)(fetchURLs, this.loadOptions);
        return Object.assign(Object.assign({}, modelJSON), {
            weightSpecs,
            getWeightStream: stream
        });
    }
    async getWeightUrls(weightsManifest) {
        const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
        const [prefix, suffix] = parseUrl(weightPath);
        const pathPrefix = this.weightPathPrefix || prefix;
        const fetchURLs = [];
        const urlPromises = [];
        for (const weightsGroup of weightsManifest){
            for (const path of weightsGroup.paths)if (this.weightUrlConverter != null) urlPromises.push(this.weightUrlConverter(path));
            else fetchURLs.push(pathPrefix + path + suffix);
        }
        if (this.weightUrlConverter) fetchURLs.push(...await Promise.all(urlPromises));
        return fetchURLs;
    }
    async loadWeights(weightsManifest) {
        const fetchURLs = await this.getWeightUrls(weightsManifest);
        const weightSpecs = (0, _ioUtils.getWeightSpecs)(weightsManifest);
        const buffers = await (0, _weightsLoader.loadWeightsAsArrayBuffer)(fetchURLs, this.loadOptions);
        return [
            weightSpecs,
            buffers
        ];
    }
}
HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
function parseUrl(url) {
    const lastSlash = url.lastIndexOf("/");
    const lastSearchParam = url.lastIndexOf("?");
    const prefix = url.substring(0, lastSlash);
    const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : "";
    return [
        prefix + "/",
        suffix
    ];
}
function isHTTPScheme(url) {
    return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
}
const httpRouter = (url, loadOptions)=>{
    if (typeof fetch === "undefined" && (loadOptions == null || loadOptions.fetchFunc == null)) // `http` uses `fetch` or `node-fetch`, if one wants to use it in
    // an environment that is not the browser or node they have to setup a
    // global fetch polyfill.
    return null;
    else {
        let isHTTP = true;
        if (Array.isArray(url)) isHTTP = url.every((urlItem)=>isHTTPScheme(urlItem));
        else isHTTP = isHTTPScheme(url);
        if (isHTTP) return http(url, loadOptions);
    }
    return null;
};
(0, _routerRegistry.IORouterRegistry).registerSaveRouter(httpRouter);
(0, _routerRegistry.IORouterRegistry).registerLoadRouter(httpRouter);
function http(path, loadOptions) {
    return new HTTPRequest(path, loadOptions);
}
function browserHTTPRequest(path, loadOptions) {
    return http(path, loadOptions);
}

},{"../environment":"dXmHm","../util":"ctavo","./io_utils":"hW5iQ","./composite_array_buffer":"gHH7C","./router_registry":"btpsw","./weights_loader":"9BLSt","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9BLSt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Reads binary weights data from a number of URLs.
 *
 * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.
 * @param requestOptions RequestInit (options) for the HTTP requests.
 * @param fetchFunc Optional overriding value for the `window.fetch` function.
 * @param onProgress Optional, progress callback function, fired periodically
 *   before the load is completed.
 * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same
 *   length as `fetchURLs`.
 */ parcelHelpers.export(exports, "loadWeightsAsArrayBuffer", ()=>loadWeightsAsArrayBuffer);
parcelHelpers.export(exports, "streamWeights", ()=>streamWeights);
/**
 * Reads a weights manifest JSON configuration, fetches the weights and
 * returns them as `Tensor`s.
 *
 * @param manifest The weights manifest JSON.
 * @param filePathPrefix The path prefix for filenames given in the manifest.
 *     Defaults to the empty string.
 * @param weightNames The names of the weights to be fetched.
 */ parcelHelpers.export(exports, "loadWeights", ()=>loadWeights);
/**
 * Creates a function, which reads a weights manifest JSON configuration,
 * fetches the weight files using the specified function and returns them as
 * `Tensor`s.
 *
 * ```js
 * // example for creating a nodejs weight loader, which reads the weight files
 * // from disk using fs.readFileSync
 *
 * import * as fs from 'fs'
 *
 * const fetchWeightsFromDisk = (filePaths: string[]) =>
 *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)
 *
 * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
 *
 * const manifest = JSON.parse(
 *   fs.readFileSync('./my_model-weights_manifest').toString()
 * )
 * const weightMap = await loadWeights(manifest, './')
 * ```
 * @param fetchWeightsFunction The function used for fetching the weight files.
 * @returns Weight loading function.
 */ parcelHelpers.export(exports, "weightsLoaderFactory", ()=>weightsLoaderFactory);
var _environment = require("../environment");
var _util = require("../util");
var _compositeArrayBuffer = require("./composite_array_buffer");
var _ioUtils = require("./io_utils");
var _progress = require("./progress");
var _types = require("./types");
async function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) {
    if (loadOptions == null) loadOptions = {};
    const fetchFunc = loadOptions.fetchFunc == null ? (0, _environment.env)().platform.fetch : loadOptions.fetchFunc;
    // Create the requests for all of the weights in parallel.
    const requests = fetchURLs.map((fetchURL)=>fetchFunc(fetchURL, loadOptions.requestInit, {
            isBinary: true
        }));
    const fetchStartFraction = 0;
    const fetchEndFraction = 0.5;
    const responses = loadOptions.onProgress == null ? await Promise.all(requests) : await (0, _progress.monitorPromisesProgress)(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction);
    const bufferPromises = responses.map((response)=>response.arrayBuffer());
    const bufferStartFraction = 0.5;
    const bufferEndFraction = 1;
    const buffers = loadOptions.onProgress == null ? await Promise.all(bufferPromises) : await (0, _progress.monitorPromisesProgress)(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction);
    return buffers;
}
function streamWeights(fetchURLs, loadOptions) {
    var _a;
    const fetchFunc = loadOptions.fetchFunc == null ? (0, _environment.env)().platform.fetch : loadOptions.fetchFunc;
    let fetchIndex = 0;
    let chunkReader;
    (_a = loadOptions.onProgress) === null || _a === void 0 || _a.call(loadOptions, 0);
    return new ReadableStream({
        pull: async (controller)=>{
            var _a;
            while(fetchIndex < fetchURLs.length){
                if (!chunkReader) {
                    const body = (await fetchFunc(fetchURLs[fetchIndex], loadOptions.requestInit, {
                        isBinary: true
                    })).body;
                    chunkReader = body.getReader();
                }
                const { done, value } = await chunkReader.read();
                if (done) {
                    fetchIndex++;
                    chunkReader = undefined;
                    (_a = loadOptions.onProgress) === null || _a === void 0 || _a.call(loadOptions, fetchIndex / fetchURLs.length);
                    continue;
                }
                controller.enqueue(value);
                return;
            }
            controller.close();
        }
    });
}
async function loadWeights(manifest, filePathPrefix = "", weightNames, requestInit) {
    // TODO(nsthorat): Groups are currently fetched atomically. If you need a
    // single weight from a group, the whole group will be fetched. At a future
    // date, we should support fetching only the individual shards within a
    // group that are needed to reconstruct the requested weight.
    // TODO(cais): Use `decodeWeights` for implementation.
    const fetchWeights = (fetchUrls)=>loadWeightsAsArrayBuffer(fetchUrls, {
            requestInit
        });
    const loadWeights = weightsLoaderFactory(fetchWeights);
    return loadWeights(manifest, filePathPrefix, weightNames);
}
function weightsLoaderFactory(fetchWeightsFunction) {
    return async (manifest, filePathPrefix = "", weightNames)=>{
        // Collect all the groups, weights, and their relative offsets to be
        // fetched.
        const groupIndicesToFetchMap = manifest.map(()=>false);
        const groupWeightsToFetch = {};
        const weightsFound = weightNames != null ? weightNames.map(()=>false) : [];
        const allManifestWeightNames = [];
        manifest.forEach((manifestGroupConfig, groupIndex)=>{
            let groupOffset = 0;
            manifestGroupConfig.weights.forEach((weightsEntry)=>{
                const rawDtype = "quantization" in weightsEntry ? weightsEntry.quantization.dtype : weightsEntry.dtype;
                const weightsBytes = (0, _types.DTYPE_VALUE_SIZE_MAP)[rawDtype] * _util.sizeFromShape(weightsEntry.shape);
                const enqueueWeightsForFetchingFn = ()=>{
                    groupIndicesToFetchMap[groupIndex] = true;
                    if (groupWeightsToFetch[groupIndex] == null) groupWeightsToFetch[groupIndex] = [];
                    groupWeightsToFetch[groupIndex].push({
                        manifestEntry: weightsEntry,
                        groupOffset,
                        sizeBytes: weightsBytes
                    });
                };
                if (weightNames != null) weightNames.forEach((weightName, weightIndex)=>{
                    if (weightName === weightsEntry.name) {
                        enqueueWeightsForFetchingFn();
                        weightsFound[weightIndex] = true;
                    }
                });
                else enqueueWeightsForFetchingFn();
                allManifestWeightNames.push(weightsEntry.name);
                groupOffset += weightsBytes;
            });
        });
        if (!weightsFound.every((found)=>found)) {
            const weightsNotFound = weightNames.filter((_, i)=>!weightsFound[i]);
            throw new Error(`Could not find weights in manifest with names: ` + `${weightsNotFound.join(", ")}. \n` + `Manifest JSON has weights with names: ` + `${allManifestWeightNames.join(", ")}.`);
        }
        // Convert the one-hot boolean groupId => shouldFetch map to a list of group
        // IDs.
        const groupIndicesToFetch = groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i)=>{
            if (shouldFetch) accumulator.push(i);
            return accumulator;
        }, []);
        const fetchUrls = [];
        groupIndicesToFetch.forEach((i)=>{
            manifest[i].paths.forEach((filepath)=>{
                const fetchUrl = filePathPrefix + (!filePathPrefix.endsWith("/") ? "/" : "") + filepath;
                fetchUrls.push(fetchUrl);
            });
        });
        const buffers = await fetchWeightsFunction(fetchUrls);
        const weightsTensorMap = {};
        let bufferIndexOffset = 0;
        groupIndicesToFetch.forEach((i)=>{
            const numBuffers = manifest[i].paths.length;
            const weightsBuffer = new (0, _compositeArrayBuffer.CompositeArrayBuffer)(buffers.slice(bufferIndexOffset, bufferIndexOffset + numBuffers));
            const weightsEntries = groupWeightsToFetch[i];
            weightsEntries.forEach((weightsEntry)=>{
                const byteBuffer = weightsBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
                const nameToTensorMap = (0, _ioUtils.decodeWeights)(byteBuffer, [
                    weightsEntry.manifestEntry
                ]);
                for(const name in nameToTensorMap)weightsTensorMap[name] = nameToTensorMap[name];
            });
            bufferIndexOffset += numBuffers;
        });
        return weightsTensorMap;
    };
}

},{"../environment":"dXmHm","../util":"ctavo","./composite_array_buffer":"gHH7C","./io_utils":"hW5iQ","./progress":"O6WSZ","./types":"ixgVc","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"O6WSZ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Monitor Promise.all progress, fire onProgress callback function.
 *
 * @param promises Promise list going to be monitored
 * @param onProgress Callback function. Fired when a promise resolved.
 * @param startFraction Optional fraction start. Default to 0.
 * @param endFraction Optional fraction end. Default to 1.
 */ parcelHelpers.export(exports, "monitorPromisesProgress", ()=>monitorPromisesProgress);
var _util = require("../util");
function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) {
    checkPromises(promises);
    startFraction = startFraction == null ? 0 : startFraction;
    endFraction = endFraction == null ? 1 : endFraction;
    checkFraction(startFraction, endFraction);
    let resolvedPromise = 0;
    const registerMonitor = (promise)=>{
        promise.then((value)=>{
            const fraction = startFraction + ++resolvedPromise / promises.length * (endFraction - startFraction);
            // pass fraction as parameter to callback function.
            onProgress(fraction);
            return value;
        });
        return promise;
    };
    function checkPromises(promises) {
        (0, _util.assert)(promises != null && Array.isArray(promises) && promises.length > 0, ()=>"promises must be a none empty array");
    }
    function checkFraction(startFraction, endFraction) {
        (0, _util.assert)(startFraction >= 0 && startFraction <= 1, ()=>`Progress fraction must be in range [0, 1], but ` + `got startFraction ${startFraction}`);
        (0, _util.assert)(endFraction >= 0 && endFraction <= 1, ()=>`Progress fraction must be in range [0, 1], but ` + `got endFraction ${endFraction}`);
        (0, _util.assert)(endFraction >= startFraction, ()=>`startFraction must be no more than endFraction, but ` + `got startFraction ${startFraction} and endFraction ` + `${endFraction}`);
    }
    return Promise.all(promises.map(registerMonitor));
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aUTFH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates an IOHandler that loads model artifacts from memory.
 *
 * When used in conjunction with `tf.loadLayersModel`, an instance of
 * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
 *
 * ```js
 * const model = await tf.loadLayersModel(tf.io.fromMemory(
 *     modelTopology, weightSpecs, weightData));
 * ```
 *
 * @param modelArtifacts a object containing model topology (i.e., parsed from
 *   the JSON format).
 * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
 *   names, shapes, types, and quantization of the weight data. Optional.
 * @param weightData A single `ArrayBuffer` containing the weight data,
 *   concatenated in the order described by the weightSpecs. Optional.
 * @param trainingConfig Model training configuration. Optional.
 *
 * @returns A passthrough `IOHandler` that simply loads the provided data.
 */ parcelHelpers.export(exports, "fromMemory", ()=>fromMemory);
/**
 * Creates an IOHandler that loads model artifacts from memory.
 *
 * When used in conjunction with `tf.loadLayersModel`, an instance of
 * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts.
 *
 * ```js
 * const model = await tf.loadLayersModel(tf.io.fromMemory(
 *     modelTopology, weightSpecs, weightData));
 * ```
 *
 * @param modelArtifacts a object containing model topology (i.e., parsed from
 *   the JSON format).
 * @param weightSpecs An array of `WeightsManifestEntry` objects describing the
 *   names, shapes, types, and quantization of the weight data. Optional.
 * @param weightData A single `ArrayBuffer` containing the weight data,
 *   concatenated in the order described by the weightSpecs. Optional.
 * @param trainingConfig Model training configuration. Optional.
 *
 * @returns A passthrough `IOHandlerSync` that simply loads the provided data.
 */ parcelHelpers.export(exports, "fromMemorySync", ()=>fromMemorySync);
/**
 * Creates an IOHandler that passes saved model artifacts to a callback.
 *
 * ```js
 * function handleSave(artifacts) {
 *   // ... do something with the artifacts ...
 *   return {modelArtifactsInfo: {...}, ...};
 * }
 *
 * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
 * ```
 *
 * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
 *     promise that resolves to a `SaveResult`.
 */ parcelHelpers.export(exports, "withSaveHandler", ()=>withSaveHandler);
/**
 * Creates an IOHandlerSync that passes saved model artifacts to a callback.
 *
 * ```js
 * function handleSave(artifacts) {
 *   // ... do something with the artifacts ...
 *   return {modelArtifactsInfo: {...}, ...};
 * }
 *
 * const saveResult = model.save(tf.io.withSaveHandler(handleSave));
 * ```
 *
 * @param saveHandler A function that accepts a `ModelArtifacts` and returns a
 *     `SaveResult`.
 */ parcelHelpers.export(exports, "withSaveHandlerSync", ()=>withSaveHandlerSync);
class PassthroughLoader {
    constructor(modelArtifacts){
        this.modelArtifacts = modelArtifacts;
    }
    load() {
        return this.modelArtifacts;
    }
}
class PassthroughSaver {
    constructor(saveHandler){
        this.saveHandler = saveHandler;
    }
    save(modelArtifacts) {
        return this.saveHandler(modelArtifacts);
    }
}
class PassthroughAsync {
    constructor(handler){
        if (handler.load) this.load = ()=>Promise.resolve(handler.load());
        if (handler.save) this.save = (modelArtifacts)=>Promise.resolve(handler.save(modelArtifacts));
    }
}
function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
    const args = arguments;
    return new PassthroughAsync(fromMemorySync(...args));
}
function fromMemorySync(modelArtifacts, weightSpecs, weightData, trainingConfig) {
    if (arguments.length === 1) {
        const isModelArtifacts = modelArtifacts.modelTopology != null || modelArtifacts.weightSpecs != null;
        if (isModelArtifacts) return new PassthroughLoader(modelArtifacts);
        else {
            // Legacy support: with only modelTopology.
            // TODO(cais): Remove this deprecated API.
            console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
            return new PassthroughLoader({
                modelTopology: modelArtifacts
            });
        }
    } else {
        // Legacy support.
        // TODO(cais): Remove this deprecated API.
        console.warn("Please call tf.io.fromMemory() with only one argument. The argument should be of type ModelArtifacts. The multi-argument signature of tf.io.fromMemory() has been deprecated and will be removed in a future release.");
        return new PassthroughLoader({
            modelTopology: modelArtifacts,
            weightSpecs,
            weightData,
            trainingConfig
        });
    }
}
function withSaveHandler(saveHandler) {
    return new PassthroughSaver(saveHandler);
}
function withSaveHandlerSync(saveHandler) {
    return new PassthroughSaver(saveHandler);
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8ony7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Exports under the tf.math.* namespace.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "confusionMatrix", ()=>(0, _confusionMatrix.confusionMatrix));
var _confusionMatrix = require("./ops/confusion_matrix");

},{"./ops/confusion_matrix":"fAWJm","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fAWJm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Computes the confusion matrix from true labels and predicted labels.
 *
 * ```js
 * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
 * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
 * const numClasses = 3;
 * const out = tf.math.confusionMatrix(labels, predictions, numClasses);
 * out.print();
 * // Expected output matrix:
 * // [[2, 0, 0],
 * //  [0, 1, 1],
 * //  [0, 0, 1]]
 * ```
 *
 * @param labels The target labels, assumed to be 0-based integers
 *   for the classes. The shape is `[numExamples]`, where
 *   `numExamples` is the number of examples included.
 * @param predictions The predicted classes, assumed to be
 *   0-based integers for the classes. Must have the same shape as `labels`.
 * @param numClasses Number of all classes, as an integer.
 *   Its value must be larger than the largest element in `labels` and
 *   `predictions`.
 * @returns The confusion matrix as a int32-type 2D tensor. The value at
 *   row `r` and column `c` is the number of times examples of actual class
 *   `r` were predicted as class `c`.
 *
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */ parcelHelpers.export(exports, "confusionMatrix_", ()=>confusionMatrix_);
parcelHelpers.export(exports, "confusionMatrix", ()=>confusionMatrix);
var _tensorUtilEnv = require("../tensor_util_env");
var _util = require("../util");
var _cast = require("./cast");
var _matMul = require("./mat_mul");
var _oneHot = require("./one_hot");
var _operation = require("./operation");
var _transpose = require("./transpose");
function confusionMatrix_(labels, predictions, numClasses) {
    const $labels = (0, _tensorUtilEnv.convertToTensor)(labels, "labels", "confusionMatrix");
    const $predictions = (0, _tensorUtilEnv.convertToTensor)(predictions, "predictions", "confusionMatrix");
    _util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), ()=>`If provided, numClasses must be a positive integer, ` + `but got ${numClasses}`);
    _util.assert($labels.rank === 1, ()=>`Expected the rank of labels to be 1, but got ${$labels.rank}`);
    _util.assert($predictions.rank === 1, ()=>`Expected the rank of predictions to be 1, ` + `but got ${$predictions.rank}`);
    _util.assert($labels.shape[0] === $predictions.shape[0], ()=>`Mismatch in the number of examples: ` + `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` + `Labels and predictions should have the same number of elements.`);
    _util.assert(numClasses > 0 && Number.isInteger(numClasses), ()=>`numClasses is required to be a positive integer, but got ` + `${numClasses}`);
    // TODO(cais): In the future, if oneHot supports tensors inputs for
    //   `numClasses`, `confusionMatrix` can make `numClasses` optional.
    const oneHotLabels = (0, _oneHot.oneHot)((0, _cast.cast)($labels, "int32"), numClasses);
    const oneHotPredictions = (0, _oneHot.oneHot)((0, _cast.cast)($predictions, "int32"), numClasses);
    const oneHotLabelsT = (0, _transpose.transpose)(oneHotLabels);
    const product = (0, _matMul.matMul)(oneHotLabelsT, oneHotPredictions);
    return (0, _cast.cast)(product, "int32");
}
const confusionMatrix = /* @__PURE__ */ (0, _operation.op)({
    confusionMatrix_
});

},{"../tensor_util_env":"6vfHI","../util":"ctavo","./cast":"b4VDA","./mat_mul":"fiMp4","./one_hot":"91jNM","./operation":"ip64W","./transpose":"2bqbS","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iaaSH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Creates a `tf.Tensor` from an image in async way.
 *
 * ```js
 * const image = new ImageData(1, 1);
 * image.data[0] = 100;
 * image.data[1] = 150;
 * image.data[2] = 200;
 * image.data[3] = 255;
 *
 * (await tf.browser.fromPixelsAsync(image)).print();
 * ```
 * This API is the async version of fromPixels. The API will first
 * check |WRAP_TO_IMAGEBITMAP| flag, and try to wrap the input to
 * imageBitmap if the flag is set to true.
 *
 * @param pixels The input image to construct the tensor from. The
 * supported image types are all 4-channel. You can also pass in an image
 * object with following attributes:
 * `{data: Uint8Array; width: number; height: number}`
 * @param numChannels The number of channels of the output tensor. A
 * numChannels value less than 4 allows you to ignore channels. Defaults to
 * 3 (ignores alpha channel of input image).
 *
 * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
 */ parcelHelpers.export(exports, "fromPixelsAsync", ()=>fromPixelsAsync);
/**
 * Draws a `tf.Tensor` of pixel values to a byte array or optionally a
 * canvas.
 *
 * When the dtype of the input is 'float32', we assume values in the range
 * [0-1]. Otherwise, when input is 'int32', we assume values in the range
 * [0-255].
 *
 * Returns a promise that resolves when the canvas has been drawn to.
 *
 * @param img A rank-2 tensor with shape `[height, width]`, or a rank-3 tensor
 * of shape `[height, width, numChannels]`. If rank-2, draws grayscale. If
 * rank-3, must have depth of 1, 3 or 4. When depth of 1, draws
 * grayscale. When depth of 3, we draw with the first three components of
 * the depth dimension corresponding to r, g, b and alpha = 1. When depth of
 * 4, all four components of the depth dimension correspond to r, g, b, a.
 * @param canvas The canvas to draw to.
 *
 * @doc {heading: 'Browser', namespace: 'browser'}
 */ parcelHelpers.export(exports, "toPixels", ()=>toPixels);
/**
 * Draws a `tf.Tensor` to a canvas.
 *
 * When the dtype of the input is 'float32', we assume values in the range
 * [0-1]. Otherwise, when input is 'int32', we assume values in the range
 * [0-255].
 *
 * @param image The tensor to draw on the canvas. Must match one of
 * these shapes:
 *   - Rank-2 with shape `[height, width`]: Drawn as grayscale.
 *   - Rank-3 with shape `[height, width, 1]`: Drawn as grayscale.
 *   - Rank-3 with shape `[height, width, 3]`: Drawn as RGB with alpha set in
 *     `imageOptions` (defaults to 1, which is opaque).
 *   - Rank-3 with shape `[height, width, 4]`: Drawn as RGBA.
 * @param canvas The canvas to draw to.
 * @param options The configuration arguments for image to be drawn and the
 *     canvas to draw to.
 *
 * @doc {heading: 'Browser', namespace: 'browser'}
 */ parcelHelpers.export(exports, "draw", ()=>draw);
parcelHelpers.export(exports, "fromPixels", ()=>fromPixels);
var _engine = require("../engine");
var _environment = require("../environment");
var _kernelNames = require("../kernel_names");
var _kernelRegistry = require("../kernel_registry");
var _tensor = require("../tensor");
var _tensorUtilEnv = require("../tensor_util_env");
var _cast = require("./cast");
var _operation = require("./operation");
var _tensor3D = require("./tensor3d");
let fromPixels2DContext;
let hasToPixelsWarned = false;
/**
 * Creates a `tf.Tensor` from an image.
 *
 * ```js
 * const image = new ImageData(1, 1);
 * image.data[0] = 100;
 * image.data[1] = 150;
 * image.data[2] = 200;
 * image.data[3] = 255;
 *
 * tf.browser.fromPixels(image).print();
 * ```
 *
 * @param pixels The input image to construct the tensor from. The
 * supported image types are all 4-channel. You can also pass in an image
 * object with following attributes:
 * `{data: Uint8Array; width: number; height: number}`
 * @param numChannels The number of channels of the output tensor. A
 * numChannels value less than 4 allows you to ignore channels. Defaults to
 * 3 (ignores alpha channel of input image).
 *
 * @returns A Tensor3D with the shape `[height, width, numChannels]`.
 *
 * Note: fromPixels can be lossy in some cases, same image may result in
 * slightly different tensor values, if rendered by different rendering
 * engines. This means that results from different browsers, or even same
 * browser with CPU and GPU rendering engines can be different. See discussion
 * in details:
 * https://github.com/tensorflow/tfjs/issues/5482
 *
 * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true}
 */ function fromPixels_(pixels, numChannels = 3) {
    // Sanity checks.
    if (numChannels > 4) throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");
    if (pixels == null) throw new Error("pixels passed to tf.browser.fromPixels() can not be null");
    let isPixelData = false;
    let isImageData = false;
    let isVideo = false;
    let isImage = false;
    let isCanvasLike = false;
    let isImageBitmap = false;
    if (pixels.data instanceof Uint8Array) isPixelData = true;
    else if (typeof ImageData !== "undefined" && pixels instanceof ImageData) isImageData = true;
    else if (typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement) isVideo = true;
    else if (typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement) isImage = true;
    else if (pixels.getContext != null) isCanvasLike = true;
    else if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) isImageBitmap = true;
    else throw new Error("pixels passed to tf.browser.fromPixels() must be either an " + `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement, ImageData ` + `in browser, or OffscreenCanvas, ImageData in webworker` + ` or {data: Uint32Array, width: number, height: number}, ` + `but was ${pixels.constructor.name}`);
    // If the current backend has 'FromPixels' registered, it has a more
    // efficient way of handling pixel uploads, so we call that.
    const kernel = (0, _kernelRegistry.getKernel)((0, _kernelNames.FromPixels), (0, _engine.ENGINE).backendName);
    if (kernel != null) {
        const inputs = {
            pixels
        };
        const attrs = {
            numChannels
        };
        return (0, _engine.ENGINE).runKernel((0, _kernelNames.FromPixels), inputs, attrs);
    }
    const [width, height] = isVideo ? [
        pixels.videoWidth,
        pixels.videoHeight
    ] : [
        pixels.width,
        pixels.height
    ];
    let vals;
    if (isCanvasLike) vals = // tslint:disable-next-line:no-any
    pixels.getContext("2d").getImageData(0, 0, width, height).data;
    else if (isImageData || isPixelData) vals = pixels.data;
    else if (isImage || isVideo || isImageBitmap) {
        if (fromPixels2DContext == null) {
            if (typeof document === "undefined") {
                if (typeof OffscreenCanvas !== "undefined" && typeof OffscreenCanvasRenderingContext2D !== "undefined") // @ts-ignore
                fromPixels2DContext = new OffscreenCanvas(1, 1).getContext("2d");
                else throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
            } else fromPixels2DContext = document.createElement("canvas").getContext("2d", {
                willReadFrequently: true
            });
        }
        fromPixels2DContext.canvas.width = width;
        fromPixels2DContext.canvas.height = height;
        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
        vals = fromPixels2DContext.getImageData(0, 0, width, height).data;
    }
    let values;
    if (numChannels === 4) values = new Int32Array(vals);
    else {
        const numPixels = width * height;
        values = new Int32Array(numPixels * numChannels);
        for(let i = 0; i < numPixels; i++)for(let channel = 0; channel < numChannels; ++channel)values[i * numChannels + channel] = vals[i * 4 + channel];
    }
    const outShape = [
        height,
        width,
        numChannels
    ];
    return (0, _tensor3D.tensor3d)(values, outShape, "int32");
}
// Helper functions for |fromPixelsAsync| to check whether the input can
// be wrapped into imageBitmap.
function isPixelData(pixels) {
    return pixels != null && pixels.data instanceof Uint8Array;
}
function isImageBitmapFullySupported() {
    return typeof window !== "undefined" && typeof ImageBitmap !== "undefined" && window.hasOwnProperty("createImageBitmap");
}
function isNonEmptyPixels(pixels) {
    return pixels != null && pixels.width !== 0 && pixels.height !== 0;
}
function canWrapPixelsToImageBitmap(pixels) {
    return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);
}
async function fromPixelsAsync(pixels, numChannels = 3) {
    let inputs = null;
    // Check whether the backend needs to wrap |pixels| to imageBitmap and
    // whether |pixels| can be wrapped to imageBitmap.
    if ((0, _environment.env)().getBool("WRAP_TO_IMAGEBITMAP") && canWrapPixelsToImageBitmap(pixels)) {
        // Force the imageBitmap creation to not do any premultiply alpha
        // ops.
        let imageBitmap;
        try {
            // wrap in try-catch block, because createImageBitmap may not work
            // properly in some browsers, e.g.
            // https://bugzilla.mozilla.org/show_bug.cgi?id=1335594
            // tslint:disable-next-line: no-any
            imageBitmap = await createImageBitmap(pixels, {
                premultiplyAlpha: "none"
            });
        } catch (e) {
            imageBitmap = null;
        }
        // createImageBitmap will clip the source size.
        // In some cases, the input will have larger size than its content.
        // E.g. new Image(10, 10) but with 1 x 1 content. Using
        // createImageBitmap will clip the size from 10 x 10 to 1 x 1, which
        // is not correct. We should avoid wrapping such resouce to
        // imageBitmap.
        if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) inputs = imageBitmap;
        else inputs = pixels;
    } else inputs = pixels;
    return fromPixels_(inputs, numChannels);
}
function validateImgTensor(img) {
    if (img.rank !== 2 && img.rank !== 3) throw new Error(`toPixels only supports rank 2 or 3 tensors, got rank ${img.rank}.`);
    const depth = img.rank === 2 ? 1 : img.shape[2];
    if (depth > 4 || depth === 2) throw new Error(`toPixels only supports depth of size ` + `1, 3 or 4 but got ${depth}`);
    if (img.dtype !== "float32" && img.dtype !== "int32") throw new Error(`Unsupported type for toPixels: ${img.dtype}.` + ` Please use float32 or int32 tensors.`);
}
function validateImageOptions(imageOptions) {
    const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1;
    if (alpha > 1 || alpha < 0) throw new Error(`Alpha value ${alpha} is suppoed to be in range [0 - 1].`);
}
async function toPixels(img, canvas) {
    let $img = (0, _tensorUtilEnv.convertToTensor)(img, "img", "toPixels");
    if (!(img instanceof (0, _tensor.Tensor))) {
        // Assume int32 if user passed a native array.
        const originalImgTensor = $img;
        $img = (0, _cast.cast)(originalImgTensor, "int32");
        originalImgTensor.dispose();
    }
    validateImgTensor($img);
    const [height, width] = $img.shape.slice(0, 2);
    const depth = $img.rank === 2 ? 1 : $img.shape[2];
    const data = await $img.data();
    const multiplier = $img.dtype === "float32" ? 255 : 1;
    const bytes = new Uint8ClampedArray(width * height * 4);
    for(let i = 0; i < height * width; ++i){
        const rgba = [
            0,
            0,
            0,
            255
        ];
        for(let d = 0; d < depth; d++){
            const value = data[i * depth + d];
            if ($img.dtype === "float32") {
                if (value < 0 || value > 1) throw new Error(`Tensor values for a float32 Tensor must be in the ` + `range [0 - 1] but encountered ${value}.`);
            } else if ($img.dtype === "int32") {
                if (value < 0 || value > 255) throw new Error(`Tensor values for a int32 Tensor must be in the ` + `range [0 - 255] but encountered ${value}.`);
            }
            if (depth === 1) {
                rgba[0] = value * multiplier;
                rgba[1] = value * multiplier;
                rgba[2] = value * multiplier;
            } else rgba[d] = value * multiplier;
        }
        const j = i * 4;
        bytes[j + 0] = Math.round(rgba[0]);
        bytes[j + 1] = Math.round(rgba[1]);
        bytes[j + 2] = Math.round(rgba[2]);
        bytes[j + 3] = Math.round(rgba[3]);
    }
    if (canvas != null) {
        if (!hasToPixelsWarned) {
            const kernel = (0, _kernelRegistry.getKernel)((0, _kernelNames.Draw), (0, _engine.ENGINE).backendName);
            if (kernel != null) {
                console.warn("tf.browser.toPixels is not efficient to draw tensor on canvas. Please try tf.browser.draw instead.");
                hasToPixelsWarned = true;
            }
        }
        canvas.width = width;
        canvas.height = height;
        const ctx = canvas.getContext("2d");
        const imageData = new ImageData(bytes, width, height);
        ctx.putImageData(imageData, 0, 0);
    }
    if ($img !== img) $img.dispose();
    return bytes;
}
function draw(image, canvas, options) {
    let $img = (0, _tensorUtilEnv.convertToTensor)(image, "img", "draw");
    if (!(image instanceof (0, _tensor.Tensor))) {
        // Assume int32 if user passed a native array.
        const originalImgTensor = $img;
        $img = (0, _cast.cast)(originalImgTensor, "int32");
        originalImgTensor.dispose();
    }
    validateImgTensor($img);
    validateImageOptions(options === null || options === void 0 ? void 0 : options.imageOptions);
    const inputs = {
        image: $img
    };
    const attrs = {
        canvas,
        options
    };
    (0, _engine.ENGINE).runKernel((0, _kernelNames.Draw), inputs, attrs);
}
const fromPixels = /* @__PURE__ */ (0, _operation.op)({
    fromPixels_
});

},{"../engine":"1g2qo","../environment":"dXmHm","../kernel_names":"hniYO","../kernel_registry":"aG7g9","../tensor":"cJNoh","../tensor_util_env":"6vfHI","./cast":"b4VDA","./operation":"ip64W","./tensor3d":"lVDsd","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4RUOy":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Validate gather nd inputs.
 *
 * @param tensor The tensor contains the source values.
 * @param indices The tensor contains the indices to slice the source.
 *
 * @returns [resultShape, numUpdates, sliceSize, strides]
 */ parcelHelpers.export(exports, "prepareAndValidate", ()=>prepareAndValidate);
var _util = require("../util");
function prepareAndValidate(tensor, indices) {
    const tensorRank = tensor.shape.length;
    const indicesRank = indices.shape.length;
    if (tensorRank < 1) throw new Error("tf.gatherND() expects the input to be rank 1 or higher," + ` but the rank was ${tensorRank}.`);
    if (indicesRank < 1) throw new Error("tf.gatherND() expects the indices to be rank 1 or higher," + ` but the rank was ${indicesRank}.`);
    if (indices.dtype !== "int32") throw new Error("tf.gatherND() expects the indices to be int32 type," + ` but the dtype was ${indices.dtype}.`);
    if (indices.shape[indicesRank - 1] > tensorRank) throw new Error("index innermost dimension length must be <= tensor rank; saw: " + `${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);
    if ((0, _util.sizeFromShape)(tensor.shape) === 0) throw new Error("Requested more than 0 entries, but input is empty." + ` Input shape: ${tensor.shape}.`);
    const indicesShape = indices.shape;
    const sliceRank = indicesShape[indicesShape.length - 1];
    // The result shape is
    //   indices.shape[:-1] + params.shape[indices.shape[-1]:]
    let nResult = 1;
    for(let i = 0; i < indicesShape.length - 1; ++i)nResult *= indicesShape[i];
    const inputShape = tensor.shape;
    const resultShape = indicesShape.slice();
    resultShape.pop();
    let sliceSize = 1;
    for(let i = sliceRank; i < tensorRank; ++i){
        sliceSize *= inputShape[i];
        resultShape.push(inputShape[i]);
    }
    const strides = [
        ...(0, _util.computeStrides)(tensor.shape).map((stride)=>stride / sliceSize),
        1
    ].slice(0, sliceRank);
    return [
        resultShape,
        nResult,
        sliceSize,
        strides
    ];
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3A9Ql":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "assertParamsValid", ()=>assertParamsValid);
/** Converts a binary mask to an array of axes. Used in stridedSlice(). */ parcelHelpers.export(exports, "maskToAxes", ()=>maskToAxes);
/** Computes the output shape given the strided slice params. */ parcelHelpers.export(exports, "computeOutShape", ()=>computeOutShape);
// Creates full selection at the elided dimensions. If the dimension matches
// the ellipsis mask, override the current stride value. Otherwise, insert.
parcelHelpers.export(exports, "stridesWithElidedDims", ()=>stridesWithElidedDims);
// Normalize the start, end and strides.
parcelHelpers.export(exports, "getNormalizedAxes", ()=>getNormalizedAxes);
// Creates full selection at the elided dimensions. If the dimension matches
// the ellipsis mask, override the current start value. Otherwise, insert.
parcelHelpers.export(exports, "startIndicesWithElidedDims", ()=>startIndicesWithElidedDims);
// Creates full selection at the elided dimensions. If the dimension matches
// the ellipsis mask, override the current stop value. Otherwise, insert.
parcelHelpers.export(exports, "stopIndicesWithElidedDims", ()=>stopIndicesWithElidedDims);
parcelHelpers.export(exports, "stridesForAxis", ()=>stridesForAxis);
parcelHelpers.export(exports, "startForAxis", ()=>startForAxis);
parcelHelpers.export(exports, "stopForAxis", ()=>stopForAxis);
/**
 * Returns true if the slice occupies a continous set of elements in the
 * 'flat' space.
 */ parcelHelpers.export(exports, "isSliceContinous", ()=>isSliceContinous);
parcelHelpers.export(exports, "computeFlatOffset", ()=>computeFlatOffset);
parcelHelpers.export(exports, "parseSliceParams", ()=>parseSliceParams);
// Convert the slicing specification from a sparse representation to a dense
// representation. This means that all ellipses and newaxis are expanded out.
parcelHelpers.export(exports, "sliceInfo", ()=>sliceInfo);
var _util = require("../util");
const NEW_AXIS = -2;
const SHRINK_AXIS = -1;
function assertParamsValid(input, begin, size) {
    const inputRank = input.shape.length;
    _util.assert(inputRank === begin.length, ()=>`Error in slice${inputRank}D: Length of begin ${begin} must ` + `match the rank of the array (${inputRank}).`);
    _util.assert(inputRank === size.length, ()=>`Error in slice${inputRank}D: Length of size ${size} must ` + `match the rank of the array (${inputRank}).`);
    for(let i = 0; i < inputRank; ++i)_util.assert(begin[i] + size[i] <= input.shape[i], ()=>`Error in slice${inputRank}D: begin[${i}] + size[${i}] ` + `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${input.shape[i]})`);
}
function maskToAxes(mask) {
    const axes = [];
    let axis = 0;
    while(mask > 0){
        if (mask & 1) axes.push(axis);
        mask /= 2;
        axis++;
    }
    return axes;
}
function computeOutShape(begin, end, strides) {
    const size = [];
    for(let axis = 0; axis < begin.length; axis++)size[axis] = Math.ceil((end[axis] - begin[axis]) / strides[axis]);
    return size;
}
function stridesWithElidedDims(strides, ellipsisInsertionIndex, numElidedAxes, inputShape) {
    const newStrides = [
        ...strides
    ];
    for(let i = newStrides.length; i < inputShape.length; i++)newStrides.push(1);
    for(let i = 0; i < numElidedAxes; i++)if (i === 0) newStrides[ellipsisInsertionIndex] = 1;
    else {
        newStrides.splice(ellipsisInsertionIndex, 0 /* num elements to delete */ , 1 /* element to add */ );
        newStrides.pop();
    }
    return newStrides;
}
function unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, normalizedAxis) {
    if (normalizedAxis <= ellipsisInsertionIndex) return normalizedAxis;
    return normalizedAxis - (numElidedAxes - 1);
}
function getElidedAxes(numElidedAxes, ellipsisInsertionIndex) {
    const elidedAxes = [];
    for(let i = 0; i < numElidedAxes; i++)elidedAxes.push(ellipsisInsertionIndex + i);
    return elidedAxes;
}
function getNormalizedAxes(inputShape, ellipsisAxes, numInterpolatedAxes, begin, end, strides, beginMask, endMask, ellipsisMask) {
    const inputRank = inputShape.length;
    let normalizedBegin = new Array(inputRank), normalizedEnd = new Array(inputRank), normalizedStrides = new Array(inputRank);
    if (ellipsisAxes.length && numInterpolatedAxes > 0) {
        const fullIndex = ellipsisAxes[0];
        // The ellipsis applies to the masked index as well as any dimensions
        // that are interpolated.
        const numElidedAxes = numInterpolatedAxes + 1;
        normalizedBegin = startIndicesWithElidedDims(beginMask, fullIndex, numElidedAxes, begin, inputShape);
        normalizedEnd = stopIndicesWithElidedDims(endMask, fullIndex, numElidedAxes, end, inputShape);
        normalizedStrides = stridesWithElidedDims(strides, fullIndex, numElidedAxes, inputShape);
    } else for(let axis = 0; axis < inputRank; axis++){
        normalizedBegin[axis] = startForAxis(beginMask, begin, strides, inputShape, axis, ellipsisMask);
        normalizedEnd[axis] = stopForAxis(endMask, end, strides, inputShape, axis, ellipsisMask);
        normalizedStrides[axis] = stridesForAxis(strides, axis, ellipsisMask);
    }
    return {
        begin: normalizedBegin,
        end: normalizedEnd,
        strides: normalizedStrides
    };
}
function startIndicesWithElidedDims(beginMask, ellipsisInsertionIndex, numElidedAxes, originalBegin, inputShape) {
    const newIndices = [
        ...inputShape
    ];
    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
    for(let axis = 0; axis < newIndices.length; axis++)if (elidedAxes.indexOf(axis) > -1) newIndices[axis] = 0;
    else {
        const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
        let originalValue = originalBegin[originalAxis];
        if (beginMask & 1 << originalAxis) originalValue = 0;
        newIndices[axis] = originalValue;
    }
    return newIndices;
}
function stopIndicesWithElidedDims(endMask, ellipsisInsertionIndex, numElidedAxes, originalEnd, inputShape) {
    const newIndices = [
        ...inputShape
    ];
    const elidedAxes = getElidedAxes(numElidedAxes, ellipsisInsertionIndex);
    for(let axis = 0; axis < newIndices.length; axis++)if (elidedAxes.indexOf(axis) > -1) newIndices[axis] = Number.MAX_SAFE_INTEGER;
    else {
        const originalAxis = unnormalizeAxis(ellipsisInsertionIndex, numElidedAxes, axis);
        let originalValue = originalEnd[originalAxis];
        if (endMask & 1 << originalAxis) originalValue = Number.MAX_SAFE_INTEGER;
        newIndices[axis] = originalValue;
    }
    for(let i = 0; i < newIndices.length; i++){
        // Handle negative indices
        const axisSize = inputShape[i];
        if (newIndices[i] < 0) newIndices[i] += axisSize;
        newIndices[i] = _util.clamp(0, newIndices[i], inputShape[i]);
    }
    return newIndices;
}
function stridesForAxis(strides, axis, ellipsisMask) {
    let stride = strides[axis];
    if (ellipsisMask & 1 << axis || stride == null) stride = 1;
    return stride;
}
function startForAxis(beginMask, startIndices, strides, inputShape, axis, ellipsisMask) {
    // Begin with the specified index
    let start = startIndices[axis];
    const stride = strides[axis] || 1;
    // Check the axis bit from right of masked axes, or the begin index is not set
    // for the axis.
    if (beginMask & 1 << axis || ellipsisMask & 1 << axis || start == null) {
        if (stride > 0) // Forward iteration - use the first element. These values will get
        // clamped below (Note: We could have set them to 0 and axis_size-1, but
        // use lowest() and max() to maintain symmetry with StopForAxis())
        start = Number.MIN_SAFE_INTEGER;
        else // Backward iteration - use the last element.
        start = Number.MAX_SAFE_INTEGER;
    }
    // Handle negative indices
    const axisSize = inputShape[axis];
    if (start < 0) start += axisSize;
    // Clamping
    start = _util.clamp(0, start, axisSize - 1);
    return start;
}
function stopForAxis(endMask, stopIndices, strides, inputShape, axis, ellipsisMask) {
    // Begin with the specified index
    let stop = stopIndices[axis];
    const stride = strides[axis] || 1;
    // Check the axis bit from right of masked axes, or if the stop index is not
    // set for this axis.
    if (endMask & 1 << axis || ellipsisMask & 1 << axis || stop == null) {
        if (stride > 0) // Forward iteration - use the last element. These values will get
        // clamped below
        stop = Number.MAX_SAFE_INTEGER;
        else // Backward iteration - use the first element.
        stop = Number.MIN_SAFE_INTEGER;
    }
    // Handle negative indices
    const axisSize = inputShape[axis];
    if (stop < 0) stop += axisSize;
    // Clamping
    // Because the end index points one past the last element, we need slightly
    // different clamping ranges depending on the direction.
    if (stride > 0) // Forward iteration
    stop = _util.clamp(0, stop, axisSize);
    else // Backward iteration
    stop = _util.clamp(-1, stop, axisSize - 1);
    return stop;
}
function isSliceContinous(shape, begin, size) {
    // Index of the first axis that has size > 1.
    let firstNonOneAxis = size.length;
    for(let i = 0; i < size.length; i++)if (size[i] > 1) {
        firstNonOneAxis = i;
        break;
    }
    for(let i = firstNonOneAxis + 1; i < size.length; i++){
        if (begin[i] > 0 || size[i] !== shape[i]) return false;
    }
    return true;
}
function computeFlatOffset(begin, strides) {
    let flatOffset = begin.length > 0 ? begin[begin.length - 1] : 1;
    for(let i = 0; i < begin.length - 1; i++)flatOffset += begin[i] * strides[i];
    return flatOffset;
}
function parseSliceParams(x, begin, size) {
    // The following logic allows for more ergonomic calls.
    let begin_;
    const xRank = x.shape.length;
    if (typeof begin === "number") begin_ = [
        begin,
        ...new Array(xRank - 1).fill(0)
    ];
    else if (begin.length < xRank) begin_ = begin.concat(new Array(xRank - begin.length).fill(0));
    else begin_ = begin.slice();
    begin_.forEach((d)=>{
        _util.assert(d !== -1, ()=>"slice() does not support negative begin indexing.");
    });
    let size_;
    if (size == null) size_ = new Array(xRank).fill(-1);
    else if (typeof size === "number") size_ = [
        size,
        ...new Array(xRank - 1).fill(-1)
    ];
    else if (size.length < xRank) size_ = size.concat(new Array(xRank - size.length).fill(-1));
    else size_ = size;
    size_ = size_.map((d, i)=>{
        if (d >= 0) return d;
        else {
            _util.assert(d === -1, ()=>`Negative size values should be exactly -1 but got ` + `${d} for the slice() size at index ${i}.`);
            return x.shape[i] - begin_[i];
        }
    });
    return [
        begin_,
        size_
    ];
}
function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
    let stridesNonNull;
    if (strides == null) {
        stridesNonNull = new Array(begin.length);
        stridesNonNull.fill(1);
    } else stridesNonNull = strides;
    // Only one non-zero bit is allowed in ellipsisMask, which means ellipsisMask
    // is a power of 2. Use bit compares to ensure ellipsisMask is 0 or a power
    // of 2. When i is a power of 2, i & (i - 1) is always 0.
    // Also ref:
    // https://stackoverflow.com/questions/600293/how-to-check-if-a-number-is-a-power-of-2
    if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) throw new Error("Multiple ellipses in slice is not allowed.");
    // Step 1: Account for ellipsis and new axis.
    // Check for ellipsis and count how many non-newaxis there are after.
    let ellipsisSeen = false;
    const sparseSpec = {
        dims: stridesNonNull.length,
        numAddAxisAfterEllipsis: 0,
        begin: begin.slice(),
        end: end.slice(),
        strides: stridesNonNull.slice(),
        beginMask,
        endMask,
        ellipsisMask,
        newAxisMask,
        shrinkAxisMask
    };
    for(let i = 0; i < sparseSpec.dims; i++){
        if (ellipsisSeen && (1 << i & newAxisMask) !== 0) sparseSpec.numAddAxisAfterEllipsis++;
        if (1 << i & ellipsisMask) ellipsisSeen = true;
    }
    // If no ellipsis insert one at the end.
    if (!ellipsisSeen) {
        sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;
        sparseSpec.dims++; // this effects loop iteration below
    }
    // Step 2: Make a sparse spec into a full index spec.
    //
    // The sparse spec deos not correspond to the number of dimensions.
    // Make a dense spec that cooresponds to the number of dimensions.
    //
    // For example suppose foo[...,3:] on foo.shape = [2, 2, 3] then we need to
    // produce the missing beginMask for the first two dimensions i.e. from
    // beginMaskSpec = 0, endMaskSpec = 2, we achieve beginMask = 6 (110),
    // endMask = 7 (111).
    const denseSpec = {
        dims: xShape.length,
        beginMask: 0,
        endMask: 0,
        beginValid: false,
        endValid: false
    };
    buildDenseSpec(sparseSpec, denseSpec);
    // Step 3: Make implicit ranges (non-zero beginMasks and endMasks) explicit
    // and bounds check.
    let isIdentity = true;
    let sliceDim0 = true;
    let isSimpleSlice = true;
    const processingShape = [];
    const finalShape = [];
    for(let i = 0; i < xShape.length; ++i){
        if (denseSpec.strides[i] === 0) throw Error(`strides[${i}] must be non-zero`);
        const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i);
        const dimI = xShape[i];
        if (dimI === -1) {
            processingShape.push(shrinkI ? 1 : -1);
            continue;
        }
        const masks = [
            denseSpec.beginMask & 1 << i,
            denseSpec.endMask & 1 << i
        ];
        const validRange = [
            denseSpec.strides[i] > 0 ? 0 : -1,
            denseSpec.strides[i] > 0 ? dimI : dimI - 1
        ];
        if (shrinkI && denseSpec.strides[i] <= 0) throw Error("only stride 1 allowed on non-range indexing.");
        isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1;
        const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i);
        if (denseSpec.beginValid && denseSpec.endValid) {
            if (shrinkI) {
                // If we are shrinking, the end index is now possibly incorrect. In
                // particular foo[-1] produces sparseBegin = -1, sparseEnd = 0.
                // and canonical puts these to n-1 and 0, which implies a degenerate
                // interval. Fortunately, it is now safe to re-create end as begin + 1.
                const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i];
                denseSpec.begin[i] = xFwd;
                denseSpec.end[i] = denseSpec.begin[i] + 1;
                if (xFwd < 0 || xFwd >= dimI) throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);
            } else {
                denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);
                denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);
            }
            // Update optimization values
            const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;
            isIdentity = isIdentity && takeAllInDimension;
            sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension);
        } else {
            isIdentity = isIdentity && denseSpec.strides[i] === 1 && beginAndEndMasked;
            sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked);
        }
        // Compute the processing shape (the intermediate Eigen will produce)
        let intervalLength;
        let knownInterval = false;
        if (denseSpec.beginValid && denseSpec.endValid) {
            intervalLength = denseSpec.end[i] - denseSpec.begin[i];
            knownInterval = true;
        } else if (shrinkI) {
            // The dimension is still known as 1 for the processingShape, but will be
            // discarded for the final shape.
            intervalLength = 1;
            knownInterval = true;
        } else if (beginAndEndMasked) // Even if we don't have values for begin or end, we do know that this
        // dimension covers the whole interval. If we have shape information for
        // this dimension, that tells us the interval length.
        {
            if (dimI >= 0) {
                if (denseSpec.strides[i] < 0) intervalLength = -dimI;
                else intervalLength = dimI;
                knownInterval = true;
            }
        }
        if (knownInterval) {
            let sizeI;
            // Hold zero if the interval is degenerate, otherwise account for
            // remainder
            if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) sizeI = 0;
            else sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);
            processingShape.push(sizeI);
        } else processingShape.push(-1);
    }
    // Step 4: Compute the final shape
    //
    // newAxis will increase dimension by 1 (with a one-size dimension)
    // slices like foo[3, ...] will reduce dimension by 1.
    // This cannot be done earlier, because it depends on Step 3.
    for(let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim){
        const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];
        if (gatherIndex >= 0) finalShape.push(processingShape[gatherIndex]);
        else if (gatherIndex === NEW_AXIS) finalShape.push(1);
    }
    const finalShapeSparse = finalShape.filter((dim, i)=>denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);
    return {
        finalShapeSparse,
        finalShape,
        isIdentity,
        sliceDim0,
        isSimpleSlice,
        begin: denseSpec.begin,
        end: denseSpec.end,
        strides: denseSpec.strides
    };
}
function buildDenseSpec(sparse, dense) {
    dense.beginMask = 0;
    dense.endMask = 0;
    dense.shrinkAxisMask = 0;
    let fullIndex = 0;
    dense.beginValid = sparse.begin != null;
    dense.endValid = sparse.end != null;
    dense.begin = new Array(dense.dims);
    dense.end = new Array(dense.dims);
    dense.strides = new Array(dense.dims);
    dense.finalShapeGatherIndices = [];
    dense.finalShapeGatherIndicesSparse = [];
    dense.inputShapeGatherIndicesSparse = new Array(dense.dims);
    for(let i = 0; i < sparse.dims; i++){
        if (1 << i & sparse.ellipsisMask) {
            // Only the bit that has ellipsis will fall in this condition.
            // Expand the ellipsis into the appropriate indices
            // Note: this only works because we guaranteed one ellipsis.
            const nextIndex = Math.min(dense.dims - (sparse.dims - i) + 1 + sparse.numAddAxisAfterEllipsis, dense.dims);
            for(; fullIndex < nextIndex; fullIndex++){
                // newAxis aren't real axis so you have to skip.
                dense.begin[fullIndex] = 0;
                dense.end[fullIndex] = 0;
                dense.strides[fullIndex] = 1;
                dense.beginMask |= 1 << fullIndex;
                dense.endMask |= 1 << fullIndex;
                dense.finalShapeGatherIndices.push(fullIndex);
                dense.finalShapeGatherIndicesSparse.push(-1);
                dense.inputShapeGatherIndicesSparse[fullIndex] = i;
            }
        } else if (1 << i & sparse.newAxisMask) {
            // Only the bit that has newAxis will fall in this condition.
            dense.finalShapeGatherIndices.push(NEW_AXIS);
            dense.finalShapeGatherIndicesSparse.push(-1);
        } else {
            if (fullIndex === dense.begin.length) throw Error(`Index out of range using input dim ${fullIndex}; input ` + `has only ${dense.dims} dims, ${dense.begin.length}.`);
            // Gather slicing spec into appropriate index.
            if (sparse.begin != null) dense.begin[fullIndex] = sparse.begin[i];
            if (sparse.end != null) dense.end[fullIndex] = sparse.end[i];
            dense.strides[fullIndex] = sparse.strides[i];
            if (sparse.beginMask & 1 << i) dense.beginMask |= 1 << fullIndex;
            if (sparse.endMask & 1 << i) dense.endMask |= 1 << fullIndex;
            // If shrink, record where to get the dimensionality from (i.e. newAxis)
            // creates a fake 1 size dimension. Also remember shrink axis (now in
            // dense form) so we can ignore dense.end below.
            if (sparse.shrinkAxisMask & 1 << i) {
                dense.finalShapeGatherIndices.push(SHRINK_AXIS);
                dense.finalShapeGatherIndicesSparse.push(-1);
                dense.shrinkAxisMask |= 1 << fullIndex;
            } else {
                dense.finalShapeGatherIndices.push(fullIndex);
                // Remember that where in the sparse shape the dense dim comes from.
                dense.finalShapeGatherIndicesSparse.push(i);
            }
            dense.inputShapeGatherIndicesSparse[fullIndex] = i;
            fullIndex++;
        }
    }
}
function canonical(x, c, strideI, dimI, masks, validRange) {
    if (masks[c]) return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];
    else {
        const xFwd = x < 0 ? dimI + x : x; // make negative indices positive
        return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;
    }
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5jZGL":[function(require,module,exports) {
/** @license See the LICENSE file. */ // This code is auto-generated, do not modify this file!
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "version", ()=>version);
const version = "4.21.0";

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eaK8y":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "OptimizerConstructors", ()=>OptimizerConstructors);
var _adadeltaOptimizer = require("./adadelta_optimizer");
var _adagradOptimizer = require("./adagrad_optimizer");
var _adamOptimizer = require("./adam_optimizer");
var _adamaxOptimizer = require("./adamax_optimizer");
var _momentumOptimizer = require("./momentum_optimizer");
var _rmspropOptimizer = require("./rmsprop_optimizer");
var _sgdOptimizer = require("./sgd_optimizer");
class OptimizerConstructors {
    /**
     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.
     *
     * ```js
     * // Fit a quadratic function by learning the coefficients a, b, c.
     * const xs = tf.tensor1d([0, 1, 2, 3]);
     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);
     *
     * const a = tf.scalar(Math.random()).variable();
     * const b = tf.scalar(Math.random()).variable();
     * const c = tf.scalar(Math.random()).variable();
     *
     * // y = a * x^2 + b * x + c.
     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
     * const loss = (pred, label) => pred.sub(label).square().mean();
     *
     * const learningRate = 0.01;
     * const optimizer = tf.train.sgd(learningRate);
     *
     * // Train the model.
     * for (let i = 0; i < 10; i++) {
     *   optimizer.minimize(() => loss(f(xs), ys));
     * }
     *
     * // Make predictions.
     * console.log(
     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);
     * const preds = f(xs).dataSync();
     * preds.forEach((pred, i) => {
     *   console.log(`x: ${i}, pred: ${pred}`);
     * });
     * ```
     *
     * @param learningRate The learning rate to use for the SGD algorithm.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */ static sgd(learningRate) {
        return new (0, _sgdOptimizer.SGDOptimizer)(learningRate);
    }
    /**
     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient
     * descent.
     *
     * See
     * [http://proceedings.mlr.press/v28/sutskever13.pdf](
     * http://proceedings.mlr.press/v28/sutskever13.pdf)
     *
     * @param learningRate The learning rate to use for the Momentum gradient
     * descent algorithm.
     * @param momentum The momentum to use for the momentum gradient descent
     * algorithm.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */ static momentum(learningRate, momentum, useNesterov = false) {
        return new (0, _momentumOptimizer.MomentumOptimizer)(learningRate, momentum, useNesterov);
    }
    /**
     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient
     * descent. This implementation uses plain momentum and is not centered
     * version of RMSProp.
     *
     * See
     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](
     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
     *
     * @param learningRate The learning rate to use for the RMSProp gradient
     * descent algorithm.
     * @param decay The discounting factor for the history/coming gradient.
     * @param momentum The momentum to use for the RMSProp gradient descent
     * algorithm.
     * @param epsilon Small value to avoid zero denominator.
     * @param centered If true, gradients are normalized by the estimated
     * variance of the gradient.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */ static rmsprop(learningRate, decay = .9, momentum = 0.0, epsilon = null, centered = false) {
        return new (0, _rmspropOptimizer.RMSPropOptimizer)(learningRate, decay, momentum, epsilon, centered);
    }
    /**
     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.
     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
     *
     * @param learningRate The learning rate to use for the Adam gradient
     * descent algorithm.
     * @param beta1 The exponential decay rate for the 1st moment estimates.
     * @param beta2 The exponential decay rate for the 2nd moment estimates.
     * @param epsilon A small constant for numerical stability.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */ static adam(learningRate = 0.001, beta1 = 0.9, beta2 = 0.999, epsilon = null) {
        return new (0, _adamOptimizer.AdamOptimizer)(learningRate, beta1, beta2, epsilon);
    }
    /**
     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.
     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)
     *
     * @param learningRate The learning rate to use for the Adadelta gradient
     * descent algorithm.
     * @param rho The learning rate decay over each update.
     * @param epsilon A constant epsilon used to better condition the grad
     * update.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */ static adadelta(learningRate = .001, rho = .95, epsilon = null) {
        return new (0, _adadeltaOptimizer.AdadeltaOptimizer)(learningRate, rho, epsilon);
    }
    /**
     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.
     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
     *
     * @param learningRate The learning rate to use for the Adamax gradient
     * descent algorithm.
     * @param beta1 The exponential decay rate for the 1st moment estimates.
     * @param beta2 The exponential decay rate for the 2nd moment estimates.
     * @param epsilon A small constant for numerical stability.
     * @param decay The learning rate decay over each update.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */ static adamax(learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon = null, decay = 0.0) {
        return new (0, _adamaxOptimizer.AdamaxOptimizer)(learningRate, beta1, beta2, epsilon, decay);
    }
    /**
     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.
     * See
     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](
     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
     * or
     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](
     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)
     *
     * @param learningRate The learning rate to use for the Adagrad gradient
     * descent algorithm.
     * @param initialAccumulatorValue Starting value for the accumulators, must be
     * positive.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */ static adagrad(learningRate, initialAccumulatorValue = 0.1) {
        return new (0, _adagradOptimizer.AdagradOptimizer)(learningRate, initialAccumulatorValue);
    }
}

},{"./adadelta_optimizer":"a2jsK","./adagrad_optimizer":"ddRDd","./adam_optimizer":"5uefH","./adamax_optimizer":"72DSu","./momentum_optimizer":"bKhX1","./rmsprop_optimizer":"66Wvy","./sgd_optimizer":"4dxnr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cWOs6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "train", ()=>train);
var _optimizerConstructors = require("./optimizers/optimizer_constructors");
const train = (0, _optimizerConstructors.OptimizerConstructors);

},{"./optimizers/optimizer_constructors":"eaK8y","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dz6Y8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nextFrame", ()=>nextFrame);
const delayCallback = (()=>{
    if (typeof requestAnimationFrame !== "undefined") return requestAnimationFrame;
    else if (typeof setImmediate !== "undefined") return setImmediate;
    return (f)=>f(); // no delays
})();
/**
 * Returns a promise that resolves when a requestAnimationFrame has completed.
 *
 * On Node.js this uses setImmediate instead of requestAnimationFrame.
 *
 * This is simply a sugar method so that users can do the following:
 * `await tf.nextFrame();`
 *
 * @doc {heading: 'Performance', subheading: 'Timing'}
 */ function nextFrame() {
    return new Promise((resolve)=>delayCallback(()=>resolve()));
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8IvgS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "upcastType", ()=>(0, _types.upcastType));
parcelHelpers.export(exports, "slice_util", ()=>_sliceUtil);
parcelHelpers.export(exports, "segment_util", ()=>_segmentUtil);
parcelHelpers.export(exports, "fromUint8ToStringArray", ()=>fromUint8ToStringArray);
parcelHelpers.export(exports, "fromStringArrayToUint8", ()=>fromStringArrayToUint8);
var _util = require("../util");
// Utilities needed by backend consumers of tf-core.
var _axisUtil = require("../ops/axis_util");
parcelHelpers.exportAll(_axisUtil, exports);
var _broadcastUtil = require("../ops/broadcast_util");
parcelHelpers.exportAll(_broadcastUtil, exports);
var _concatUtil = require("../ops/concat_util");
parcelHelpers.exportAll(_concatUtil, exports);
var _convUtil = require("../ops/conv_util");
parcelHelpers.exportAll(_convUtil, exports);
var _fusedUtil = require("../ops/fused_util");
parcelHelpers.exportAll(_fusedUtil, exports);
var _fusedTypes = require("../ops/fused_types");
parcelHelpers.exportAll(_fusedTypes, exports);
var _raggedToDenseUtil = require("../ops/ragged_to_dense_util");
parcelHelpers.exportAll(_raggedToDenseUtil, exports);
var _reduceUtil = require("../ops/reduce_util");
parcelHelpers.exportAll(_reduceUtil, exports);
var _sliceUtil = require("../ops/slice_util");
var _types = require("../types");
var _rotateUtil = require("../ops/rotate_util");
parcelHelpers.exportAll(_rotateUtil, exports);
var _arrayOpsUtil = require("../ops/array_ops_util");
parcelHelpers.exportAll(_arrayOpsUtil, exports);
var _gatherNdUtil = require("../ops/gather_nd_util");
parcelHelpers.exportAll(_gatherNdUtil, exports);
var _scatterNdUtil = require("../ops/scatter_nd_util");
parcelHelpers.exportAll(_scatterNdUtil, exports);
var _seluUtil = require("../ops/selu_util");
parcelHelpers.exportAll(_seluUtil, exports);
parcelHelpers.exportAll(_fusedUtil, exports);
var _erfUtil = require("../ops/erf_util");
parcelHelpers.exportAll(_erfUtil, exports);
var _log = require("../log");
parcelHelpers.exportAll(_log, exports);
var _complexUtil = require("../backends/complex_util");
parcelHelpers.exportAll(_complexUtil, exports);
var _einsumUtil = require("../backends/einsum_util");
parcelHelpers.exportAll(_einsumUtil, exports);
var _splitUtil = require("../ops/split_util");
parcelHelpers.exportAll(_splitUtil, exports);
var _sparseFillEmptyRowsUtil = require("../ops/sparse/sparse_fill_empty_rows_util");
parcelHelpers.exportAll(_sparseFillEmptyRowsUtil, exports);
var _sparseReshapeUtil = require("../ops/sparse/sparse_reshape_util");
parcelHelpers.exportAll(_sparseReshapeUtil, exports);
var _sparseSegmentReductionUtil = require("../ops/sparse/sparse_segment_reduction_util");
parcelHelpers.exportAll(_sparseSegmentReductionUtil, exports);
var _segmentUtil = require("../ops/segment_util");
function fromUint8ToStringArray(vals) {
    try {
        // Decode the bytes into string.
        return vals.map((val)=>(0, _util.decodeString)(val));
    } catch (err) {
        throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);
    }
}
function fromStringArrayToUint8(strings) {
    return strings.map((s)=>(0, _util.encodeString)(s));
}

},{"../util":"ctavo","../ops/axis_util":"hzS4B","../ops/broadcast_util":"eLo4J","../ops/concat_util":"9tQ1B","../ops/conv_util":"bf4MH","../ops/fused_util":"8847K","../ops/fused_types":"71RqE","../ops/ragged_to_dense_util":"CafDV","../ops/reduce_util":"lSy0z","../ops/slice_util":"3A9Ql","../types":"10R8n","../ops/rotate_util":"28w8O","../ops/array_ops_util":"fuOID","../ops/gather_nd_util":"4RUOy","../ops/scatter_nd_util":"dDUQ5","../ops/selu_util":"f1Rmi","../ops/erf_util":"lREHE","../log":"5xR9j","../backends/complex_util":"eEkKB","../backends/einsum_util":"4URRN","../ops/split_util":"5SjNI","../ops/sparse/sparse_fill_empty_rows_util":"6kj4w","../ops/sparse/sparse_reshape_util":"eH8pS","../ops/sparse/sparse_segment_reduction_util":"lUp7V","../ops/segment_util":"dZEhU","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9tQ1B":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "assertParamsConsistent", ()=>assertParamsConsistent);
parcelHelpers.export(exports, "computeOutShape", ()=>computeOutShape);
var _util = require("../util");
function assertParamsConsistent(shapes, axis) {
    const rank = shapes[0].length;
    shapes.forEach((shape, i)=>{
        _util.assert(shape.length === rank, ()=>`Error in concat${rank}D: rank of tensors[${i}] must be the same ` + `as the rank of the rest (${rank})`);
    });
    _util.assert(axis >= 0 && axis < rank, ()=>`Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);
    const firstShape = shapes[0];
    shapes.forEach((shape, i)=>{
        for(let r = 0; r < rank; r++)_util.assert(r === axis || shape[r] === firstShape[r], ()=>`Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` + `does not match the shape of the rest (${firstShape}) ` + `along the non-concatenated axis ${i}.`);
    });
}
function computeOutShape(shapes, axis) {
    const outputShape = shapes[0].slice();
    for(let i = 1; i < shapes.length; i++)outputShape[axis] += shapes[i][axis];
    return outputShape;
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"71RqE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"CafDV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "RowPartitionType", ()=>RowPartitionType);
parcelHelpers.export(exports, "combineRaggedTensorToTensorShapes", ()=>combineRaggedTensorToTensorShapes);
parcelHelpers.export(exports, "getRowPartitionTypesHelper", ()=>getRowPartitionTypesHelper);
parcelHelpers.export(exports, "getRaggedRank", ()=>getRaggedRank);
parcelHelpers.export(exports, "validateDefaultValueShape", ()=>validateDefaultValueShape);
var RowPartitionType;
(function(RowPartitionType) {
    RowPartitionType[RowPartitionType["FIRST_DIM_SIZE"] = 0] = "FIRST_DIM_SIZE";
    RowPartitionType[RowPartitionType["VALUE_ROWIDS"] = 1] = "VALUE_ROWIDS";
    RowPartitionType[RowPartitionType["ROW_LENGTHS"] = 2] = "ROW_LENGTHS";
    RowPartitionType[RowPartitionType["ROW_SPLITS"] = 3] = "ROW_SPLITS";
    RowPartitionType[RowPartitionType["ROW_LIMITS"] = 4] = "ROW_LIMITS";
    RowPartitionType[RowPartitionType["ROW_STARTS"] = 5] = "ROW_STARTS";
})(RowPartitionType || (RowPartitionType = {}));
function combineRaggedTensorToTensorShapes(raggedRank, shape, valueShape) {
    // Test for consistency of valueShape and shape specified.
    // If shape is unspecified and valueShape is specified, then copy
    // over the size from the valueShape dimension.
    let outputShape = new Array();
    if (valueShape == null && shape == null) return outputShape;
    if (shape == null) // Here, value_shape must be of known size.
    while(outputShape.length < raggedRank + valueShape.length)outputShape.push(-1);
    else outputShape = shape.slice();
    if (valueShape == null) return outputShape;
    // At this point, valueShape and output_shape have known ranks.
    if (raggedRank + valueShape.length !== outputShape.length) throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.rank = ${raggedRank + valueShape.length}, but shape.rank = ${outputShape.length}`);
    for(let i = 1; i < valueShape.length; ++i){
        const valueDim = valueShape[i];
        const outputShapeDimIndex = outputShape[outputShape.length - valueShape.length + i];
        const outputShapeDim = outputShape[outputShapeDimIndex];
        if (valueDim >= 0) {
            if (outputShapeDim >= 0) {
                if (outputShapeDim !== valueDim) throw new Error(`rt input.shape and shape=${shape} are incompatible: rt input.shape[${i + raggedRank}] = ${valueDim} but shape[${i + raggedRank}] = ${outputShapeDim}`);
            } else outputShape[outputShapeDimIndex] = valueDim;
        }
    }
    return outputShape;
}
function getRowPartitionTypesHelper(rowPartitionTypeStrings) {
    const stringToType = {
        "FIRST_DIM_SIZE": RowPartitionType.FIRST_DIM_SIZE,
        "VALUE_ROWIDS": RowPartitionType.VALUE_ROWIDS,
        "ROW_LENGTHS": RowPartitionType.ROW_LENGTHS,
        "ROW_SPLITS": RowPartitionType.ROW_SPLITS,
        "ROW_LIMITS": RowPartitionType.ROW_LIMITS,
        "ROW_STARTS": RowPartitionType.ROW_STARTS
    };
    const result = [];
    for (const typeStr of rowPartitionTypeStrings){
        if (typeStr in stringToType) result.push(stringToType[typeStr]);
        else break;
    }
    return result;
}
function getRaggedRank(rowPartitionTypes) {
    if (rowPartitionTypes.length === 0) return 0;
    if (rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) return rowPartitionTypes.length - 1;
    return rowPartitionTypes.length;
}
function validateDefaultValueShape(defaultValueShape, valueShape) {
    if (defaultValueShape == null || valueShape == null) return;
    const defaultNDims = defaultValueShape.length;
    const valuesNDims = valueShape.length;
    if (defaultNDims >= valuesNDims) throw new Error(`defaultValue.shape=${defaultValueShape} and ragged tensor flatValues.shape=${valueShape}, are incompatible: defaultValue.rank = ${defaultNDims} must be less than ragged tensor input flatValues.rank = ${valuesNDims})`);
    for(let i = 0; i < Math.min(defaultNDims, valuesNDims - 1); ++i){
        const defaultDim = defaultValueShape[i];
        const valueDim = valueShape[i + 1];
        if (defaultDim >= 0 && valueDim >= 0 && defaultDim !== 1 && defaultDim !== valueDim) throw new Error(`defaultValue.shape=${defaultValueShape}, and ragged tensor input flatValues.shape=${valueShape} are incompatible: defaultValue.shape[${i - defaultValueShape.length}] = ${defaultDim} but ragged tensor input.flatValues.shape[${i - defaultValueShape.length}] = ${valueDim}`);
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lSy0z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Inputs of size above this threshold will be parallelized by calling multiple
 * shader programs.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "PARALLELIZE_THRESHOLD", ()=>PARALLELIZE_THRESHOLD);
parcelHelpers.export(exports, "computeOptimalWindowSize", ()=>computeOptimalWindowSize);
var _util = require("../util");
const PARALLELIZE_THRESHOLD = 30;
function computeOptimalWindowSize(inSize) {
    if (inSize <= PARALLELIZE_THRESHOLD) return inSize;
    return (0, _util.nearestDivisor)(inSize, Math.floor(Math.sqrt(inSize)));
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"28w8O":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Returns the image center in pixels.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getImageCenter", ()=>getImageCenter);
function getImageCenter(center, imageHeight, imageWidth) {
    const centerX = imageWidth * (typeof center === "number" ? center : center[0]);
    const centerY = imageHeight * (typeof center === "number" ? center : center[1]);
    return [
        centerX,
        centerY
    ];
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fuOID":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Gets the new shape of the input Tensor after it's been reshaped
 * to:
 * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),
 * inputShape[1], ..., inputShape[N-1]]
 *
 * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getReshaped", ()=>getReshaped);
/**
 * Gets the permutation that will transpose the dimensions of the
 * reshaped tensor to shape:
 *
 * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,
 * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
 *
 * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */ parcelHelpers.export(exports, "getPermuted", ()=>getPermuted);
/**
 * Gets the shape of the reshaped and permuted input Tensor before any cropping
 * is applied.  The new shape will be:
 *
 * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,
 * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
 *
 * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */ parcelHelpers.export(exports, "getReshapedPermuted", ()=>getReshapedPermuted);
/**
 * Converts the crops argument into the beginning coordinates of a slice
 * operation.
 */ parcelHelpers.export(exports, "getSliceBeginCoords", ()=>getSliceBeginCoords);
/**
 * Converts the crops argument into the size of a slice operation.  When
 * combined with getSliceBeginCoords this function allows the reshaped and
 * permuted Tensor to be cropped to its final output shape of:
 *
 * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,
 * inputShape[M] * blockShape[M-1] -crops[M-1,0] -
 * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]
 *
 * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */ parcelHelpers.export(exports, "getSliceSize", ()=>getSliceSize);
function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {
    let reshaped = [];
    if (batchToSpace) {
        reshaped = reshaped.concat(blockShape.slice(0));
        reshaped.push(inputShape[0] / prod);
        reshaped = reshaped.concat(inputShape.slice(1));
    } else {
        reshaped = reshaped.concat(inputShape[0]);
        const spatialLength = blockShape.length;
        for(let i = 0; i < spatialLength; ++i)reshaped = reshaped.concat([
            inputShape[i + 1] / blockShape[i],
            blockShape[i]
        ]);
        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
    }
    return reshaped;
}
function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
    const permuted = [];
    if (batchToSpace) {
        permuted.push(blockShapeRank);
        for(let i = blockShapeRank + 1; i < reshapedRank; ++i)if (i <= 2 * blockShapeRank) {
            permuted.push(i);
            permuted.push(i - (blockShapeRank + 1));
        } else permuted.push(i);
    } else {
        const permutedBeforeBatch = [];
        const permutedAfterBatch = [];
        for(let i = 1; i < reshapedRank; ++i)if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) permutedAfterBatch.push(i);
        else permutedBeforeBatch.push(i);
        permuted.push(...permutedBeforeBatch);
        permuted.push(0);
        permuted.push(...permutedAfterBatch);
    }
    return permuted;
}
function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {
    const reshapedPermuted = [];
    if (batchToSpace) reshapedPermuted.push(inputShape[0] / prod);
    else reshapedPermuted.push(inputShape[0] * prod);
    for(let i = 1; i < inputShape.length; ++i)if (i <= blockShape.length) {
        if (batchToSpace) reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
        else reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
    } else reshapedPermuted.push(inputShape[i]);
    return reshapedPermuted;
}
function getSliceBeginCoords(crops, blockShape) {
    const sliceBeginCoords = [
        0
    ];
    for(let i = 0; i < blockShape; ++i)sliceBeginCoords.push(crops[i][0]);
    return sliceBeginCoords;
}
function getSliceSize(uncroppedShape, crops, blockShape) {
    const sliceSize = uncroppedShape.slice(0, 1);
    for(let i = 0; i < blockShape; ++i)sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
    return sliceSize;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"f1Rmi":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SELU_SCALEALPHA", ()=>SELU_SCALEALPHA);
parcelHelpers.export(exports, "SELU_SCALE", ()=>SELU_SCALE);
const SELU_SCALEALPHA = 1.7580993408473768599402175208123;
const SELU_SCALE = 1.0507009873554804934193349852946;

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lREHE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ERF_P", ()=>ERF_P);
parcelHelpers.export(exports, "ERF_A1", ()=>ERF_A1);
parcelHelpers.export(exports, "ERF_A2", ()=>ERF_A2);
parcelHelpers.export(exports, "ERF_A3", ()=>ERF_A3);
parcelHelpers.export(exports, "ERF_A4", ()=>ERF_A4);
parcelHelpers.export(exports, "ERF_A5", ()=>ERF_A5);
const ERF_P = 0.3275911;
const ERF_A1 = 0.254829592;
const ERF_A2 = -0.284496736;
const ERF_A3 = 1.421413741;
const ERF_A4 = -1.453152027;
const ERF_A5 = 1.061405429;

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eEkKB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Merges real and imaginary Float32Arrays into a single complex Float32Array.
 *
 * The memory layout is interleaved as follows:
 * real: [r0, r1, r2]
 * imag: [i0, i1, i2]
 * complex: [r0, i0, r1, i1, r2, i2]
 *
 * This is the inverse of splitRealAndImagArrays.
 *
 * @param real The real values of the complex tensor values.
 * @param imag The imag values of the complex tensor values.
 * @returns A complex tensor as a Float32Array with merged values.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mergeRealAndImagArrays", ()=>mergeRealAndImagArrays);
/**
 * Splits a complex Float32Array into real and imag parts.
 *
 * The memory layout is interleaved as follows:
 * complex: [r0, i0, r1, i1, r2, i2]
 * real: [r0, r1, r2]
 * imag: [i0, i1, i2]
 *
 * This is the inverse of mergeRealAndImagArrays.
 *
 * @param complex The complex tensor values.
 * @returns An object with real and imag Float32Array components of the complex
 *     tensor.
 */ parcelHelpers.export(exports, "splitRealAndImagArrays", ()=>splitRealAndImagArrays);
/**
 * Extracts even indexed complex values in the given array.
 * @param complex The complex tensor values
 */ parcelHelpers.export(exports, "complexWithEvenIndex", ()=>complexWithEvenIndex);
/**
 * Extracts odd indexed comple values in the given array.
 * @param complex The complex tensor values
 */ parcelHelpers.export(exports, "complexWithOddIndex", ()=>complexWithOddIndex);
/**
 * Get the map representing a complex value in the given array.
 * @param complex The complex tensor values.
 * @param index An index of the target complex value.
 */ parcelHelpers.export(exports, "getComplexWithIndex", ()=>getComplexWithIndex);
/**
 * Insert a given complex value into the TypedArray.
 * @param data The array in which the complex value is inserted.
 * @param c The complex value to be inserted.
 * @param index An index of the target complex value.
 */ parcelHelpers.export(exports, "assignToTypedArray", ()=>assignToTypedArray);
/**
 * Make the list of exponent terms used by FFT.
 */ parcelHelpers.export(exports, "exponents", ()=>exponents);
/**
 * Make the exponent term used by FFT.
 */ parcelHelpers.export(exports, "exponent", ()=>exponent);
function mergeRealAndImagArrays(real, imag) {
    if (real.length !== imag.length) throw new Error(`Cannot merge real and imag arrays of different lengths. real:` + `${real.length}, imag: ${imag.length}.`);
    const result = new Float32Array(real.length * 2);
    for(let i = 0; i < result.length; i += 2){
        result[i] = real[i / 2];
        result[i + 1] = imag[i / 2];
    }
    return result;
}
function splitRealAndImagArrays(complex) {
    const real = new Float32Array(complex.length / 2);
    const imag = new Float32Array(complex.length / 2);
    for(let i = 0; i < complex.length; i += 2){
        real[i / 2] = complex[i];
        imag[i / 2] = complex[i + 1];
    }
    return {
        real,
        imag
    };
}
function complexWithEvenIndex(complex) {
    const len = Math.ceil(complex.length / 4);
    const real = new Float32Array(len);
    const imag = new Float32Array(len);
    for(let i = 0; i < complex.length; i += 4){
        real[Math.floor(i / 4)] = complex[i];
        imag[Math.floor(i / 4)] = complex[i + 1];
    }
    return {
        real,
        imag
    };
}
function complexWithOddIndex(complex) {
    const len = Math.floor(complex.length / 4);
    const real = new Float32Array(len);
    const imag = new Float32Array(len);
    for(let i = 2; i < complex.length; i += 4){
        real[Math.floor(i / 4)] = complex[i];
        imag[Math.floor(i / 4)] = complex[i + 1];
    }
    return {
        real,
        imag
    };
}
function getComplexWithIndex(complex, index) {
    const real = complex[index * 2];
    const imag = complex[index * 2 + 1];
    return {
        real,
        imag
    };
}
function assignToTypedArray(data, real, imag, index) {
    data[index * 2] = real;
    data[index * 2 + 1] = imag;
}
function exponents(n, inverse) {
    const real = new Float32Array(n / 2);
    const imag = new Float32Array(n / 2);
    for(let i = 0; i < Math.ceil(n / 2); i++){
        const x = (inverse ? 2 : -2) * Math.PI * (i / n);
        real[i] = Math.cos(x);
        imag[i] = Math.sin(x);
    }
    return {
        real,
        imag
    };
}
function exponent(k, n, inverse) {
    const x = (inverse ? 2 : -2) * Math.PI * (k / n);
    const real = Math.cos(x);
    const imag = Math.sin(x);
    return {
        real,
        imag
    };
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4URRN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Parse an equation for einsum.
 *
 * @param equation The einsum equation (e.g., "ij,jk->ik").
 * @param numTensors Number of tensors provided along with `equation`. Used to
 *   check matching number of input tensors.
 * @returns An object consisting of the following fields:
 *   - allDims: all dimension names as strings.
 *   - summedDims: a list of all dimensions being summed over, as indices to
 *     the elements of `allDims`.
 *   - idDims: indices of the dimensions in each input tensor, as indices to
 *     the elements of `allDims.
 */ parcelHelpers.export(exports, "decodeEinsumEquation", ()=>decodeEinsumEquation);
/**
 * Get the permutation for a given input tensor.
 *
 * @param nDims Total number of dimension of all tensors involved in the einsum
 *   operation.
 * @param idDims Dimension indices involve in the tensor in question.
 * @returns An object consisting of the following fields:
 *   - permutationIndices: Indices to permute the axes of the tensor with.
 *   - expandDims: Indices to the dimension that need to be expanded from the
 *     tensor after permutation.
 */ parcelHelpers.export(exports, "getEinsumPermutation", ()=>getEinsumPermutation);
/**
 * Checks that the dimension sizes from different input tensors match the
 * equation.
 */ parcelHelpers.export(exports, "checkEinsumDimSizes", ()=>checkEinsumDimSizes);
/**
 * Gets path of computation for einsum.
 *
 * @param summedDims indices to the dimensions being summed over.
 * @param idDims A look up table for the dimensions present in each input
 *     tensor. Each consituent array contains indices for the dimensions in the
 *     corresponding input tensor.
 *
 * @return A map with two fields:
 *   - path: The path of computation, with each element indicating the dimension
 *     being summed over after the element-wise multiplication in that step.
 *   - steps: With the same length as `path`. Each element contains the indices
 *     to the input tensors being used for element-wise multiplication in the
 *     corresponding step.
 */ parcelHelpers.export(exports, "getEinsumComputePath", ()=>getEinsumComputePath);
/** Determines if an axes permutation is the identity permutation. */ parcelHelpers.export(exports, "isIdentityPermutation", ()=>isIdentityPermutation);
var _utilBase = require("../util_base");
const ARROW = "->";
const ARROW_REGEX = /->/g;
const COMMA = ",";
const ELLIPSIS = "...";
function decodeEinsumEquation(equation, numTensors) {
    equation = equation.replace(/\s/g, ""); // Remove witespace in equation.
    const numArrows = (equation.length - equation.replace(ARROW_REGEX, "").length) / ARROW.length;
    if (numArrows < 1) throw new Error("Equations without an arrow are not supported.");
    else if (numArrows > 1) throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`);
    const [inputString, outputString] = equation.split(ARROW);
    (0, _utilBase.assert)(inputString.indexOf(ELLIPSIS) === -1, ()=>`The ellipsis notation ("${ELLIPSIS}") is not supported yet.`);
    const inputTerms = inputString.split(COMMA);
    const numInputs = inputTerms.length;
    if (numTensors !== numInputs) throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);
    if (numInputs > 2) throw new Error("Support for more than 2 input tensors is not implemented yet.");
    const allDims = [];
    for(let i = 0; i < outputString.length; ++i){
        const dimName = outputString[i];
        if (!inputTerms.some((inputTerm)=>inputTerm.indexOf(dimName) !== -1)) throw new Error(`Output subscripts contain the label ${dimName} ` + `not present in the input subscripts.`);
        if (allDims.indexOf(dimName) === -1) allDims.push(dimName);
    }
    for(let i = 0; i < inputString.length; ++i){
        const dimName = inputString[i];
        if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) allDims.push(dimName);
    }
    const idDims = new Array(inputTerms.length);
    for(let i = 0; i < numInputs; ++i){
        if (new Set(inputTerms[i].split("")).size !== inputTerms[i].length) throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. ` + `Support for duplicate axes in input is not implemented yet.`);
        idDims[i] = [];
        for(let j = 0; j < inputTerms[i].length; ++j)idDims[i].push(allDims.indexOf(inputTerms[i][j]));
    }
    const numDims = allDims.length; // Number of unique dimensions.
    const numOutDims = outputString.length; // Number of output dimensions.
    const summedDims = []; // Dimensions being summed over.
    for(let i = numOutDims; i < numDims; ++i)summedDims.push(i);
    return {
        allDims,
        summedDims,
        idDims
    };
}
function getEinsumPermutation(nDims, idDims) {
    let permutationIndices = new Array(nDims);
    permutationIndices.fill(-1);
    for(let i = 0; i < idDims.length; ++i)permutationIndices[idDims[i]] = i;
    const expandDims = [];
    for(let i = 0; i < nDims; ++i)if (permutationIndices[i] === -1) expandDims.push(i);
    permutationIndices = permutationIndices.filter((d)=>d !== -1);
    return {
        permutationIndices,
        expandDims
    };
}
function checkEinsumDimSizes(nDims, idDims, tensors) {
    const dimSizes = new Array(nDims);
    for(let i = 0; i < tensors.length; ++i){
        const shape = tensors[i].shape;
        for(let j = 0; j < idDims[i].length; ++j)if (dimSizes[idDims[i][j]] === undefined) dimSizes[idDims[i][j]] = shape[j];
        else (0, _utilBase.assert)(dimSizes[idDims[i][j]] === shape[j], ()=>`Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} ` + `of input shaped ${JSON.stringify(shape)}, ` + `but got dimension ${shape[j]}`);
    }
}
function getEinsumComputePath(summedDims, idDims) {
    const path = summedDims;
    const steps = [];
    let nSteps = 0;
    if (summedDims.length === 0) // Einsum that involes no summing: e.g., transpose and outer product.
    path.push(-1);
    nSteps = summedDims.length + 1;
    for(let i = 0; i < nSteps; ++i)steps.push([]);
    const computedTermIndices = [];
    for(let i = 0; i < path.length; ++i){
        const summedDim = path[i];
        const termIndices = findTermsWithDim(idDims, summedDim);
        for (const termIndex of termIndices)if (computedTermIndices.indexOf(termIndex) === -1) {
            steps[i].push(termIndex);
            computedTermIndices.push(termIndex);
        }
    }
    return {
        path,
        steps
    };
}
function isIdentityPermutation(perm) {
    return perm.every((dim, index)=>dim === index);
}
function findTermsWithDim(idDims, dim) {
    const termIndices = [];
    for(let i = 0; i < idDims.length; ++i)if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) termIndices.push(i);
    return termIndices;
}

},{"../util_base":"4Vo4a","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5SjNI":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Prepare the split size array. When the input is a number, the axis is evenly
 * divided among the split size. When the input contains the negative value, the
 * rest of the axis is allocated toward that.
 */ parcelHelpers.export(exports, "prepareSplitSize", ()=>prepareSplitSize);
var _util = require("../util");
function prepareSplitSize(x, numOrSizeSplits, axis = 0) {
    let splitSizes = [];
    if (typeof numOrSizeSplits === "number") {
        (0, _util.assert)(x.shape[axis] % numOrSizeSplits === 0, ()=>"Number of splits must evenly divide the axis.");
        splitSizes = new Array(numOrSizeSplits).fill(x.shape[axis] / numOrSizeSplits);
    } else {
        const numOfNegs = numOrSizeSplits.reduce((count, value)=>{
            if (value === -1) count += 1;
            return count;
        }, 0);
        (0, _util.assert)(numOfNegs <= 1, ()=>"There should be only one negative value in split array.");
        const negIndex = numOrSizeSplits.indexOf(-1);
        // Allow the number of split array to be -1, which indicates the rest
        // of dimension is allocated to that split.
        if (negIndex !== -1) {
            const total = numOrSizeSplits.reduce((a, b)=>b > 0 ? a + b : a);
            numOrSizeSplits[negIndex] = x.shape[axis] - total;
        }
        (0, _util.assert)(x.shape[axis] === numOrSizeSplits.reduce((a, b)=>a + b), ()=>"The sum of sizes must match the size of the axis dimension.");
        splitSizes = numOrSizeSplits;
    }
    return splitSizes;
}

},{"../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6kj4w":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Generates sparse fill empty rows indices, dense shape mismatch error message.
 *
 * @param indicesLength The first dimension of indices.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getSparseFillEmptyRowsIndicesDenseShapeMismatch", ()=>getSparseFillEmptyRowsIndicesDenseShapeMismatch);
/**
 * Generates sparse fill empty rows negative index error message.
 *
 * @param index The index with a negative value.
 * @param value The negative value.
 */ parcelHelpers.export(exports, "getSparseFillEmptyRowsNegativeIndexErrorMessage", ()=>getSparseFillEmptyRowsNegativeIndexErrorMessage);
/**
 * Generates sparse fill empty rows out of range index error message.
 *
 * @param index The index with an out of range value.
 * @param value The out of range value.
 * @param limit The upper limit for indices.
 */ parcelHelpers.export(exports, "getSparseFillEmptyRowsOutOfRangeIndexErrorMessage", ()=>getSparseFillEmptyRowsOutOfRangeIndexErrorMessage);
function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {
    return `Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${indicesLength}`;
}
function getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {
    return `indices(${index}, 0) is invalid: ${value} < 0`;
}
function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {
    return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eH8pS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Generates sparse reshape multiple negative 1 output dimension error message.
 *
 * @param dim1 The first dimension with a negative 1 value.
 * @param dim2 The second dimension with a negative 1 value.
 */ parcelHelpers.export(exports, "getSparseReshapeMultipleNegativeOneOutputDimErrorMessage", ()=>getSparseReshapeMultipleNegativeOneOutputDimErrorMessage);
/**
 * Generates sparse reshape negative output dimension error message.
 *
 * @param dim The dimension with a negative value.
 * @param value The negative value.
 */ parcelHelpers.export(exports, "getSparseReshapeNegativeOutputDimErrorMessage", ()=>getSparseReshapeNegativeOutputDimErrorMessage);
/**
 * Generates sparse reshape empty tensor zero output dimension error message.
 *
 */ parcelHelpers.export(exports, "getSparseReshapeEmptyTensorZeroOutputDimErrorMessage", ()=>getSparseReshapeEmptyTensorZeroOutputDimErrorMessage);
/**
 * Generates sparse reshape input output multiple mismatch error message.
 *
 * @param inputShape the input shape.
 * @param outputShape the requested output shape.
 */ parcelHelpers.export(exports, "getSparseReshapeInputOutputMultipleErrorMessage", ()=>getSparseReshapeInputOutputMultipleErrorMessage);
/**
 * Generates sparse reshape input output inequality error message.
 *
 * @param inputShape the input shape.
 * @param outputShape the requested output shape.
 */ parcelHelpers.export(exports, "getSparseReshapeInputOutputMismatchErrorMessage", ()=>getSparseReshapeInputOutputMismatchErrorMessage);
var _util = require("../../util");
function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {
    return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;
}
function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {
    return `size ${dim} must be non-negative, not ${value}`;
}
function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {
    return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {
    const inputSize = (0, _util.sizeFromShape)(inputShape);
    const outputSize = (0, _util.sizeFromShape)(outputShape);
    return `Input to reshape is a SparseTensor with ${inputSize}
  dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;
}
function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {
    const inputSize = (0, _util.sizeFromShape)(inputShape);
    const outputSize = (0, _util.sizeFromShape)(outputShape);
    return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;
}

},{"../../util":"ctavo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lUp7V":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * Generates sparse segment reduction negative segment ids error message.
 *
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getSparseSegmentReductionNegativeSegmentIdsErrorMessage", ()=>getSparseSegmentReductionNegativeSegmentIdsErrorMessage);
/**
 * Generates sparse segment reduction non increasing segment ids error message.
 *
 */ parcelHelpers.export(exports, "getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage", ()=>getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage);
/**
 * Generates sparse segment reduction segment id out of range error message.
 *
 * @param segmentId The segment id index that is out of range.
 * @param outputRows Upper bound of valid segment id values.
 */ parcelHelpers.export(exports, "getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage", ()=>getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage);
/**
 * Generates sparse segment reduction input indice out of range error message.
 *
 * @param index The index that holds the out of range value.
 * @param indexValue The value that is out of range.
 * @param inputRows Upper bound of valid index values.
 */ parcelHelpers.export(exports, "getSparseSegmentReductionIndicesOutOfRangeErrorMessage", ()=>getSparseSegmentReductionIndicesOutOfRangeErrorMessage);
function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {
    return `segment ids must be >= 0`;
}
function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {
    return `segment ids are not increasing`;
}
function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {
    return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;
}
function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {
    return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dZEhU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "segOpComputeOptimalWindowSize", ()=>segOpComputeOptimalWindowSize);
parcelHelpers.export(exports, "computeOutShape", ()=>computeOutShape);
parcelHelpers.export(exports, "collectGatherOpShapeInfo", ()=>collectGatherOpShapeInfo);
var _util = require("../util");
var _reduceUtil = require("./reduce_util");
function segOpComputeOptimalWindowSize(inSize, numSegments) {
    let done = false;
    let res;
    if (inSize <= (0, _reduceUtil.PARALLELIZE_THRESHOLD)) {
        res = inSize;
        done = true;
    } else res = (0, _util.nearestDivisor)(inSize, Math.floor(Math.sqrt(inSize)));
    while(!done)if (res > numSegments || res === inSize) done = true;
    else res = (0, _util.nearestDivisor)(inSize, res + 1);
    return res;
}
function computeOutShape(aShape, axis, numSegments) {
    const outShape = [];
    const rank = aShape.length;
    for(let dim = 0; dim < rank; dim++)if (dim !== axis) outShape.push(aShape[dim]);
    else outShape.push(numSegments);
    return outShape;
}
function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
    const indicesRank = indices.shape.length;
    const xRank = x.shape.length;
    if (batchDims !== 0) {
        if (batchDims < -indicesRank || batchDims > indicesRank) throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);
    }
    if (batchDims < 0) batchDims += indicesRank;
    if (batchDims > xRank) throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
    ${xRank}).`);
    if (axis < batchDims) throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);
    for(let i = 0; i < batchDims; ++i){
        if (x.shape[i] !== indices.shape[i]) throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);
    }
    const dimSize = x.shape[axis];
    const outputShape = [];
    let batchSize = 1;
    let outerSize = 1;
    let sliceSize = 1;
    for(let i = 0; i < batchDims; ++i){
        outputShape.push(x.shape[i]);
        batchSize *= x.shape[i];
    }
    for(let i = batchDims; i < axis; i++){
        outputShape.push(x.shape[i]);
        outerSize *= x.shape[i];
    }
    for(let i = batchDims; i < indicesRank; i++)outputShape.push(indices.shape[i]);
    for(let i = axis + 1; i < xRank; i++){
        outputShape.push(x.shape[i]);
        sliceSize *= x.shape[i];
    }
    return {
        batchSize,
        sliceSize,
        outerSize,
        dimSize,
        outputShape
    };
}

},{"../util":"ctavo","./reduce_util":"lSy0z","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cylCc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV3Impl", ()=>(0, _nonMaxSuppressionImpl.nonMaxSuppressionV3Impl));
parcelHelpers.export(exports, "nonMaxSuppressionV4Impl", ()=>(0, _nonMaxSuppressionImpl.nonMaxSuppressionV4Impl));
parcelHelpers.export(exports, "nonMaxSuppressionV5Impl", ()=>(0, _nonMaxSuppressionImpl.nonMaxSuppressionV5Impl));
parcelHelpers.export(exports, "whereImpl", ()=>(0, _whereImpl.whereImpl));
var _nonMaxSuppressionImpl = require("./non_max_suppression_impl");
var _whereImpl = require("./where_impl");

},{"./non_max_suppression_impl":"e0x4w","./where_impl":"lNCmF","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bXLEs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // All exports from this package should be in base.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
var _base = require("./base");
parcelHelpers.exportAll(_base, exports);
var _registerAllKernels = require("./register_all_kernels");

},{"./base":"iTvOJ","./register_all_kernels":"es42p","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iTvOJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // base.ts is the webgl backend without auto kernel registration.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "version_webgl", ()=>(0, _version.version));
parcelHelpers.export(exports, "webgl", ()=>webgl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _backendWebgl = require("./backend_webgl");
var _version = require("./version");
// Export webgl utilities
var _webgl = require("./webgl");
parcelHelpers.exportAll(_webgl, exports);
if ((0, _tfjsCore.device_util).isBrowser()) (0, _tfjsCore.registerBackend)("webgl", ()=>new (0, _backendWebgl.MathBackendWebGL)(), 2 /* priority */ );
const webgl = {
    forceHalfFloat: (0, _webgl.forceHalfFloat)
};

},{"@tensorflow/tfjs-core":"21Ftl","./backend_webgl":"9XxNW","./version":"8XYZf","./webgl":"l4jNp","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9XxNW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Import webgl flags.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "EPSILON_FLOAT32", ()=>EPSILON_FLOAT32);
parcelHelpers.export(exports, "EPSILON_FLOAT16", ()=>EPSILON_FLOAT16);
parcelHelpers.export(exports, "getBinaryCache", ()=>getBinaryCache);
parcelHelpers.export(exports, "MathBackendWebGL", ()=>MathBackendWebGL);
var _flagsWebgl = require("./flags_webgl");
var _tfjsCore = require("@tensorflow/tfjs-core");
var _canvasUtil = require("./canvas_util");
var _decodeMatrixGpu = require("./decode_matrix_gpu");
var _decodeMatrixPackedGpu = require("./decode_matrix_packed_gpu");
var _encodeFloatGpu = require("./encode_float_gpu");
var _encodeFloatPackedGpu = require("./encode_float_packed_gpu");
var _encodeMatrixGpu = require("./encode_matrix_gpu");
var _encodeMatrixPackedGpu = require("./encode_matrix_packed_gpu");
var _gpgpuContext = require("./gpgpu_context");
var _gpgpuMath = require("./gpgpu_math");
var _shared = require("./kernel_utils/shared");
var _packGpu = require("./pack_gpu");
var _reshapePackedGpu = require("./reshape_packed_gpu");
var _texUtil = require("./tex_util");
var _textureManager = require("./texture_manager");
var _unaryopGpu = require("./unaryop_gpu");
var _unaryopPackedGpu = require("./unaryop_packed_gpu");
var _unpackGpu = require("./unpack_gpu");
var _webglUtil = require("./webgl_util");
const whereImpl = (0, _tfjsCore.kernel_impls).whereImpl;
const EPSILON_FLOAT32 = 1e-7;
const EPSILON_FLOAT16 = 1e-4;
const binaryCaches = {};
function getBinaryCache(webGLVersion) {
    if (webGLVersion in binaryCaches) return binaryCaches[webGLVersion];
    binaryCaches[webGLVersion] = {};
    return binaryCaches[webGLVersion];
}
// Empirically determined constant used to determine size threshold for handing
// off execution to the CPU.
const CPU_HANDOFF_SIZE_THRESHOLD = (0, _tfjsCore.env)().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
// Empirically determined constant used to decide the number of MB on GPU
// before we warn about high memory use. The MB are this constant * screen area
// * dpi / 1024 / 1024.
const BEFORE_PAGING_CONSTANT = 600;
function numMBBeforeWarning() {
    if ((0, _tfjsCore.env)().global.screen == null) return 1024; // 1 GB.
    return (0, _tfjsCore.env)().global.screen.height * (0, _tfjsCore.env)().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;
}
class MathBackendWebGL extends (0, _tfjsCore.KernelBackend) {
    nextDataId() {
        return MathBackendWebGL.nextDataId++;
    }
    constructor(gpuResource){
        super();
        // Maps data ids that have a pending read operation, to list of subscribers.
        this.pendingRead = new WeakMap();
        // List of data ids that are scheduled for disposal, but are waiting on a
        // pending read operation.
        this.pendingDisposal = new WeakSet();
        // Used to count the number of 'shallow' sliced tensors that point to the
        // same data id.
        this.dataRefCount = new WeakMap();
        this.numBytesInGPU = 0;
        // Accumulated time spent (including blocking) in uploading data to webgl.
        this.uploadWaitMs = 0;
        // Accumulated time spent (including blocking in downloading data from webgl.
        this.downloadWaitMs = 0;
        // record the last manual GL Flush time.
        this.lastGlFlushTime = 0;
        this.warnedAboutMemory = false;
        this.pendingDeletes = 0;
        this.disposed = false;
        if (!(0, _tfjsCore.env)().getBool("HAS_WEBGL")) throw new Error("WebGL is not supported on this device");
        let newGPGPU;
        if (gpuResource != null) {
            if (gpuResource instanceof (0, _gpgpuContext.GPGPUContext)) newGPGPU = gpuResource;
            else {
                const gl = (0, _canvasUtil.getWebGLContext)((0, _tfjsCore.env)().getNumber("WEBGL_VERSION"), gpuResource);
                newGPGPU = new (0, _gpgpuContext.GPGPUContext)(gl);
            }
            this.binaryCache = {};
            this.gpgpuCreatedLocally = false;
        } else {
            const gl = (0, _canvasUtil.getWebGLContext)((0, _tfjsCore.env)().getNumber("WEBGL_VERSION"));
            newGPGPU = new (0, _gpgpuContext.GPGPUContext)(gl);
            this.binaryCache = getBinaryCache((0, _tfjsCore.env)().getNumber("WEBGL_VERSION"));
            this.gpgpuCreatedLocally = true;
        }
        this.gpgpu = newGPGPU;
        this.canvas = this.gpgpu.gl.canvas;
        this.textureManager = new (0, _textureManager.TextureManager)(this.gpgpu);
        this.numMBBeforeWarning = numMBBeforeWarning();
        this.texData = new (0, _tfjsCore.DataStorage)(this, (0, _tfjsCore.engine)());
    }
    numDataIds() {
        return this.texData.numDataIds() - this.pendingDeletes;
    }
    // Writes a new entry to the data store with a WebGL texture, and registers it
    // to the texture manager.
    writeTexture(texture, shape, dtype, texHeight, texWidth, channels) {
        // Temporarily create an tensor info to make the texture compatible with
        // the runWebGLProgram's input.
        const input = this.makeTensorInfo(shape, dtype);
        const inData = this.texData.get(input.dataId);
        // Even though the input texture could be unpacked or dense packed, it is
        // always considered as unpacked for EncodeMatrixProgram.
        inData.isPacked = false;
        // Bind texture to the input tensor.
        inData.texture = {
            texture,
            texShape: [
                texHeight,
                texWidth
            ]
        };
        inData.texShape = [
            texHeight,
            texWidth
        ];
        const shapeAs3D = _webglUtil.getShapeAs3D(shape);
        const program = new (0, _encodeMatrixGpu.EncodeMatrixProgram)(shapeAs3D, false, channels);
        const output = this.runWebGLProgram(program, [
            input
        ], dtype, [
            [
                texHeight,
                texWidth
            ]
        ]);
        output.shape = shape;
        // Unbind the texture from the input tensor to avoid the texture being
        // released.
        inData.texture = null;
        this.disposeIntermediateTensorInfo(input);
        return output.dataId;
    }
    write(values, shape, dtype) {
        if ((0, _tfjsCore.env)().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || (0, _tfjsCore.env)().getBool("DEBUG")) this.checkNumericalProblems(values);
        if (dtype === "complex64" && values != null) throw new Error(`Cannot write to a complex64 dtype. ` + `Please use tf.complex(real, imag).`);
        const dataId = {
            id: this.nextDataId()
        };
        this.texData.set(dataId, {
            shape,
            dtype,
            values,
            usage: (0, _texUtil.TextureUsage).UPLOAD,
            refCount: 1
        });
        return dataId;
    }
    /** Return refCount of a `TensorData`. */ refCount(dataId) {
        if (this.texData.has(dataId)) {
            const tensorData = this.texData.get(dataId);
            return tensorData.refCount;
        }
        return 0;
    }
    /** Increase refCount of a `TextureData`. */ incRef(dataId) {
        const texData = this.texData.get(dataId);
        texData.refCount++;
    }
    /** Decrease refCount of a `TextureData`. */ decRef(dataId) {
        if (this.texData.has(dataId)) {
            const texData = this.texData.get(dataId);
            texData.refCount--;
        }
    }
    move(dataId, values, shape, dtype, refCount) {
        if ((0, _tfjsCore.env)().getBool("DEBUG")) this.checkNumericalProblems(values);
        if (dtype === "complex64") throw new Error(`Cannot write to a complex64 dtype. ` + `Please use tf.complex(real, imag).`);
        this.texData.set(dataId, {
            shape,
            dtype,
            values,
            usage: (0, _texUtil.TextureUsage).UPLOAD,
            refCount
        });
    }
    disposeIntermediateTensorInfo(tensorInfo) {
        this.disposeData(tensorInfo.dataId);
    }
    readSync(dataId) {
        const texData = this.texData.get(dataId);
        const { values, dtype, complexTensorInfos, slice, shape, isPacked } = texData;
        // The presence of `slice` indicates this tensor is a shallow slice of a
        // different tensor, and is using that original tensor's texture. Run
        // `clone` in order to copy that texture and read from it.
        if (slice != null) {
            let program;
            if (isPacked) program = new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(shape, _unaryopGpu.CLONE);
            else program = new (0, _unaryopGpu.UnaryOpProgram)(shape, _unaryopGpu.CLONE);
            const res = this.runWebGLProgram(program, [
                {
                    dataId,
                    shape,
                    dtype
                }
            ], dtype);
            const data = this.readSync(res.dataId);
            this.disposeIntermediateTensorInfo(res);
            return data;
        }
        if (values != null) return this.convertAndCacheOnCPU(dataId);
        if (dtype === "string") return values;
        const shouldTimeProgram = this.activeTimers != null;
        let start;
        if (shouldTimeProgram) start = (0, _tfjsCore.util).now();
        let result;
        if (dtype === "complex64") {
            const realValues = this.readSync(complexTensorInfos.real.dataId);
            const imagValues = this.readSync(complexTensorInfos.imag.dataId);
            result = (0, _tfjsCore.backend_util).mergeRealAndImagArrays(realValues, imagValues);
        } else result = this.getValuesFromTexture(dataId);
        if (shouldTimeProgram) this.downloadWaitMs += (0, _tfjsCore.util).now() - start;
        return this.convertAndCacheOnCPU(dataId, result);
    }
    async read(dataId) {
        if (this.pendingRead.has(dataId)) {
            const subscribers = this.pendingRead.get(dataId);
            return new Promise((resolve)=>subscribers.push(resolve));
        }
        const texData = this.texData.get(dataId);
        const { values, shape, slice, dtype, complexTensorInfos, isPacked } = texData;
        // The presence of `slice` indicates this tensor is a shallow slice of a
        // different tensor, and is using that original tensor's texture. Run
        // `clone` in order to copy that texture and read from it.
        if (slice != null) {
            let program;
            if (isPacked) program = new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(shape, _unaryopGpu.CLONE);
            else program = new (0, _unaryopGpu.UnaryOpProgram)(shape, _unaryopGpu.CLONE);
            const res = this.runWebGLProgram(program, [
                {
                    dataId,
                    shape,
                    dtype
                }
            ], dtype);
            const data = this.read(res.dataId);
            this.disposeIntermediateTensorInfo(res);
            return data;
        }
        if (values != null) return this.convertAndCacheOnCPU(dataId);
        if ((0, _tfjsCore.env)().getBool("DEBUG")) {
            // getBool('WEBGL_DOWNLOAD_FLOAT_ENABLED') caused a blocking GPU call.
            // For performance reason, only check it for debugging. In production,
            // it doesn't handle this use case anyway, so behavior is not changed.
            if (!(0, _tfjsCore.env)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && (0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` + `WEBGL_VERSION=2 not yet supported.`);
        }
        let buffer = null;
        let tmpDownloadTarget;
        if (dtype !== "complex64" && (0, _tfjsCore.env)().get("WEBGL_BUFFER_SUPPORTED")) {
            // Possibly copy the texture into a buffer before inserting a fence.
            tmpDownloadTarget = this.decode(dataId);
            const tmpData = this.texData.get(tmpDownloadTarget.dataId);
            buffer = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ..._texUtil.getDenseTexShape(shape));
        }
        this.pendingRead.set(dataId, []);
        if (dtype !== "complex64") // Create a fence and wait for it to resolve.
        await this.gpgpu.createAndWaitForFence();
        // Download the values from the GPU.
        let vals;
        if (dtype === "complex64") {
            const ps = await Promise.all([
                this.read(complexTensorInfos.real.dataId),
                this.read(complexTensorInfos.imag.dataId)
            ]);
            const realValues = ps[0];
            const imagValues = ps[1];
            vals = (0, _tfjsCore.backend_util).mergeRealAndImagArrays(realValues, imagValues);
        } else if (buffer == null) vals = this.getValuesFromTexture(dataId);
        else {
            const size = (0, _tfjsCore.util).sizeFromShape(shape);
            vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer, size);
        }
        if (tmpDownloadTarget != null) this.disposeIntermediateTensorInfo(tmpDownloadTarget);
        if (buffer != null) {
            const gl = this.gpgpu.gl;
            _webglUtil.callAndCheck(gl, ()=>gl.deleteBuffer(buffer));
        }
        const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
        const subscribers = this.pendingRead.get(dataId);
        this.pendingRead.delete(dataId);
        // Notify all pending reads.
        subscribers.forEach((resolve)=>resolve(dTypeVals));
        if (this.pendingDisposal.has(dataId)) {
            this.pendingDisposal.delete(dataId);
            if (this.disposeData(dataId)) (0, _tfjsCore.engine)().removeDataId(dataId, this);
            this.pendingDeletes--;
        }
        return dTypeVals;
    }
    /**
     * Read tensor to a new texture that is densely packed for ease of use.
     * @param dataId The source tensor.
     * @param options
     *     customTexShape: Optional. If set, will use the user defined texture
     *     shape to create the texture.
     */ readToGPU(dataId, options = {}) {
        const texData = this.texData.get(dataId);
        const { values, shape, slice, dtype, isPacked, texture } = texData;
        if (dtype === "complex64") throw new Error("Does not support reading texture for complex64 dtype.");
        // The presence of `slice` indicates this tensor is a shallow slice of a
        // different tensor, and is using that original tensor's texture. Run
        // `clone` in order to copy that texture and read from it.
        if (slice != null) {
            let program;
            if (isPacked) program = new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(shape, _unaryopGpu.CLONE);
            else program = new (0, _unaryopGpu.UnaryOpProgram)(shape, _unaryopGpu.CLONE);
            const res = this.runWebGLProgram(program, [
                {
                    dataId,
                    shape,
                    dtype
                }
            ], dtype);
            const gpuResouorce = this.readToGPU(res, options);
            this.disposeIntermediateTensorInfo(res);
            return gpuResouorce;
        }
        if (texture == null) {
            if (values != null) throw new Error("Data is not on GPU but on CPU.");
            else throw new Error("There is no data on GPU or CPU.");
        }
        // Decode the texture so that it is stored densely (using four channels).
        const tmpTarget = this.decode(dataId, options.customTexShape);
        // Make engine track this tensor, so that we can dispose it later.
        const tensorRef = (0, _tfjsCore.engine)().makeTensorFromTensorInfo(tmpTarget);
        const tmpData = this.texData.get(tmpTarget.dataId);
        return Object.assign({
            tensorRef
        }, tmpData.texture);
    }
    bufferSync(t) {
        const data = this.readSync(t.dataId);
        if (t.dtype === "string") try {
            // Decode the bytes into string.
            const strings = data.map((d)=>(0, _tfjsCore.util).decodeString(d));
            return (0, _tfjsCore.buffer)(t.shape, t.dtype, strings);
        } catch (_a) {
            throw new Error("Failed to decode encoded string bytes into utf-8");
        }
        return (0, _tfjsCore.buffer)(t.shape, t.dtype, data);
    }
    checkNumericalProblems(values) {
        if (values == null) return;
        for(let i = 0; i < values.length; i++){
            const num = values[i];
            if (!_webglUtil.canBeRepresented(num)) {
                if ((0, _tfjsCore.env)().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) throw Error(`The value ${num} cannot be represented with your ` + `current settings. Consider enabling float32 rendering: ` + `'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
                throw Error(`The value ${num} cannot be represented on this device.`);
            }
        }
    }
    getValuesFromTexture(dataId) {
        const { shape, dtype, isPacked } = this.texData.get(dataId);
        const size = (0, _tfjsCore.util).sizeFromShape(shape);
        if ((0, _tfjsCore.env)().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
            const tmpTarget = this.decode(dataId);
            const tmpData = this.texData.get(tmpTarget.dataId);
            const vals = this.gpgpu.downloadMatrixFromPackedTexture(tmpData.texture.texture, ..._texUtil.getDenseTexShape(shape)).subarray(0, size);
            this.disposeIntermediateTensorInfo(tmpTarget);
            return vals;
        }
        const shouldUsePackedProgram = (0, _tfjsCore.env)().getBool("WEBGL_PACK") && isPacked === true;
        const outputShape = shouldUsePackedProgram ? _webglUtil.getShapeAs3D(shape) : shape;
        const program = shouldUsePackedProgram ? new (0, _encodeFloatPackedGpu.EncodeFloatPackedProgram)(outputShape) : new (0, _encodeFloatGpu.EncodeFloatProgram)(outputShape);
        const output = this.runWebGLProgram(program, [
            {
                shape: outputShape,
                dtype,
                dataId
            }
        ], "float32");
        const tmpData = this.texData.get(output.dataId);
        const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);
        this.disposeIntermediateTensorInfo(output);
        return vals;
    }
    timerAvailable() {
        return (0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
    }
    time(f) {
        const oldActiveTimers = this.activeTimers;
        const newActiveTimers = [];
        let outerMostTime = false;
        if (this.programTimersStack == null) {
            this.programTimersStack = newActiveTimers;
            outerMostTime = true;
        } else this.activeTimers.push(newActiveTimers);
        this.activeTimers = newActiveTimers;
        f();
        // needing to split these up because util.flatten only accepts certain types
        const flattenedActiveTimerQueries = (0, _tfjsCore.util).flatten(this.activeTimers.map((d)=>d.query)).filter((d)=>d != null);
        const flattenedActiveTimerNames = (0, _tfjsCore.util).flatten(this.activeTimers.map((d)=>d.name)).filter((d)=>d != null);
        this.activeTimers = oldActiveTimers;
        if (outerMostTime) this.programTimersStack = null;
        const res = {
            uploadWaitMs: this.uploadWaitMs,
            downloadWaitMs: this.downloadWaitMs,
            kernelMs: null,
            wallMs: null // will be filled by the engine
        };
        return (async ()=>{
            if ((0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
                const kernelMs = await Promise.all(flattenedActiveTimerQueries);
                res["kernelMs"] = (0, _tfjsCore.util).sum(kernelMs);
                res["getExtraProfileInfo"] = ()=>kernelMs.map((d, i)=>({
                            name: flattenedActiveTimerNames[i],
                            ms: d
                        })).map((d)=>`${d.name}: ${d.ms}`).join(", ");
            } else res["kernelMs"] = {
                error: "WebGL query timers are not supported in this environment."
            };
            this.uploadWaitMs = 0;
            this.downloadWaitMs = 0;
            return res;
        })();
    }
    memory() {
        return {
            unreliable: false,
            numBytesInGPU: this.numBytesInGPU,
            numBytesInGPUAllocated: this.textureManager.numBytesAllocated,
            numBytesInGPUFree: this.textureManager.numBytesFree
        };
    }
    startTimer() {
        if ((0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) return this.gpgpu.beginQuery();
        return {
            startMs: (0, _tfjsCore.util).now(),
            endMs: null
        };
    }
    endTimer(query) {
        if ((0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
            this.gpgpu.endQuery();
            return query;
        }
        query.endMs = (0, _tfjsCore.util).now();
        return query;
    }
    async getQueryTime(query) {
        if ((0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) return this.gpgpu.waitForQueryAndGetTime(query);
        const timerQuery = query;
        return timerQuery.endMs - timerQuery.startMs;
    }
    /**
     * Decrease the RefCount on the dataId and dispose the memory if the dataId
     * has 0 refCount. If there are pending read on the data, the disposal would
     * added to the pending delete queue. Return true if the dataId is removed
     * from backend or the backend does not contain the dataId, false if the
     * dataId is not removed. Memory may or may not be released even when dataId
     * is removed, which also depends on dataRefCount, see `releaseGPU`.
     * @param dataId
     * @oaram force Optional, remove the data regardless of refCount
     */ disposeData(dataId, force = false) {
        if (this.pendingDisposal.has(dataId)) return false;
        // No-op if already disposed.
        if (!this.texData.has(dataId)) return true;
        // if force flag is set, change refCount to 0, this would ensure disposal
        // when added to the pendingDisposal queue. Memory may or may not be
        // released, which also depends on dataRefCount, see `releaseGPU`.
        if (force) this.texData.get(dataId).refCount = 0;
        else this.texData.get(dataId).refCount--;
        if (!force && this.texData.get(dataId).refCount > 0) return false;
        if (this.pendingRead.has(dataId)) {
            this.pendingDisposal.add(dataId);
            this.pendingDeletes++;
            return false;
        }
        this.releaseGPUData(dataId);
        const { complexTensorInfos } = this.texData.get(dataId);
        if (complexTensorInfos != null) {
            this.disposeData(complexTensorInfos.real.dataId, force);
            this.disposeData(complexTensorInfos.imag.dataId, force);
        }
        this.texData.delete(dataId);
        return true;
    }
    releaseGPUData(dataId) {
        const { texture, dtype, texShape, usage, isPacked, slice } = this.texData.get(dataId);
        const key = slice && slice.origDataId || dataId;
        const refCount = this.dataRefCount.get(key);
        if (refCount > 1) this.dataRefCount.set(key, refCount - 1);
        else {
            this.dataRefCount.delete(key);
            if (texture != null) {
                this.numBytesInGPU -= this.computeBytes(texShape, dtype);
                this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
            }
        }
        const texData = this.texData.get(dataId);
        texData.texture = null;
        texData.texShape = null;
        texData.isPacked = false;
        texData.slice = null;
    }
    getTexture(dataId) {
        this.uploadToGPU(dataId);
        return this.texData.get(dataId).texture.texture;
    }
    /**
     * Returns internal information for the specific data bucket. Used in unit
     * tests.
     */ getDataInfo(dataId) {
        return this.texData.get(dataId);
    }
    /*
    Tests whether all the inputs to an op are small and on the CPU. This heuristic
    determines when it would be faster to execute a kernel on the CPU. WebGL
    kernels opt into running this check and forwarding when appropriate.
    TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more
    sustainable strategy for optimizing backend execution of ops.
     */ shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {
        return (0, _tfjsCore.env)().getBool("WEBGL_CPU_FORWARD") && inputs.every((input)=>this.texData.get(input.dataId).texture == null && (0, _tfjsCore.util).sizeFromShape(input.shape) < sizeThreshold);
    }
    getGPGPUContext() {
        return this.gpgpu;
    }
    where(condition) {
        (0, _tfjsCore.backend_util).warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
        const condVals = condition.dataSync();
        return whereImpl(condition.shape, condVals);
    }
    packedUnaryOp(x, op, dtype) {
        const program = new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(x.shape, op);
        const outInfo = this.compileAndRun(program, [
            x
        ], dtype);
        return (0, _tfjsCore.engine)().makeTensorFromTensorInfo(outInfo);
    }
    // TODO(msoulanille) remove this once the backend has been modularized
    // a copy is needed here to break a circular dependency.
    // Also remove the op from unary_op.
    abs(x) {
        // TODO: handle cases when x is complex.
        if (this.shouldExecuteOnCPU([
            x
        ]) && x.dtype !== "complex64") {
            const outValues = (0, _shared.simpleAbsImplCPU)(this.texData.get(x.dataId).values);
            return this.makeOutput(x.shape, x.dtype, outValues);
        }
        if ((0, _tfjsCore.env)().getBool("WEBGL_PACK_UNARY_OPERATIONS")) return this.packedUnaryOp(x, _unaryopGpu.ABS, x.dtype);
        const program = new (0, _unaryopGpu.UnaryOpProgram)(x.shape, _unaryopGpu.ABS);
        const outInfo = this.compileAndRun(program, [
            x
        ]);
        return (0, _tfjsCore.engine)().makeTensorFromTensorInfo(outInfo);
    }
    makeTensorInfo(shape, dtype, values) {
        let dataId;
        if (dtype === "string" && values != null && values.length > 0 && (0, _tfjsCore.util).isString(values[0])) {
            const encodedValues = values.map((d)=>(0, _tfjsCore.util).encodeString(d));
            dataId = this.write(encodedValues, shape, dtype);
        } else dataId = this.write(values, shape, dtype);
        this.texData.get(dataId).usage = null;
        return {
            dataId,
            shape,
            dtype
        };
    }
    makeOutput(shape, dtype, values) {
        return (0, _tfjsCore.engine)().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
    }
    unpackTensor(input) {
        const program = new (0, _unpackGpu.UnpackProgram)(input.shape);
        return this.runWebGLProgram(program, [
            input
        ], input.dtype);
    }
    packTensor(input) {
        const program = new (0, _packGpu.PackProgram)(input.shape);
        const preventEagerUnpackingOutput = true;
        return this.runWebGLProgram(program, [
            input
        ], input.dtype, null, preventEagerUnpackingOutput);
    }
    packedReshape(input, afterShape) {
        const input3DShape = [
            _webglUtil.getBatchDim(input.shape),
            ..._webglUtil.getRowsCols(input.shape)
        ];
        const input3D = {
            dtype: input.dtype,
            shape: input3DShape,
            dataId: input.dataId
        };
        const afterShapeAs3D = [
            _webglUtil.getBatchDim(afterShape),
            ..._webglUtil.getRowsCols(afterShape)
        ];
        const program = new (0, _reshapePackedGpu.ReshapePackedProgram)(afterShapeAs3D, input3DShape);
        const preventEagerUnpackingOfOutput = true;
        const customValues = [
            input3DShape
        ];
        const output = this.runWebGLProgram(program, [
            input3D
        ], input.dtype, customValues, preventEagerUnpackingOfOutput);
        return {
            dataId: output.dataId,
            shape: afterShape,
            dtype: output.dtype
        };
    }
    decode(dataId, customTexShape) {
        const texData = this.texData.get(dataId);
        const { isPacked, shape, dtype } = texData;
        if (customTexShape != null) {
            const size = (0, _tfjsCore.util).sizeFromShape(shape);
            const texSize = customTexShape[0] * customTexShape[1] * 4;
            (0, _tfjsCore.util).assert(size <= texSize, ()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
        }
        const shapeAs3D = _webglUtil.getShapeAs3D(shape);
        let program;
        if (isPacked) program = new (0, _decodeMatrixPackedGpu.DecodeMatrixPackedProgram)(shapeAs3D);
        else program = new (0, _decodeMatrixGpu.DecodeMatrixProgram)(shapeAs3D);
        const preventEagerUnpackingOfOutput = true;
        const customValues = [
            customTexShape != null ? customTexShape : _texUtil.getDenseTexShape(shapeAs3D)
        ];
        const out = this.runWebGLProgram(program, [
            {
                shape: shapeAs3D,
                dtype,
                dataId
            }
        ], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);
        return {
            dtype,
            shape,
            dataId: out.dataId
        };
    }
    runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {
        const output = this.makeTensorInfo(program.outputShape, outputDtype);
        const outData = this.texData.get(output.dataId);
        if (program.packedOutput) outData.isPacked = true;
        if (program.outPackingScheme === _texUtil.PackingScheme.DENSE) {
            const texelShape = customTexShape != null ? customTexShape : _texUtil.getDenseTexShape(program.outputShape);
            // For a densely packed output, we explicitly set texShape
            // so it doesn't get assigned later according to our typical packing
            // scheme wherein a single texel can only contain values from adjacent
            // rows/cols.
            outData.texShape = texelShape.map((d)=>d * 2);
        }
        if (program.outTexUsage != null) outData.usage = program.outTexUsage;
        if ((0, _tfjsCore.util).sizeFromShape(output.shape) === 0) {
            // Short-circuit the computation since the result is empty (has 0 in its
            // shape).
            outData.values = (0, _tfjsCore.util).getTypedArrayFromDType(output.dtype, 0);
            return output;
        }
        const dataToDispose = [];
        const inputsData = inputs.map((input)=>{
            if (input.dtype === "complex64") throw new Error(`GPGPUProgram does not support complex64 input. For complex64 ` + `dtypes, please separate the program into real and imaginary ` + `parts.`);
            let texData = this.texData.get(input.dataId);
            if (texData.texture == null) {
                if (!program.packedInputs && (0, _tfjsCore.util).sizeFromShape(input.shape) <= (0, _tfjsCore.env)().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) // Upload small tensors that live on the CPU as uniforms, not as
                // textures. Do this only when the environment supports 32bit floats
                // due to problems when comparing 16bit floats with 32bit floats.
                // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it
                // possible for packed shaders to sample from uniforms.
                return {
                    shape: input.shape,
                    texData: null,
                    isUniform: true,
                    uniformValues: texData.values
                };
                // This ensures that if a packed program's inputs have not yet been
                // uploaded to the GPU, they get uploaded as packed right off the bat.
                if (program.packedInputs) {
                    texData.isPacked = true;
                    texData.shape = input.shape;
                }
            }
            this.uploadToGPU(input.dataId);
            if (!!texData.isPacked !== !!program.packedInputs) {
                input = texData.isPacked ? this.unpackTensor(input) : this.packTensor(input);
                dataToDispose.push(input);
                texData = this.texData.get(input.dataId);
            } else if (texData.isPacked && !_webglUtil.isReshapeFree(texData.shape, input.shape)) {
                // This is a special case where a texture exists for a tensor
                // but the shapes are incompatible (due to packing constraints) because
                // the tensor did not have a chance to go through the packed reshape
                // shader. This only happens when we reshape the *same* tensor to form
                // *distinct* inputs to an op, e.g. dotting a vector with itself. This
                // case will disappear once packed uploading is the default.
                const savedInput = input;
                const targetShape = input.shape;
                input.shape = texData.shape;
                input = this.packedReshape(input, targetShape);
                dataToDispose.push(input);
                texData = this.texData.get(input.dataId);
                savedInput.shape = targetShape;
            }
            return {
                shape: input.shape,
                texData,
                isUniform: false
            };
        });
        this.uploadToGPU(output.dataId);
        const outputData = {
            shape: output.shape,
            texData: outData,
            isUniform: false
        };
        const key = _gpgpuMath.makeShaderKey(program, inputsData, outputData);
        const binary = this.getAndSaveBinary(key, ()=>{
            return _gpgpuMath.compileProgram(this.gpgpu, program, inputsData, outputData);
        });
        const shouldTimeProgram = this.activeTimers != null;
        let query;
        if (shouldTimeProgram) query = this.startTimer();
        if (!(0, _tfjsCore.env)().get("ENGINE_COMPILE_ONLY")) _gpgpuMath.runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);
        dataToDispose.forEach((info)=>this.disposeIntermediateTensorInfo(info));
        if (shouldTimeProgram) {
            query = this.endTimer(query);
            this.activeTimers.push({
                name: program.constructor.name,
                query: this.getQueryTime(query)
            });
        }
        const glFlushThreshold = (0, _tfjsCore.env)().getNumber("WEBGL_FLUSH_THRESHOLD");
        // Manually GL flush requested
        if (glFlushThreshold > 0) {
            const time = (0, _tfjsCore.util).now();
            if (time - this.lastGlFlushTime > glFlushThreshold) {
                this.gpgpu.gl.flush();
                this.lastGlFlushTime = time;
            }
        }
        if (!(0, _tfjsCore.env)().getBool("WEBGL_LAZILY_UNPACK") && outData.isPacked && preventEagerUnpackingOfOutput === false) {
            const unpacked = this.unpackTensor(output);
            this.disposeIntermediateTensorInfo(output);
            return unpacked;
        }
        return output;
    }
    compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {
        outputDtype = outputDtype || inputs[0].dtype;
        const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);
        return outInfo;
    }
    getAndSaveBinary(key, getBinary) {
        if (!(key in this.binaryCache)) this.binaryCache[key] = getBinary();
        return this.binaryCache[key];
    }
    getTextureManager() {
        return this.textureManager;
    }
    dispose() {
        if (this.disposed) return;
        // Avoid disposing the compiled webgl programs during unit testing because
        // it slows down test execution.
        if (!(0, _tfjsCore.env)().getBool("IS_TEST")) {
            const allKeys = Object.keys(this.binaryCache);
            allKeys.forEach((key)=>{
                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
                delete this.binaryCache[key];
            });
        }
        this.textureManager.dispose();
        if (this.canvas != null && typeof HTMLCanvasElement !== "undefined" && this.canvas instanceof HTMLCanvasElement) this.canvas.remove();
        else this.canvas = null;
        if (this.gpgpuCreatedLocally) {
            this.gpgpu.program = null;
            this.gpgpu.dispose();
        }
        this.disposed = true;
    }
    floatPrecision() {
        if (this.floatPrecisionValue == null) this.floatPrecisionValue = (0, _tfjsCore.tidy)(()=>{
            if (!(0, _tfjsCore.env)().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
                // Momentarily switching DEBUG flag to false so we don't throw an
                // error trying to upload a small value.
                const debugFlag = (0, _tfjsCore.env)().getBool("DEBUG");
                (0, _tfjsCore.env)().set("DEBUG", false);
                const underflowCheckValue = this.abs((0, _tfjsCore.scalar)(1e-8)).dataSync()[0];
                (0, _tfjsCore.env)().set("DEBUG", debugFlag);
                if (underflowCheckValue > 0) return 32;
            }
            return 16;
        });
        return this.floatPrecisionValue;
    }
    /** Returns the smallest representable number.  */ epsilon() {
        return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
    }
    uploadToGPU(dataId) {
        const texData = this.texData.get(dataId);
        const { shape, dtype, values, texture, usage, isPacked } = texData;
        if (texture != null) // Array is already on GPU. No-op.
        return;
        const shouldTimeProgram = this.activeTimers != null;
        let start;
        if (shouldTimeProgram) start = (0, _tfjsCore.util).now();
        let texShape = texData.texShape;
        if (texShape == null) {
            // This texShape may not be the final texture shape. For packed or dense
            // textures, the texShape will be changed when textures are created.
            texShape = _webglUtil.getTextureShapeFromLogicalShape(shape, isPacked);
            texData.texShape = texShape;
        }
        if (values != null) {
            const shapeAs3D = _webglUtil.getShapeAs3D(shape);
            let program;
            let width = texShape[1], height = texShape[0];
            const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;
            // texture for float array is PhysicalTextureType.PACKED_2X2_FLOAT32, we
            // need to make sure the upload uses the same packed size
            if (isPacked || !isByteArray) [width, height] = _texUtil.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);
            if (isPacked) program = new (0, _encodeMatrixPackedGpu.EncodeMatrixPackedProgram)(shapeAs3D, isByteArray);
            else program = new (0, _encodeMatrixGpu.EncodeMatrixProgram)(shapeAs3D, isByteArray);
            // TexShape for float array needs to be the original shape, which byte
            // array needs to be packed size. This allow the data upload shape to be
            // matched with texture creation logic.
            const tempDenseInputTexShape = isByteArray ? [
                height,
                width
            ] : texShape;
            const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);
            const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);
            if (isByteArray) tempDenseInputTexData.usage = (0, _texUtil.TextureUsage).PIXELS;
            else tempDenseInputTexData.usage = (0, _texUtil.TextureUsage).UPLOAD;
            tempDenseInputTexData.texShape = tempDenseInputTexShape;
            this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);
            const customValues = [
                [
                    height,
                    width
                ]
            ];
            // We want the output to remain packed regardless of the value of
            // WEBGL_PACK.
            const preventEagerUnpacking = true;
            const encodedOutputTarget = this.runWebGLProgram(program, [
                tempDenseInputHandle
            ], dtype, customValues, preventEagerUnpacking);
            // Have the original texture assume the identity of the encoded output.
            const outputTexData = this.texData.get(encodedOutputTarget.dataId);
            texData.texShape = outputTexData.texShape;
            texData.isPacked = outputTexData.isPacked;
            texData.usage = outputTexData.usage;
            if (!(0, _tfjsCore.env)().get("ENGINE_COMPILE_ONLY")) {
                texData.texture = outputTexData.texture;
                // Once uploaded, don't store the values on cpu.
                texData.values = null;
                this.texData.delete(encodedOutputTarget.dataId);
            } else this.disposeData(encodedOutputTarget.dataId);
            this.disposeIntermediateTensorInfo(tempDenseInputHandle);
            if (shouldTimeProgram) this.uploadWaitMs += (0, _tfjsCore.util).now() - start;
        } else {
            const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
            texData.texture = newTexture;
        }
    }
    convertAndCacheOnCPU(dataId, float32Values) {
        const texData = this.texData.get(dataId);
        const { dtype } = texData;
        if (float32Values != null) texData.values = float32ToTypedArray(float32Values, dtype);
        return texData.values;
    }
    acquireTexture(texShape, texType, dtype, isPacked) {
        this.numBytesInGPU += this.computeBytes(texShape, dtype);
        if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1048576) {
            const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
            this.warnedAboutMemory = true;
            console.warn(`High memory usage in GPU: ${mb} MB, ` + `most likely due to a memory leak`);
        }
        return this.textureManager.acquireTexture(texShape, texType, isPacked);
    }
    computeBytes(shape, dtype) {
        return shape[0] * shape[1] * (0, _tfjsCore.util).bytesPerElement(dtype);
    }
    checkCompileCompletion() {
        for (const [, binary] of Object.entries(this.binaryCache))this.checkCompletion_(binary);
    }
    async checkCompileCompletionAsync() {
        const ps = [];
        if (this.gpgpu.parallelCompilationExtension) {
            for (const [, binary] of Object.entries(this.binaryCache))ps.push(this.checkCompletionAsync_(binary));
            return Promise.all(ps);
        } else {
            for (const [, binary] of Object.entries(this.binaryCache)){
                const p = new Promise((resolve)=>{
                    try {
                        this.checkCompletion_(binary);
                        resolve(true);
                    } catch (error) {
                        throw error;
                    }
                });
                ps.push(p);
            }
            return Promise.all(ps);
        }
    }
    async checkCompletionAsync_(binary) {
        if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) return this.checkCompletion_(binary);
        else {
            await (0, _tfjsCore.nextFrame)();
            return this.checkCompletionAsync_(binary);
        }
    }
    checkCompletion_(binary) {
        if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {
            console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));
            if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {
                _webglUtil.logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));
                throw new Error("Failed to compile fragment shader.");
            }
            throw new Error("Failed to link vertex and fragment shaders.");
        }
        return true;
    }
    getUniformLocations() {
        for (const binary of Object.values(this.binaryCache)){
            // TODO: Iterating through all binaries to build VAOs is supposed to be in
            // a seperate function, like 'setVaos'. However, to avoid breaking changes
            // for the users using parallel compile feature now, buildVao is silently
            // added here.
            this.gpgpu.buildVao(binary.webGLProgram);
            const { variablesLocations, customUniformLocations, infLoc, nanLoc, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = (0, _gpgpuMath.getUniformLocations)(this.gpgpu, binary.program, binary.webGLProgram);
            binary.variablesLocations = variablesLocations;
            binary.customUniformLocations = customUniformLocations;
            binary.infLoc = infLoc;
            binary.nanLoc = nanLoc;
            binary.outShapeLocation = outShapeLocation;
            binary.outShapeStridesLocation = outShapeStridesLocation;
            binary.outTexShapeLocation = outTexShapeLocation;
        }
    }
    /**
     * Create a TF.js tensor out of an existing WebGL texture. A new texture will
     * be created.
     */ createTensorFromGPUData(values, shape, dtype) {
        values.channels = values.channels || "RGBA";
        const { texture, height, width, channels } = values;
        const backend = (0, _tfjsCore.engine)().backend;
        // Have to throw an error, otherwise WebGL just warns and returns wrong
        // values.
        if (!backend.gpgpu.gl.isTexture(texture)) throw new Error(`The texture is invalid. Also, please make sure the texture and ` + `the TFJS WebGL backend are using the same canvas. If you want to ` + `use your own custom canvas, you have to create and use the custom ` + `TFJS WebGL backend created from the canvas through ` + `'new tf.MathBackendWebGL(customCanvas)'.`);
        const dataId = backend.writeTexture(texture, shape, dtype, height, width, channels);
        return (0, _tfjsCore.engine)().makeTensorFromDataId(dataId, shape, dtype, backend);
    }
}
MathBackendWebGL.nextDataId = 0;
function float32ToTypedArray(a, dtype) {
    if (dtype === "float32" || dtype === "complex64") return a;
    else if (dtype === "int32" || dtype === "bool") {
        const result = dtype === "int32" ? new Int32Array(a.length) : new Uint8Array(a.length);
        for(let i = 0; i < result.length; ++i)result[i] = Math.round(a[i]);
        return result;
    } else throw new Error(`Unknown dtype ${dtype}`);
}

},{"./flags_webgl":"fXPM4","@tensorflow/tfjs-core":"21Ftl","./canvas_util":"1tNGf","./decode_matrix_gpu":"b07CY","./decode_matrix_packed_gpu":"gZFiF","./encode_float_gpu":"g6NXt","./encode_float_packed_gpu":"8lMpY","./encode_matrix_gpu":"4fC2J","./encode_matrix_packed_gpu":"aBUBT","./gpgpu_context":"9nvvF","./gpgpu_math":"btJo5","./kernel_utils/shared":"e4M7v","./pack_gpu":"1FCRm","./reshape_packed_gpu":"6lTtb","./tex_util":"1oYRe","./texture_manager":"72VRa","./unaryop_gpu":"gIMrR","./unaryop_packed_gpu":"c4D5K","./unpack_gpu":"6fxgJ","./webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fXPM4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var _tfjsCore = require("@tensorflow/tfjs-core");
var _webglUtil = require("./webgl_util");
const ENV = (0, _tfjsCore.env)();
/**
 * This file contains WebGL-specific flag registrations.
 */ /**
 * True if WebGL is supported.
 */ ENV.registerFlag("HAS_WEBGL", ()=>ENV.getNumber("WEBGL_VERSION") > 0);
/** 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0. */ ENV.registerFlag("WEBGL_VERSION", ()=>{
    if ((0, _webglUtil.isWebGLVersionEnabled)(2)) return 2;
    else if ((0, _webglUtil.isWebGLVersionEnabled)(1)) return 1;
    return 0;
});
/** Whether to check for numerical representation problems. */ ENV.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", ()=>false);
ENV.registerFlag("WEBGL_BUFFER_SUPPORTED", ()=>ENV.get("WEBGL_VERSION") === 2);
/** Whether the WebGL backend will sometimes forward ops to the CPU. */ ENV.registerFlag("WEBGL_CPU_FORWARD", ()=>true);
/** Whether the WebGL backend will always use f16 textures for rendering. */ ENV.registerFlag("WEBGL_FORCE_F16_TEXTURES", ()=>false);
/** Whether to turn all packing related flags on. */ ENV.registerFlag("WEBGL_PACK", ()=>ENV.getBool("HAS_WEBGL"));
/** Whether we will pack the batchnormalization op. */ ENV.registerFlag("WEBGL_PACK_NORMALIZATION", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack the clip op. */ ENV.registerFlag("WEBGL_PACK_CLIP", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack the depthwise conv op. */ ENV.registerFlag("WEBGL_PACK_DEPTHWISECONV", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack binary ops. */ ENV.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack unary ops. */ ENV.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack array ops. */ ENV.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack image ops. */ ENV.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack reduce ops. */ ENV.registerFlag("WEBGL_PACK_REDUCE", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether packed WebGL kernels lazily unpack their outputs. */ ENV.registerFlag("WEBGL_LAZILY_UNPACK", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will use the im2col algorithm to speed up convolutions. */ ENV.registerFlag("WEBGL_CONV_IM2COL", ()=>ENV.getBool("WEBGL_PACK"));
/** Whether we will pack conv2dTranspose op. */ ENV.registerFlag("WEBGL_PACK_CONV2DTRANSPOSE", ()=>ENV.getBool("WEBGL_PACK"));
/** The maximum texture dimension. */ ENV.registerFlag("WEBGL_MAX_TEXTURE_SIZE", ()=>(0, _webglUtil.getWebGLMaxTextureSize)(ENV.getNumber("WEBGL_VERSION")));
/** The maximum texture dimension. */ ENV.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", ()=>(0, _webglUtil.getMaxTexturesInShader)(ENV.getNumber("WEBGL_VERSION")));
/**
 * The disjoint_query_timer extension version.
 * 0: disabled, 1: EXT_disjoint_timer_query, 2:
 * EXT_disjoint_timer_query_webgl2.
 * In Firefox with WebGL 2.0,
 * EXT_disjoint_timer_query_webgl2 is not available, so we must use the
 * WebGL 1.0 extension.
 */ ENV.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", ()=>{
    const webGLVersion = ENV.getNumber("WEBGL_VERSION");
    if (webGLVersion === 0) return 0;
    return (0, _webglUtil.getWebGLDisjointQueryTimerVersion)(webGLVersion);
});
/**
 * Whether the timer object from the disjoint_query_timer extension gives
 * timing information that is reliable.
 */ ENV.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", ()=>ENV.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !(0, _tfjsCore.device_util).isMobile());
/**
 * Whether the device is physically capable of rendering to float32 textures.
 */ ENV.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", ()=>(0, _webglUtil.isCapableOfRenderingToFloatTexture)(ENV.getNumber("WEBGL_VERSION")));
/**
 * Whether rendering to float32 textures is enabled. If disabled, renders to
 * float16 textures.
 */ ENV.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", ()=>{
    return ENV.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV.getBool("WEBGL_RENDER_FLOAT32_CAPABLE");
});
/**
 * Whether downloading float textures is enabled (16 or 32 bit). If disabled,
 * uses IEEE 754 encoding of the float32 values to 4 uint8 when downloading.
 */ ENV.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", ()=>(0, _webglUtil.isDownloadFloatTextureEnabled)(ENV.getNumber("WEBGL_VERSION")));
/** Whether the fence API is available. */ ENV.registerFlag("WEBGL_FENCE_API_ENABLED", ()=>(0, _webglUtil.isWebGLFenceEnabled)(ENV.getNumber("WEBGL_VERSION")));
/**
 * Tensors with size <= than this will be uploaded as uniforms, not textures.
 */ ENV.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", ()=>{
    // Use uniform uploads only when 32bit floats are supported. In
    // 16bit
    // environments there are problems with comparing a 16bit texture value
    // with a 32bit uniform value.
    const useUniforms = ENV.getBool("WEBGL_RENDER_FLOAT32_ENABLED");
    return useUniforms ? 4 : 0;
});
/**
 * If the total number of bytes allocated on the GPU is greater than this
 * number, we will aggressively delete textures upon disposal with
 * gl.deleteMatrixTexture, rather than making them available for reuse.
 *
 * Default value -1 indicates that we will never aggressively delete textures.
 */ ENV.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", ()=>{
    return -1;
}, (threshold)=>{
    if (!(typeof threshold === "number")) throw new Error("WEBGL_DELETE_TEXTURE_THRESHOLD must be a number but " + `got ${threshold}.`);
    if (threshold < 0 && threshold !== -1) throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never ` + `delete) or at least 0, but got ${threshold}.`);
});
/**
 * Trigger a manual GL command flush if the threshold of time has passed since
 * previous Kernel execution. This can be useful for Andorid device where GL
 * command flush are delayed un til the end of javascript task. This value is
 * measured in millisecond. Typically you want to set this value to close to 1.
 *
 * Default value 1 for mobile chrome, and -1 for rest cases. -1 indicates that
 * we will not enforce manual flush and depend on system default flush schedule.
 */ ENV.registerFlag("WEBGL_FLUSH_THRESHOLD", ()=>{
    return (0, _tfjsCore.device_util).isMobile() ? 1 : -1;
}, (threshold)=>{
    if (!(typeof threshold === "number")) throw new Error("WEBGL_FLUSH_THRESHOLD must be a number but got " + `${threshold}.`);
    if (threshold < 0 && threshold !== -1) throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never ` + `manual flush) or at least 0, but got ${threshold}.`);
});
/**
 * Threshold for input tensor size that determines whether WebGL backend will
 * delegate computation to CPU.
 *
 * Default value is 128.
 */ ENV.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", ()=>128);
/** Whether we will use shapes uniforms. */ ENV.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", ()=>false);
/**
 * Threshold for last dimension of input tensor that determines whether
 * WebGL backend for the Top K op will delegate computation to CPU. If input
 * is smaller than threshold then CPU will be used
 *
 * Default value is 100000.
 */ ENV.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", ()=>100000);
/**
 * Threshold for K that determines whether
 * WebGL backend for the Top K op will delegate computation to CPU. If k
 * is larger than threshold then CPU will be used
 *
 * Default value is 128.
 */ ENV.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", ()=>128);
/** Whether we will use the experimental conv op. */ ENV.registerFlag("WEBGL_EXP_CONV", ()=>false);
/**
 * If the device performance is low or if no hardware GPU is available, whether
 * software WebGL will be used.
 */ ENV.registerFlag("SOFTWARE_WEBGL_ENABLED", ()=>ENV.getBool("IS_TEST"));
/**
 * For narrow texture (physical height or physical width is 1), if the length of
 * any texture edges exceed the threshold, the texture will be reshaped to be
 * more squarish.
 *
 * This flag is used to help some GPUs that could not provide correct
 * interpolations for long skinny triangles. We found Mali GPU probably has this
 * problem: https://github.com/tensorflow/tfjs/issues/6775.
 */ ENV.registerFlag("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE", ()=>Infinity);
/**
 * If the flag is set to true, the max size of the narrow texture will be auto
 * computed and it will be considerred as a threshold to reshape the narrow
 * texture to be more squarish.
 *
 * This flag is used to help some GPUs that could not provide correct
 * interpolations for long skinny triangles. We found Mali GPU probably has this
 * problem: https://github.com/tensorflow/tfjs/issues/6775.
 */ ENV.registerFlag("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE", ()=>false);
/**
 * Whether to use the customized isnan. It's only useful for webgl2 since webgl1
 * doesn't have the builtin isnan.
 */ ENV.registerFlag("WEBGL2_ISNAN_CUSTOM", ()=>false);
/** Experimental flag, whether enter compile only phase. */ ENV.registerFlag("ENGINE_COMPILE_ONLY", ()=>false);

},{"@tensorflow/tfjs-core":"21Ftl","./webgl_util":"fjf7t"}],"fjf7t":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "callAndCheck", ()=>callAndCheck);
parcelHelpers.export(exports, "canBeRepresented", ()=>canBeRepresented);
parcelHelpers.export(exports, "getWebGLErrorMessage", ()=>getWebGLErrorMessage);
parcelHelpers.export(exports, "getExtensionOrThrow", ()=>getExtensionOrThrow);
parcelHelpers.export(exports, "createVertexShader", ()=>createVertexShader);
parcelHelpers.export(exports, "createFragmentShader", ()=>createFragmentShader);
parcelHelpers.export(exports, "logShaderSourceAndInfoLog", ()=>logShaderSourceAndInfoLog);
parcelHelpers.export(exports, "createProgram", ()=>createProgram);
parcelHelpers.export(exports, "linkProgram", ()=>linkProgram);
/// validateProgram is effectively "If we `useProgram(program); drawArrays();`,
/// give feedback in log about perf/correctness warnings or errors that would
/// occur."
/// So make sure we set up all vertex/texture/sampler/uniform data before
/// calling validateProgram!
parcelHelpers.export(exports, "validateProgram", ()=>validateProgram);
parcelHelpers.export(exports, "createStaticVertexBuffer", ()=>createStaticVertexBuffer);
parcelHelpers.export(exports, "createStaticIndexBuffer", ()=>createStaticIndexBuffer);
parcelHelpers.export(exports, "getNumChannels", ()=>getNumChannels);
parcelHelpers.export(exports, "createTexture", ()=>createTexture);
parcelHelpers.export(exports, "validateTextureSize", ()=>validateTextureSize);
parcelHelpers.export(exports, "createFramebuffer", ()=>createFramebuffer);
parcelHelpers.export(exports, "bindVertexBufferToProgramAttribute", ()=>bindVertexBufferToProgramAttribute);
parcelHelpers.export(exports, "bindTextureUnit", ()=>bindTextureUnit);
parcelHelpers.export(exports, "unbindTextureUnit", ()=>unbindTextureUnit);
parcelHelpers.export(exports, "getProgramUniformLocationOrThrow", ()=>getProgramUniformLocationOrThrow);
parcelHelpers.export(exports, "getProgramUniformLocation", ()=>getProgramUniformLocation);
parcelHelpers.export(exports, "bindTextureToProgramUniformSampler", ()=>bindTextureToProgramUniformSampler);
parcelHelpers.export(exports, "bindCanvasToFramebuffer", ()=>bindCanvasToFramebuffer);
parcelHelpers.export(exports, "bindColorTextureToFramebuffer", ()=>bindColorTextureToFramebuffer);
parcelHelpers.export(exports, "unbindColorTextureFromFramebuffer", ()=>unbindColorTextureFromFramebuffer);
parcelHelpers.export(exports, "validateFramebuffer", ()=>validateFramebuffer);
parcelHelpers.export(exports, "getFramebufferErrorMessage", ()=>getFramebufferErrorMessage);
parcelHelpers.export(exports, "getBatchDim", ()=>getBatchDim);
parcelHelpers.export(exports, "getRowsCols", ()=>getRowsCols);
parcelHelpers.export(exports, "getShapeAs3D", ()=>getShapeAs3D);
parcelHelpers.export(exports, "getTextureShapeFromLogicalShape", ()=>getTextureShapeFromLogicalShape);
/**
 * This determines whether reshaping a packed texture requires rearranging
 * the data within the texture, assuming 2x2 packing.
 */ parcelHelpers.export(exports, "isReshapeFree", ()=>isReshapeFree);
parcelHelpers.export(exports, "getWebGLMaxTextureSize", ()=>getWebGLMaxTextureSize);
parcelHelpers.export(exports, "resetMaxTextureSize", ()=>resetMaxTextureSize);
parcelHelpers.export(exports, "resetMaxTexturesInShader", ()=>resetMaxTexturesInShader);
parcelHelpers.export(exports, "getMaxTexturesInShader", ()=>getMaxTexturesInShader);
parcelHelpers.export(exports, "getWebGLDisjointQueryTimerVersion", ()=>getWebGLDisjointQueryTimerVersion);
parcelHelpers.export(exports, "hasExtension", ()=>hasExtension);
parcelHelpers.export(exports, "isWebGLVersionEnabled", ()=>isWebGLVersionEnabled);
parcelHelpers.export(exports, "isCapableOfRenderingToFloatTexture", ()=>isCapableOfRenderingToFloatTexture);
/**
 * Check if we can download values from a float/half-float texture.
 *
 * Note that for performance reasons we use binding a texture to a framebuffer
 * as a proxy for ability to download float values later using readPixels. The
 * texture params of this texture will not match those in readPixels exactly
 * but if we are unable to bind some kind of float texture to the frameBuffer
 * then we definitely will not be able to read float values from it.
 */ parcelHelpers.export(exports, "isDownloadFloatTextureEnabled", ()=>isDownloadFloatTextureEnabled);
parcelHelpers.export(exports, "isWebGLFenceEnabled", ()=>isWebGLFenceEnabled);
parcelHelpers.export(exports, "assertNotComplex", ()=>assertNotComplex);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _canvasUtil = require("./canvas_util");
var _texUtil = require("./tex_util");
function callAndCheck(gl, func) {
    const returnValue = func();
    if ((0, _tfjsCore.env)().getBool("DEBUG")) checkWebGLError(gl);
    return returnValue;
}
function checkWebGLError(gl) {
    const error = gl.getError();
    if (error !== gl.NO_ERROR) throw new Error("WebGL Error: " + getWebGLErrorMessage(gl, error));
}
// https://en.wikipedia.org/wiki/Half-precision_floating-point_format
const MIN_FLOAT16 = 5.96e-8;
const MAX_FLOAT16 = 65504;
function canBeRepresented(num) {
    if ((0, _tfjsCore.env)().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) return true;
    return false;
}
function getWebGLErrorMessage(gl, status) {
    switch(status){
        case gl.NO_ERROR:
            return "NO_ERROR";
        case gl.INVALID_ENUM:
            return "INVALID_ENUM";
        case gl.INVALID_VALUE:
            return "INVALID_VALUE";
        case gl.INVALID_OPERATION:
            return "INVALID_OPERATION";
        case gl.INVALID_FRAMEBUFFER_OPERATION:
            return "INVALID_FRAMEBUFFER_OPERATION";
        case gl.OUT_OF_MEMORY:
            return "OUT_OF_MEMORY";
        case gl.CONTEXT_LOST_WEBGL:
            return "CONTEXT_LOST_WEBGL";
        default:
            return `Unknown error code ${status}`;
    }
}
function getExtensionOrThrow(gl, extensionName) {
    return throwIfNull(gl, ()=>gl.getExtension(extensionName), 'Extension "' + extensionName + '" not supported on this browser.');
}
function createVertexShader(gl, vertexShaderSource) {
    const vertexShader = throwIfNull(gl, ()=>gl.createShader(gl.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
    callAndCheck(gl, ()=>gl.shaderSource(vertexShader, vertexShaderSource));
    callAndCheck(gl, ()=>gl.compileShader(vertexShader));
    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
        console.log(gl.getShaderInfoLog(vertexShader));
        throw new Error("Failed to compile vertex shader.");
    }
    return vertexShader;
}
function createFragmentShader(gl, fragmentShaderSource) {
    const fragmentShader = throwIfNull(gl, ()=>gl.createShader(gl.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
    callAndCheck(gl, ()=>gl.shaderSource(fragmentShader, fragmentShaderSource));
    callAndCheck(gl, ()=>gl.compileShader(fragmentShader));
    if ((0, _tfjsCore.env)().get("ENGINE_COMPILE_ONLY")) return fragmentShader;
    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
        throw new Error("Failed to compile fragment shader.");
    }
    return fragmentShader;
}
const lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
    const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
    if (lineNumberRegexResult == null) {
        console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);
        console.log(shaderSource);
        return;
    }
    const lineNumber = +lineNumberRegexResult[1];
    const shaderLines = shaderSource.split("\n");
    const pad = shaderLines.length.toString().length + 2;
    const linesWithLineNumbers = shaderLines.map((line, lineNumber)=>(0, _tfjsCore.util).rightPad((lineNumber + 1).toString(), pad) + line);
    let maxLineLength = 0;
    for(let i = 0; i < linesWithLineNumbers.length; i++)maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
    const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
    const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
    const afterErrorLines = linesWithLineNumbers.slice(lineNumber);
    console.log(beforeErrorLines.join("\n"));
    console.log(shaderInfoLog.split("\n")[0]);
    console.log(`%c ${(0, _tfjsCore.util).rightPad(errorLine[0], maxLineLength)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717");
    console.log(afterErrorLines.join("\n"));
}
function createProgram(gl) {
    return throwIfNull(gl, ()=>gl.createProgram(), "Unable to create WebGLProgram.");
}
function linkProgram(gl, program) {
    callAndCheck(gl, ()=>gl.linkProgram(program));
    if ((0, _tfjsCore.env)().get("ENGINE_COMPILE_ONLY")) return;
    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
        console.log(gl.getProgramInfoLog(program));
        throw new Error("Failed to link vertex and fragment shaders.");
    }
}
function validateProgram(gl, program) {
    callAndCheck(gl, ()=>gl.validateProgram(program));
    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
        console.log(gl.getProgramInfoLog(program));
        throw new Error("Shader program validation failed.");
    }
}
function createStaticVertexBuffer(gl, data) {
    const buffer = throwIfNull(gl, ()=>gl.createBuffer(), "Unable to create WebGLBuffer");
    callAndCheck(gl, ()=>gl.bindBuffer(gl.ARRAY_BUFFER, buffer));
    callAndCheck(gl, ()=>gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));
    return buffer;
}
function createStaticIndexBuffer(gl, data) {
    const buffer = throwIfNull(gl, ()=>gl.createBuffer(), "Unable to create WebGLBuffer");
    callAndCheck(gl, ()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));
    callAndCheck(gl, ()=>gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));
    return buffer;
}
function getNumChannels() {
    if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) return 1;
    return 4;
}
function createTexture(gl) {
    return throwIfNull(gl, ()=>gl.createTexture(), "Unable to create WebGLTexture.");
}
function validateTextureSize(width, height) {
    const maxTextureSize = (0, _tfjsCore.env)().getNumber("WEBGL_MAX_TEXTURE_SIZE");
    if (width <= 0 || height <= 0) {
        const requested = `[${width}x${height}]`;
        throw new Error("Requested texture size " + requested + " is invalid.");
    }
    if (width > maxTextureSize || height > maxTextureSize) {
        const requested = `[${width}x${height}]`;
        const max = `[${maxTextureSize}x${maxTextureSize}]`;
        throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max + ".");
    }
}
function createFramebuffer(gl) {
    return throwIfNull(gl, ()=>gl.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
    const loc = gl.getAttribLocation(program, attribute);
    if (loc === -1) // The GPU compiler decided to strip out this attribute because it's unused,
    // thus no need to bind.
    return false;
    callAndCheck(gl, ()=>gl.bindBuffer(gl.ARRAY_BUFFER, buffer));
    callAndCheck(gl, ()=>gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));
    callAndCheck(gl, ()=>gl.enableVertexAttribArray(loc));
    return true;
}
function bindTextureUnit(gl, texture, textureUnit) {
    validateTextureUnit(gl, textureUnit);
    callAndCheck(gl, ()=>gl.activeTexture(gl.TEXTURE0 + textureUnit));
    callAndCheck(gl, ()=>gl.bindTexture(gl.TEXTURE_2D, texture));
}
function unbindTextureUnit(gl, textureUnit) {
    validateTextureUnit(gl, textureUnit);
    callAndCheck(gl, ()=>gl.activeTexture(gl.TEXTURE0 + textureUnit));
    callAndCheck(gl, ()=>gl.bindTexture(gl.TEXTURE_2D, null));
}
function getProgramUniformLocationOrThrow(gl, program, uniformName) {
    return throwIfNull(gl, ()=>gl.getUniformLocation(program, uniformName), 'uniform "' + uniformName + '" not present in program.');
}
function getProgramUniformLocation(gl, program, uniformName) {
    return gl.getUniformLocation(program, uniformName);
}
function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {
    callAndCheck(gl, ()=>bindTextureUnit(gl, texture, textureUnit));
    callAndCheck(gl, ()=>gl.uniform1i(uniformSamplerLocation, textureUnit));
}
function bindCanvasToFramebuffer(gl) {
    callAndCheck(gl, ()=>gl.bindFramebuffer(gl.FRAMEBUFFER, null));
    callAndCheck(gl, ()=>gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));
    callAndCheck(gl, ()=>gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));
}
function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
    callAndCheck(gl, ()=>gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
    callAndCheck(gl, ()=>gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));
}
function unbindColorTextureFromFramebuffer(gl, framebuffer) {
    callAndCheck(gl, ()=>gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
    callAndCheck(gl, ()=>gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));
}
function validateFramebuffer(gl) {
    const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
    if (status !== gl.FRAMEBUFFER_COMPLETE) throw new Error("Error binding framebuffer: " + getFramebufferErrorMessage(gl, status));
}
function getFramebufferErrorMessage(gl, status) {
    switch(status){
        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
            return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
            return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
            return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
        case gl.FRAMEBUFFER_UNSUPPORTED:
            return "FRAMEBUFFER_UNSUPPORTED";
        default:
            return `unknown error ${status}`;
    }
}
function throwIfNull(gl, returnTOrNull, failureMessage) {
    const tOrNull = callAndCheck(gl, ()=>returnTOrNull());
    if (tOrNull == null) throw new Error(failureMessage);
    return tOrNull;
}
function validateTextureUnit(gl, textureUnit) {
    const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
    const glTextureUnit = textureUnit + gl.TEXTURE0;
    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
        const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;
        throw new Error(`textureUnit must be in ${textureUnitRange}.`);
    }
}
function getBatchDim(shape, dimsToSkip = 2) {
    return (0, _tfjsCore.util).sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
}
function getRowsCols(shape) {
    if (shape.length === 0) throw Error("Cannot get rows and columns of an empty shape array.");
    return [
        shape.length > 1 ? shape[shape.length - 2] : 1,
        shape[shape.length - 1]
    ];
}
function getShapeAs3D(shape) {
    let shapeAs3D = [
        1,
        1,
        1
    ];
    const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;
    if (!isScalar) shapeAs3D = [
        getBatchDim(shape),
        ...getRowsCols(shape)
    ];
    return shapeAs3D;
}
function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
    let maxTexSize = (0, _tfjsCore.env)().getNumber("WEBGL_MAX_TEXTURE_SIZE");
    let maxSizeForNarrowTex = (0, _tfjsCore.env)().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");
    if (maxSizeForNarrowTex === Infinity && (0, _tfjsCore.env)().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")) maxSizeForNarrowTex = maxTexSize / 2;
    if (isPacked) {
        maxTexSize = maxTexSize * 2;
        maxSizeForNarrowTex = maxSizeForNarrowTex * 2;
        // This logic ensures we accurately count the number of packed texels needed
        // to accommodate the tensor. We can only pack values in the same texel if
        // they are from adjacent pairs of rows/cols within the same batch. So if a
        // tensor has 3 rows, we pretend it has 4 rows in order to account for the
        // fact that the texels containing the third row are half empty.
        logShape = logShape.map((d, i)=>i >= logShape.length - 2 ? (0, _tfjsCore.util).nearestLargerEven(logShape[i]) : logShape[i]);
        // Packed texture height is at least 2 (the channel height of a single
        // texel).
        if (logShape.length === 1) logShape = [
            2,
            logShape[0]
        ];
    }
    // If logical shape is 2, we don't squeeze, since we want to match physical.
    if (logShape.length !== 2) {
        const squeezeResult = (0, _tfjsCore.util).squeezeShape(logShape);
        logShape = squeezeResult.newShape;
    }
    let size = (0, _tfjsCore.util).sizeFromShape(logShape);
    let textureShape = null;
    if (logShape.length <= 1 && size <= maxTexSize) textureShape = [
        1,
        size
    ];
    else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) textureShape = logShape;
    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) textureShape = [
        logShape[0] * logShape[1],
        logShape[2]
    ];
    else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) textureShape = [
        logShape[0],
        logShape[1] * logShape[2]
    ];
    else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) textureShape = [
        logShape[0] * logShape[1] * logShape[2],
        logShape[3]
    ];
    else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) textureShape = [
        logShape[0],
        logShape[1] * logShape[2] * logShape[3]
    ];
    // true if one edge length is 1 (1 or 2, if packed), while another edge
    // length exceeds maxSizeForNarrowTex.
    const isLongNarrowTex = textureShape != null && Math.max(...textureShape) > maxSizeForNarrowTex && Math.min(...textureShape) <= (isPacked ? 2 : 1) && Math.min(...textureShape) > 0;
    if (textureShape == null || isLongNarrowTex) {
        if (isPacked) {
            // For packed textures size equals the number of channels required to
            // accommodate the texture data. However in order to squarify such that
            // inner dimensions stay even, we rewrite size to equal the number of
            // texels. Then in the return statement we rehydrate the squarified
            // dimensions to channel units.
            const batchDim = getBatchDim(logShape);
            let rows = 2, cols = 2;
            if (logShape.length) [rows, cols] = getRowsCols(logShape);
            size = batchDim * (rows / 2) * (cols / 2);
            textureShape = (0, _tfjsCore.util).sizeToSquarishShape(size).map((d)=>d * 2);
        } else textureShape = (0, _tfjsCore.util).sizeToSquarishShape(size);
    }
    return textureShape;
}
function isEven(n) {
    return n % 2 === 0;
}
function isReshapeFree(shape1, shape2) {
    shape1 = shape1.slice(-2);
    shape2 = shape2.slice(-2);
    if ((0, _tfjsCore.util).arraysEqual(shape1, shape2)) return true;
    if (!shape1.length || !shape2.length) return true;
    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) return true;
    if (shape1.length !== shape2.length) {
        const shape1Cols = shape1[shape1.length - 1];
        const shape2Cols = shape2[shape2.length - 1];
        if (shape1Cols === shape2Cols) return true;
        if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) return true;
    }
    return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
}
// We cache webgl params because the environment gets reset between
// unit tests and we don't want to constantly query the WebGLContext for
// MAX_TEXTURE_SIZE.
let MAX_TEXTURE_SIZE;
let MAX_TEXTURES_IN_SHADER;
function getWebGLMaxTextureSize(webGLVersion) {
    if (MAX_TEXTURE_SIZE == null) {
        const gl = (0, _canvasUtil.getWebGLContext)(webGLVersion);
        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
    }
    return MAX_TEXTURE_SIZE;
}
function resetMaxTextureSize() {
    MAX_TEXTURE_SIZE = null;
}
function resetMaxTexturesInShader() {
    MAX_TEXTURES_IN_SHADER = null;
}
function getMaxTexturesInShader(webGLVersion) {
    if (MAX_TEXTURES_IN_SHADER == null) {
        const gl = (0, _canvasUtil.getWebGLContext)(webGLVersion);
        MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
    }
    // We cap at 16 to avoid spurious runtime "memory exhausted" error.
    return Math.min(16, MAX_TEXTURES_IN_SHADER);
}
function getWebGLDisjointQueryTimerVersion(webGLVersion) {
    if (webGLVersion === 0) return 0;
    let queryTimerVersion;
    const gl = (0, _canvasUtil.getWebGLContext)(webGLVersion);
    if (hasExtension(gl, "EXT_disjoint_timer_query_webgl2") && webGLVersion === 2) queryTimerVersion = 2;
    else if (hasExtension(gl, "EXT_disjoint_timer_query")) queryTimerVersion = 1;
    else queryTimerVersion = 0;
    return queryTimerVersion;
}
function hasExtension(gl, extensionName) {
    const ext = gl.getExtension(extensionName);
    return ext != null;
}
function isWebGLVersionEnabled(webGLVersion) {
    try {
        const gl = (0, _canvasUtil.getWebGLContext)(webGLVersion);
        if (gl != null) return true;
    } catch (e) {
        console.log("Error when getting WebGL context: ", e);
        return false;
    }
    return false;
}
function isCapableOfRenderingToFloatTexture(webGLVersion) {
    if (webGLVersion === 0) return false;
    const gl = (0, _canvasUtil.getWebGLContext)(webGLVersion);
    if (webGLVersion === 1) {
        if (!hasExtension(gl, "OES_texture_float")) return false;
    } else {
        if (!hasExtension(gl, "EXT_color_buffer_float")) return false;
    }
    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
    return isFrameBufferComplete;
}
function isDownloadFloatTextureEnabled(webGLVersion) {
    if (webGLVersion === 0) return false;
    const gl = (0, _canvasUtil.getWebGLContext)(webGLVersion);
    if (webGLVersion === 1) {
        if (!hasExtension(gl, "OES_texture_float")) return false;
        if (!hasExtension(gl, "WEBGL_color_buffer_float")) return false;
    } else {
        if (hasExtension(gl, "EXT_color_buffer_float")) return createFloatTextureAndBindToFramebuffer(gl);
        const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
        if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
            const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
            return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);
        }
        return false;
    }
    const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
    return isFrameBufferComplete;
}
function createFloatTextureAndBindToFramebuffer(gl) {
    const texConfig = (0, _texUtil.getTextureConfig)(gl);
    const texture = gl.createTexture();
    gl.bindTexture(gl.TEXTURE_2D, texture);
    const width = 1;
    const height = 1;
    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
    const frameBuffer = gl.createFramebuffer();
    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
    gl.bindTexture(gl.TEXTURE_2D, null);
    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
    gl.deleteTexture(texture);
    gl.deleteFramebuffer(frameBuffer);
    return isFrameBufferComplete;
}
function createHalfFloatTextureAndBindToFramebuffer(// tslint:disable-next-line:no-any
gl, textureHalfFloatExtension) {
    const texConfig = (0, _texUtil.getTextureConfig)(gl, textureHalfFloatExtension);
    const texture = gl.createTexture();
    gl.bindTexture(gl.TEXTURE_2D, texture);
    const width = 1;
    const height = 1;
    gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
    const frameBuffer = gl.createFramebuffer();
    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
    const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
    gl.bindTexture(gl.TEXTURE_2D, null);
    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
    gl.deleteTexture(texture);
    gl.deleteFramebuffer(frameBuffer);
    return isFrameBufferComplete;
}
function isWebGLFenceEnabled(webGLVersion) {
    if (webGLVersion !== 2) return false;
    const gl = (0, _canvasUtil.getWebGLContext)(webGLVersion);
    // tslint:disable-next-line:no-any
    const isEnabled = gl.fenceSync != null;
    return isEnabled;
}
function assertNotComplex(tensor, opName) {
    if (!Array.isArray(tensor)) tensor = [
        tensor
    ];
    tensor.forEach((t)=>{
        if (t != null) (0, _tfjsCore.util).assert(t.dtype !== "complex64", ()=>`${opName} does not support complex64 tensors ` + "in the WebGL backend.");
    });
}

},{"@tensorflow/tfjs-core":"21Ftl","./canvas_util":"1tNGf","./tex_util":"1oYRe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1tNGf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "clearWebGLContext", ()=>clearWebGLContext);
parcelHelpers.export(exports, "setWebGLContext", ()=>setWebGLContext);
parcelHelpers.export(exports, "getWebGLContext", ()=>getWebGLContext);
var _tfjsCore = require("@tensorflow/tfjs-core");
const contexts = {};
const WEBGL_ATTRIBUTES = {
    alpha: false,
    antialias: false,
    premultipliedAlpha: false,
    preserveDrawingBuffer: false,
    depth: false,
    stencil: false,
    failIfMajorPerformanceCaveat: true
};
function clearWebGLContext(webGLVersion) {
    delete contexts[webGLVersion];
}
function setWebGLContext(webGLVersion, gl) {
    contexts[webGLVersion] = gl;
}
function getWebGLContext(webGLVersion, customCanvas) {
    if (!(webGLVersion in contexts) || customCanvas != null) {
        const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);
        if (newCtx !== null) contexts[webGLVersion] = newCtx;
        else {
            console.log("Could not get context for WebGL version", webGLVersion);
            return null;
        }
    }
    const gl = contexts[webGLVersion];
    if (gl == null || gl.isContextLost()) {
        delete contexts[webGLVersion];
        return getWebGLContext(webGLVersion);
    }
    gl.disable(gl.DEPTH_TEST);
    gl.disable(gl.STENCIL_TEST);
    gl.disable(gl.BLEND);
    gl.disable(gl.DITHER);
    gl.disable(gl.POLYGON_OFFSET_FILL);
    gl.disable(gl.SAMPLE_COVERAGE);
    gl.enable(gl.SCISSOR_TEST);
    gl.enable(gl.CULL_FACE);
    gl.cullFace(gl.BACK);
    return contexts[webGLVersion];
}
function createCanvas(webGLVersion) {
    // Use canvas element for Safari, since its offscreen canvas does not support
    // fencing.
    if (!(0, _tfjsCore.env)().getBool("IS_SAFARI") && typeof OffscreenCanvas !== "undefined" && webGLVersion === 2) return new OffscreenCanvas(300, 150);
    else if (typeof document !== "undefined") return document.createElement("canvas");
    else throw new Error("Cannot create a canvas in this context");
}
function getWebGLRenderingContext(webGLVersion, customCanvas) {
    if (webGLVersion !== 1 && webGLVersion !== 2) throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
    const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;
    canvas.addEventListener("webglcontextlost", (ev)=>{
        ev.preventDefault();
        delete contexts[webGLVersion];
    }, false);
    if ((0, _tfjsCore.env)().getBool("SOFTWARE_WEBGL_ENABLED")) WEBGL_ATTRIBUTES.failIfMajorPerformanceCaveat = false;
    if (webGLVersion === 1) return(// tslint:disable-next-line
    canvas.getContext("webgl", WEBGL_ATTRIBUTES) || canvas.getContext("experimental-webgl", WEBGL_ATTRIBUTES));
    return canvas.getContext("webgl2", WEBGL_ATTRIBUTES);
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1oYRe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "PackingScheme", ()=>PackingScheme);
parcelHelpers.export(exports, "TextureUsage", ()=>TextureUsage);
parcelHelpers.export(exports, "PhysicalTextureType", ()=>PhysicalTextureType);
parcelHelpers.export(exports, "getUnpackedMatrixTextureShapeWidthHeight", ()=>getUnpackedMatrixTextureShapeWidthHeight);
parcelHelpers.export(exports, "getUnpackedArraySizeFromMatrixSize", ()=>getUnpackedArraySizeFromMatrixSize);
parcelHelpers.export(exports, "getColorMatrixTextureShapeWidthHeight", ()=>getColorMatrixTextureShapeWidthHeight);
/**
 * Get shape for densely packed RGBA texture.
 */ parcelHelpers.export(exports, "getDenseTexShape", ()=>getDenseTexShape);
parcelHelpers.export(exports, "getMatrixSizeFromUnpackedArraySize", ()=>getMatrixSizeFromUnpackedArraySize);
parcelHelpers.export(exports, "decodeMatrixFromUnpackedColorRGBAArray", ()=>decodeMatrixFromUnpackedColorRGBAArray);
parcelHelpers.export(exports, "getPackedMatrixTextureShapeWidthHeight", ()=>getPackedMatrixTextureShapeWidthHeight);
parcelHelpers.export(exports, "getPackedRGBAArraySizeFromMatrixShape", ()=>getPackedRGBAArraySizeFromMatrixShape);
parcelHelpers.export(exports, "getTextureConfig", ()=>getTextureConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var PackingScheme;
(function(PackingScheme) {
    /**
     * All values in a single texel are densely packed without any constraints.
     *
     * This is how the shader encodes a tensor with shape = [2, 3, 4]
     * (indices are [batch, row, col]).
     *
     * 000|001   010|011   020|021
     * -------   -------   -------
     * 002|003   012|013   022|023
     *
     * 100|101   110|111   120|121
     * -------   -------   -------
     * 102|103   112|113   122|123
     *
     */ PackingScheme[PackingScheme["DENSE"] = 0] = "DENSE";
    /**
     * Single texels contain only values from the same batch, and from adjacent
     * rows and columns.
     *
     * This is how the shader encodes a tensor with shape = [2, 3, 5]
     * (indices are [batch, row, col]).
     *
     * 000|001   002|003   004|xxx   020|021   022|023   024|xxx
     * -------   -------   -------   -------   -------   -------
     * 010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx
     *
     * 100|101   102|103   104|xxx   120|121   122|123   124|xxx
     * -------   -------   -------   -------   -------   -------
     * 110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx
     *
     */ PackingScheme[PackingScheme["SHARED_BATCH"] = 1] = "SHARED_BATCH";
})(PackingScheme || (PackingScheme = {}));
var TextureUsage;
(function(TextureUsage) {
    TextureUsage[TextureUsage["RENDER"] = 0] = "RENDER";
    TextureUsage[TextureUsage["UPLOAD"] = 1] = "UPLOAD";
    TextureUsage[TextureUsage["PIXELS"] = 2] = "PIXELS";
    TextureUsage[TextureUsage["DOWNLOAD"] = 3] = "DOWNLOAD";
})(TextureUsage || (TextureUsage = {}));
var PhysicalTextureType;
(function(PhysicalTextureType) {
    PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
    PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
    PhysicalTextureType[PhysicalTextureType["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
    PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
    PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
})(PhysicalTextureType || (PhysicalTextureType = {}));
function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
    return [
        columns,
        rows
    ];
}
function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
    return matrixSize * channelsPerTexture;
}
function getColorMatrixTextureShapeWidthHeight(rows, columns) {
    return [
        columns * 4,
        rows
    ];
}
function getDenseTexShape(shape) {
    const size = (0, _tfjsCore.util).sizeFromShape(shape);
    const texelsNeeded = Math.ceil(size / 4);
    return (0, _tfjsCore.util).sizeToSquarishShape(texelsNeeded);
}
function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {
    if (unpackedSize % channelsPerTexture !== 0) throw new Error(`unpackedSize (${unpackedSize}) must be a multiple of ` + `${channelsPerTexture}`);
    return unpackedSize / channelsPerTexture;
}
function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {
    const requiredSize = unpackedArray.length * channels / 4;
    if (matrix.length < requiredSize) throw new Error(`matrix length (${matrix.length}) must be >= ${requiredSize}`);
    let dst = 0;
    for(let src = 0; src < unpackedArray.length; src += 4)for(let c = 0; c < channels; c++)matrix[dst++] = unpackedArray[src + c];
}
function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
    return [
        Math.max(1, Math.ceil(columns / 2)),
        Math.max(1, Math.ceil(rows / 2))
    ];
}
function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
    const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
    return w * h * 4;
}
function getTextureConfig(// tslint:disable-next-line:no-any
gl, textureHalfFloatExtension) {
    // tslint:disable-next-line:no-any
    const glany = gl;
    let internalFormatFloat;
    let internalFormatHalfFloat;
    let internalFormatPackedHalfFloat;
    let internalFormatPackedFloat;
    let textureFormatFloat;
    let downloadTextureFormat;
    let downloadUnpackNumChannels;
    let defaultNumChannels;
    let textureTypeHalfFloat;
    let textureTypeFloat;
    if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) {
        internalFormatFloat = glany.R32F;
        internalFormatHalfFloat = glany.R16F;
        internalFormatPackedHalfFloat = glany.RGBA16F;
        internalFormatPackedFloat = glany.RGBA32F;
        textureFormatFloat = glany.RED;
        downloadUnpackNumChannels = 4;
        defaultNumChannels = 1;
        textureTypeHalfFloat = glany.HALF_FLOAT;
        textureTypeFloat = glany.FLOAT;
        downloadTextureFormat = glany.RGBA8;
    } else {
        internalFormatFloat = gl.RGBA;
        internalFormatHalfFloat = gl.RGBA;
        internalFormatPackedHalfFloat = gl.RGBA;
        internalFormatPackedFloat = glany.RGBA;
        textureFormatFloat = gl.RGBA;
        downloadUnpackNumChannels = 4;
        defaultNumChannels = 4;
        textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;
        textureTypeFloat = gl.FLOAT;
        downloadTextureFormat = gl.RGBA;
    }
    return {
        internalFormatFloat,
        internalFormatHalfFloat,
        internalFormatPackedHalfFloat,
        internalFormatPackedFloat,
        textureFormatFloat,
        downloadTextureFormat,
        downloadUnpackNumChannels,
        defaultNumChannels,
        textureTypeHalfFloat,
        textureTypeFloat
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b07CY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DecodeMatrixProgram", ()=>DecodeMatrixProgram);
var _glslVersion = require("./glsl_version");
var _gpgpuMath = require("./gpgpu_math");
var _shaderCompilerUtil = require("./shader_compiler_util");
var _texUtil = require("./tex_util");
class DecodeMatrixProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = false;
        this.packedOutput = true;
        this.outPackingScheme = (0, _texUtil.PackingScheme).DENSE;
        this.customUniforms = [
            {
                name: "texShape",
                type: "ivec2"
            }
        ];
        const glsl = (0, _glslVersion.getGlslDifferences)();
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? _shaderCompilerUtil.getOutputLogicalCoordinatesFromFlatIndexByUniform([
            "r",
            "c",
            "d"
        ], outputShape) : _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndex([
            "r",
            "c",
            "d"
        ], outputShape)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getA(rc.x, rc.y, rc.z);
        }

        ${glsl.output} = result;
      }
    `;
    }
}

},{"./glsl_version":"kG2vE","./gpgpu_math":"btJo5","./shader_compiler_util":"4ROqF","./tex_util":"1oYRe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kG2vE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getGlslDifferences", ()=>getGlslDifferences);
var _tfjsCore = require("@tensorflow/tfjs-core");
function getGlslDifferences() {
    let version;
    let attribute;
    let varyingVs;
    let varyingFs;
    let texture2D;
    let output;
    let defineOutput;
    let defineSpecialNaN;
    let defineSpecialInf;
    let defineRound;
    if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) {
        version = "#version 300 es";
        attribute = "in";
        varyingVs = "out";
        varyingFs = "in";
        texture2D = "texture";
        output = "outputColor";
        defineOutput = "out vec4 outputColor;";
        // Use custom isnan definition to work across differences between
        // implementations on various platforms. While this should happen in ANGLE
        // we still see differences between android and windows (on chrome) when
        // using isnan directly. Since WebGL2 supports uint type and
        // floatBitsToUinT built-in function, we could implment isnan following
        // IEEE 754 rules.
        // NaN defination in IEEE 754-1985 is :
        //   - sign = either 0 or 1.
        //   - biased exponent = all 1 bits.
        //   - fraction = anything except all 0 bits (since all 0 bits represents
        //   infinity).
        // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers
        defineSpecialNaN = (0, _tfjsCore.env)().getBool("WEBGL2_ISNAN_CUSTOM") ? `
      bool isnan_custom(float val) {
        uint floatToUint = floatBitsToUint(val);
        return (floatToUint & 0x7fffffffu) > 0x7f800000u;
      }

      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan_custom(val.x),
          isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
      }

      #define isnan(value) isnan_custom(value)
    ` : "";
        // In webgl 2 we do not need to specify a custom isinf so there is no
        // need for a special INFINITY constant.
        defineSpecialInf = ``;
        defineRound = `
      #define round(value) newRound(value)
      int newRound(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 newRound(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `;
    } else {
        version = "";
        attribute = "attribute";
        varyingVs = "varying";
        varyingFs = "varying";
        texture2D = "texture2D";
        output = "gl_FragColor";
        defineOutput = "";
        // WebGL1 has no built in isnan so we define one here.
        defineSpecialNaN = `
      #define isnan(value) isnan_custom(value)
      bool isnan_custom(float val) {
        return (val > 0. || val < 1. || val == 0.) ? false : true;
      }
      bvec4 isnan_custom(vec4 val) {
        return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
      }
    `;
        defineSpecialInf = `
      uniform float INFINITY;

      bool isinf(float val) {
        return abs(val) == INFINITY;
      }
      bvec4 isinf(vec4 val) {
        return equal(abs(val), vec4(INFINITY));
      }
    `;
        defineRound = `
      int round(float value) {
        return int(floor(value + 0.5));
      }

      ivec4 round(vec4 value) {
        return ivec4(floor(value + vec4(0.5)));
      }
    `;
    }
    return {
        version,
        attribute,
        varyingVs,
        varyingFs,
        texture2D,
        output,
        defineOutput,
        defineSpecialNaN,
        defineSpecialInf,
        defineRound
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"btJo5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "compileProgram", ()=>compileProgram);
parcelHelpers.export(exports, "getUniformLocations", ()=>getUniformLocations);
parcelHelpers.export(exports, "runProgram", ()=>runProgram);
parcelHelpers.export(exports, "makeShaderKey", ()=>makeShaderKey);
parcelHelpers.export(exports, "useShapeUniforms", ()=>useShapeUniforms);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shaderCompiler = require("./shader_compiler");
var _webglUtil = require("./webgl_util");
function compileProgram(gpgpu, program, inputs, output) {
    const inputInfos = inputs.map((input, i)=>{
        const shapeInfo = {
            logicalShape: input.shape,
            texShape: input.isUniform ? null : input.texData.texShape,
            isUniform: input.isUniform,
            isPacked: input.isUniform ? false : input.texData.isPacked,
            flatOffset: null
        };
        if (input.texData != null && input.texData.slice != null && input.texData.slice.flatOffset > 0) shapeInfo.flatOffset = input.texData.slice.flatOffset;
        return {
            name: program.variableNames[i],
            shapeInfo
        };
    });
    const inShapeInfos = inputInfos.map((x)=>x.shapeInfo);
    const outShapeInfo = {
        logicalShape: output.shape,
        texShape: output.texData.texShape,
        isUniform: false,
        isPacked: output.texData.isPacked,
        flatOffset: null
    };
    const source = _shaderCompiler.makeShader(inputInfos, outShapeInfo, program);
    const fragmentShader = (0, _webglUtil.createFragmentShader)(gpgpu.gl, source);
    const webGLProgram = gpgpu.createProgram(fragmentShader);
    if (!(0, _tfjsCore.env)().get("ENGINE_COMPILE_ONLY")) {
        gpgpu.buildVao(webGLProgram);
        return Object.assign({
            program,
            fragmentShader,
            source,
            webGLProgram,
            inShapeInfos,
            outShapeInfo
        }, getUniformLocations(gpgpu, program, webGLProgram));
    } else return {
        program,
        fragmentShader,
        source,
        webGLProgram,
        inShapeInfos,
        outShapeInfo,
        variablesLocations: null,
        customUniformLocations: null,
        infLoc: null,
        nanLoc: null,
        outShapeLocation: null,
        outShapeStridesLocation: null,
        outTexShapeLocation: null
    };
}
function getUniformLocations(gpgpu, program, webGLProgram) {
    const variablesLocations = [];
    const customUniformLocations = [];
    let outShapeLocation;
    let outTexShapeLocation;
    let outShapeStridesLocation;
    let infLoc = null;
    let nanLoc = null;
    // Add special uniforms (NAN, INFINITY)
    nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false);
    if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 1) infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false);
    // Add user-defined uniforms
    const shouldThrow = false;
    for (const varName of program.variableNames){
        const varLocs = {
            name: varName,
            uniform: gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow),
            offset: gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow)
        };
        if (program.enableShapeUniforms) {
            varLocs.shape = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);
            varLocs.texShape = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);
        }
        variablesLocations.push(varLocs);
    }
    if (program.enableShapeUniforms) {
        outShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outShape", shouldThrow);
        outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, "outShapeStrides", shouldThrow);
        outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow);
    }
    if (program.customUniforms) for (const d of program.customUniforms)customUniformLocations.push(gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow));
    return {
        variablesLocations,
        customUniformLocations,
        infLoc,
        nanLoc,
        outShapeLocation,
        outShapeStridesLocation,
        outTexShapeLocation
    };
}
function validateBinaryAndProgram(shapeInfos, inputs) {
    if (shapeInfos.length !== inputs.length) throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but ` + `was executed with ${inputs.length} inputs`);
    shapeInfos.forEach((s, i)=>{
        const shapeA = s.logicalShape;
        const input = inputs[i];
        const shapeB = input.shape;
        if (!(0, _tfjsCore.util).arraysEqual(shapeA, shapeB)) throw Error(`Binary was compiled with different shapes than ` + `the current args. Shapes ${shapeA} and ${shapeB} must match`);
        // The input is uploaded as uniform.
        if (s.isUniform && input.isUniform) return;
        const texShapeA = s.texShape;
        const texShapeB = input.isUniform ? null : input.texData.texShape;
        if (!(0, _tfjsCore.util).arraysEqual(texShapeA, texShapeB)) throw Error(`Binary was compiled with different texture shapes than the` + ` current args. Shape ${texShapeA} and ${texShapeB} must match`);
    });
}
function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
    if (!binary.program.enableShapeUniforms) {
        validateBinaryAndProgram(binary.inShapeInfos, inputs);
        validateBinaryAndProgram([
            binary.outShapeInfo
        ], [
            output
        ]);
    }
    const outTex = output.texData.texture;
    const outTexShape = output.texData.texShape;
    if (output.texData.isPacked) gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
    else gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
    gpgpu.setProgram(binary.webGLProgram);
    gpgpu.bindVertexArray(binary.webGLProgram.vao);
    // Set special uniforms (NAN, INFINITY)
    if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 1) {
        if (binary.infLoc !== null) gpgpu.gl.uniform1f(binary.infLoc, Infinity);
    }
    if (binary.nanLoc !== null) gpgpu.gl.uniform1f(binary.nanLoc, NaN);
    // Set user-defined inputs
    for(let i = 0; i < inputs.length; ++i){
        const input = inputs[i];
        const { uniform: varLoc, offset: varOffsetLoc, shape: varShapeLoc, texShape: varTexShapeLoc } = binary.variablesLocations[i];
        if (varShapeLoc) {
            const { uniformShape } = _shaderCompiler.getUniformInfoFromShape(binary.program.packedInputs, input.shape, input.texData.texShape);
            switch(uniformShape.length){
                case 1:
                    gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));
                    break;
                case 2:
                    gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));
                    break;
                case 3:
                    gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));
                    break;
                case 4:
                    gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));
                    break;
                default:
                    break;
            }
        }
        if (varTexShapeLoc) gpgpu.gl.uniform2i(varTexShapeLoc, input.texData.texShape[0], input.texData.texShape[1]);
        if (varLoc == null) continue;
        if (input.isUniform) {
            // Upload the values of the tensor as uniform.
            if ((0, _tfjsCore.util).sizeFromShape(input.shape) < 2) gpgpu.gl.uniform1f(varLoc, input.uniformValues[0]);
            else {
                let vals = input.uniformValues;
                if (!(vals instanceof Float32Array)) vals = new Float32Array(vals);
                gpgpu.gl.uniform1fv(varLoc, vals);
            }
            continue;
        }
        // If the input was sliced, upload the flat offset index.
        if (input.texData.slice != null && varOffsetLoc != null) gpgpu.gl.uniform1i(varOffsetLoc, input.texData.slice.flatOffset);
        gpgpu.setInputMatrixTexture(input.texData.texture.texture, varLoc, i);
    }
    const outShapeLoc = binary.outShapeLocation;
    if (outShapeLoc) switch(output.shape.length){
        case 1:
            gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));
            break;
        case 2:
            gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));
            break;
        case 3:
            gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));
            break;
        case 4:
            gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));
            break;
        default:
            break;
    }
    if (binary.outShapeStridesLocation) {
        const strides = (0, _tfjsCore.util).computeStrides(output.shape);
        switch(output.shape.length){
            case 2:
                gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides));
                break;
            case 3:
                gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides));
                break;
            case 4:
                gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides));
                break;
            default:
                break;
        }
    }
    if (binary.outTexShapeLocation) gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);
    if (binary.program.customUniforms && customUniformValues) for(let i = 0; i < binary.program.customUniforms.length; ++i){
        const d = binary.program.customUniforms[i];
        const customLoc = binary.customUniformLocations[i];
        const customValue = customUniformValues[i];
        if (d.type === "float") gpgpu.gl.uniform1fv(customLoc, customValue);
        else if (d.type === "vec2") gpgpu.gl.uniform2fv(customLoc, customValue);
        else if (d.type === "vec3") gpgpu.gl.uniform3fv(customLoc, customValue);
        else if (d.type === "vec4") gpgpu.gl.uniform4fv(customLoc, customValue);
        else if (d.type === "int") gpgpu.gl.uniform1iv(customLoc, customValue);
        else if (d.type === "ivec2") gpgpu.gl.uniform2iv(customLoc, customValue);
        else if (d.type === "ivec3") gpgpu.gl.uniform3iv(customLoc, customValue);
        else if (d.type === "ivec4") gpgpu.gl.uniform4iv(customLoc, customValue);
        else throw Error(`uniform type ${d.type} is not supported yet.`);
    }
    gpgpu.executeProgram();
}
function makeShaderKey(program, inputs, output) {
    let keyInputs = "";
    inputs.concat(output).forEach((x)=>{
        const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;
        // TODO: Remove the condition of !x.isUniform.
        if (program.enableShapeUniforms && !x.isUniform) {
            const xTexShape = x.texData.texShape;
            const { useSqueezeShape, uniformShape, keptDims } = _shaderCompiler.getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);
            let rank1 = "", rank2 = "", rank34 = "";
            if (uniformShape.length === 1 && program.packedInputs) {
                const packedTexShape = [
                    Math.ceil(xTexShape[0] / 2),
                    Math.ceil(xTexShape[1] / 2)
                ];
                rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;
            } else if (uniformShape.length === 2 && !program.packedInputs) rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;
            else if (uniformShape.length > 2 && !program.packedInputs) {
                const strides = (0, _tfjsCore.util).computeStrides(uniformShape);
                rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`;
            }
            const xRank = x.shape.length;
            const isLogicalShapTexShapeEqual = uniformShape.length === 2 && (0, _tfjsCore.util).arraysEqual(x.shape, xTexShape);
            const isScalar = (0, _tfjsCore.util).sizeFromShape(x.shape) === 1;
            const broadcastDims = (0, _tfjsCore.backend_util).getBroadcastDims(x.shape, output.shape);
            const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && (0, _tfjsCore.util).arraysEqual(xTexShape, output.texData.texShape);
            const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? "" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;
            // These key components are needed due to shader_compiler is embedding
            // them in the shader.
            // |xRank| is used to determine the coords length. See
            // get[Packed]SamplerAtOutputCoords.
            // |isInOutTexShapeEqual| is used to determine whether going to an
            // optimization path in getSamplerAtOutputCoords.
            // |useSqueezeShape| is extracted from squeezeInputInfo of
            // getSampler[2|3|4]D/getPackedSampler3D.
            // |isScalar| is extracted from isInputScalar/isOutputScalar in
            // getPackedSamplerAtOutputCoords.
            // |broadcastDims| is extracted from get[Packed]SamplerAtOutputCoords.
            // |isLogicalShapTexShapeEqual| is used in
            // getOutput[Packed]2DCoords/get[Packed]Sampler2D.
            // |rank1| is used in getOutputPacked1DCoords.
            // |rank2| is used in getOutput2DCoords.
            // |rank34| is used in getSampler3D/getSampler4D.
            // |isTexShapeGreaterThanOne| are used in
            // getSampler[Scalar|1D|2D]/getOutput1DCoords.
            keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : ""}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;
        } else {
            const texShape = x.isUniform ? "uniform" : x.texData.texShape;
            keyInputs += `${x.shape}_${texShape}_${hasOffset}`;
        }
    });
    const keyUserCode = program.userCode;
    let key = program.constructor.name;
    // Fast string concat. See https://jsperf.com/string-concatenation/14.
    key += "_" + keyInputs + "_" + keyUserCode + `${(0, _tfjsCore.env)().getNumber("WEBGL_VERSION")}`;
    return key;
}
function useShapeUniforms(rank) {
    // TODO: Remove the limitaion of rank <= 4.
    return (0, _tfjsCore.env)().getBool("WEBGL_USE_SHAPES_UNIFORMS") && rank <= 4;
}

},{"@tensorflow/tfjs-core":"21Ftl","./shader_compiler":"5uusa","./webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5uusa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Please make sure the shaker key in makeShaderKey in gpgpu_math.ts is well
// mapped if any shader source code is changed in this file.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "makeShader", ()=>makeShader);
parcelHelpers.export(exports, "getCoordsDataType", ()=>getCoordsDataType);
parcelHelpers.export(exports, "getUniformInfoFromShape", ()=>getUniformInfoFromShape);
/** Returns a new input info (a copy) that has a squeezed logical shape. */ parcelHelpers.export(exports, "squeezeInputInfo", ()=>squeezeInputInfo);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _glslVersion = require("./glsl_version");
var _shaderCompilerUtil = require("./shader_compiler_util");
const { getBroadcastDims } = (0, _tfjsCore.backend_util);
function makeShader(inputsInfo, outputShape, program) {
    const prefixSnippets = [];
    inputsInfo.forEach((x)=>{
        const size = (0, _tfjsCore.util).sizeFromShape(x.shapeInfo.logicalShape);
        // Snippet when we decided to upload the values as uniform.
        if (x.shapeInfo.isUniform) prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ""};`);
        else {
            prefixSnippets.push(`uniform sampler2D ${x.name};`);
            prefixSnippets.push(`uniform int offset${x.name};`);
        }
        if (program.enableShapeUniforms) {
            const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);
            switch(uniformShape.length){
                case 1:
                    prefixSnippets.push(`uniform int ${x.name}Shape;`);
                    break;
                case 2:
                    prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);
                    break;
                case 3:
                    prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);
                    break;
                case 4:
                    prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);
                    break;
                default:
                    break;
            }
            prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);
        }
    });
    if (program.enableShapeUniforms) {
        switch(outputShape.logicalShape.length){
            case 1:
                prefixSnippets.push(`uniform int outShape;`);
                break;
            case 2:
                prefixSnippets.push(`uniform ivec2 outShape;`);
                prefixSnippets.push(`uniform int outShapeStrides;`);
                break;
            case 3:
                prefixSnippets.push(`uniform ivec3 outShape;`);
                prefixSnippets.push(`uniform ivec2 outShapeStrides;`);
                break;
            case 4:
                prefixSnippets.push(`uniform ivec4 outShape;`);
                prefixSnippets.push(`uniform ivec3 outShapeStrides;`);
                break;
            default:
                break;
        }
        prefixSnippets.push(`uniform ivec2 outTexShape;`);
    }
    if (program.customUniforms) program.customUniforms.forEach((d)=>{
        prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`);
    });
    const inputPrefixSnippet = prefixSnippets.join("\n");
    const inputSamplingSnippet = inputsInfo.map((x)=>getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join("\n");
    const outTexShape = outputShape.texShape;
    const glsl = (0, _glslVersion.getGlslDifferences)();
    const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
    let outputSamplingSnippet;
    let floatTextureSetOutputSnippet;
    let shaderPrefix = getShaderPrefix(glsl);
    if (outputShape.isPacked) {
        outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
        floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
    } else {
        outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
        floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
    }
    if (program.packedInputs) shaderPrefix += SHADER_PACKED_PREFIX;
    const source = [
        shaderPrefix,
        floatTextureSampleSnippet,
        floatTextureSetOutputSnippet,
        inputPrefixSnippet,
        outputSamplingSnippet,
        inputSamplingSnippet,
        program.userCode
    ].join("\n");
    return source;
}
function getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {
    const shape = inInfo.shapeInfo.logicalShape;
    switch(shape.length){
        case 0:
            return getSamplerScalar(inInfo, enableShapeUniforms);
        case 1:
            return getSampler1D(inInfo, enableShapeUniforms);
        case 2:
            return getSampler2D(inInfo, enableShapeUniforms);
        case 3:
            return getSampler3D(inInfo, enableShapeUniforms);
        case 4:
            return getSampler4D(inInfo, enableShapeUniforms);
        case 5:
            return getSampler5D(inInfo);
        case 6:
            return getSampler6D(inInfo);
        default:
            throw new Error(`${shape.length}-D input sampling` + ` is not yet supported`);
    }
}
function getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {
    const shape = inInfo.shapeInfo.logicalShape;
    switch(shape.length){
        case 0:
            return getPackedSamplerScalar(inInfo);
        case 1:
            return getPackedSampler1D(inInfo, enableShapeUniforms);
        case 2:
            return getPackedSampler2D(inInfo, enableShapeUniforms);
        case 3:
            return getPackedSampler3D(inInfo, enableShapeUniforms);
        default:
            return getPackedSamplerND(inInfo, enableShapeUniforms);
    }
}
function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {
    let res = "";
    if (usesPackedTextures) res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);
    else res += getSamplerFromInInfo(inInfo, enableShapeUniforms);
    const inShape = inInfo.shapeInfo.logicalShape;
    const outShape = outShapeInfo.logicalShape;
    if (inShape.length <= outShape.length) {
        if (usesPackedTextures) res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
        else res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
    }
    return res;
}
function getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
    switch(outShape.length){
        case 0:
            return getOutputScalarCoords();
        case 1:
            return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);
        case 2:
            return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);
        case 3:
            return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);
        default:
            return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);
    }
}
function getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
    switch(outShape.length){
        case 0:
            return getOutputScalarCoords();
        case 1:
            return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);
        case 2:
            return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);
        case 3:
            return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);
        case 4:
            return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);
        case 5:
            return getOutput5DCoords(outShape, outTexShape);
        case 6:
            return getOutput6DCoords(outShape, outTexShape);
        default:
            throw new Error(`${outShape.length}-D output sampling is not yet supported`);
    }
}
function getFloatTextureSampleSnippet(glsl) {
    return `
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${glsl.texture2D}(textureSampler, uv).r;
    }
  `;
}
function getFloatTextureSetRSnippet(glsl) {
    return `
    void setOutput(float val) {
      ${glsl.output} = vec4(val, 0, 0, 0);
    }
  `;
}
function getFloatTextureSetRGBASnippet(glsl) {
    return `
    void setOutput(vec4 val) {
      ${glsl.output} = val;
    }
  `;
}
function getShaderPrefix(glsl) {
    const SHADER_PREFIX = `${glsl.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${glsl.varyingFs} vec2 resultUV;
    ${glsl.defineOutput}
    const vec2 halfCR = vec2(0.5, 0.5);

    struct ivec5
    {
      int x;
      int y;
      int z;
      int w;
      int u;
    };

    struct ivec6
    {
      int x;
      int y;
      int z;
      int w;
      int u;
      int v;
    };

    uniform float NAN;
    ${glsl.defineSpecialNaN}
    ${glsl.defineSpecialInf}
    ${glsl.defineRound}

    int imod(int x, int y) {
      return x - y * (x / y);
    }

    int idiv(int a, int b, float sign) {
      int res = a / b;
      int mod = imod(a, b);
      if (sign < 0. && mod != 0) {
        res -= 1;
      }
      return res;
    }

    //Based on the work of Dave Hoskins
    //https://www.shadertoy.com/view/4djSRW
    #define HASHSCALE1 443.8975
    float random(float seed){
      vec2 p = resultUV * seed;
      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);
      p3 += dot(p3, p3.yzx + 19.19);
      return fract((p3.x + p3.y) * p3.z);
    }

    ${SAMPLE_1D_SNIPPET}
    ${SAMPLE_2D_SNIPPET}
    ${SAMPLE_3D_SNIPPET}
  `;
    return SHADER_PREFIX;
}
const SAMPLE_1D_SNIPPET = `
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
  int texelIndex = index / 2;
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
const SAMPLE_2D_SNIPPET = `
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
  int texNumC, int row, int col) {
  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = texelIndex / texNumC;
  int texC = texelIndex - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
const SAMPLE_3D_SNIPPET = `
vec2 packedUVfrom3D(int texNumR, int texNumC,
    int texelsInBatch, int texelsInLogicalRow, int b,
    int row, int col) {
  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
  int texR = index / texNumC;
  int texC = index - texR * texNumC;
  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
const SHADER_PACKED_PREFIX = `
  float getChannel(vec4 frag, vec2 innerDims) {
    vec2 modCoord = mod(innerDims, 2.);
    return modCoord.x == 0. ?
      (modCoord.y == 0. ? frag.r : frag.g) :
      (modCoord.y == 0. ? frag.b : frag.a);
  }
  float getChannel(vec4 frag, int dim) {
    float modCoord = mod(float(dim), 2.);
    return modCoord == 0. ? frag.r : frag.g;
  }
`;
function getOutputScalarCoords() {
    return `
    int getOutputCoords() {
      return 0;
    }
  `;
}
function getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    if (packedTexShape[0] === 1) {
        if (enableShapeUniforms) return `
      int getOutputCoords() {
        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
      }
    `;
        return `
      int getOutputCoords() {
        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
      }
    `;
    }
    if (packedTexShape[1] === 1) {
        if (enableShapeUniforms) return `
      int getOutputCoords() {
        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
      }
    `;
        return `
      int getOutputCoords() {
        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
      }
    `;
    }
    if (enableShapeUniforms) return `
    int getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
    }
  `;
    return `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
    }
  `;
}
function getOutput1DCoords(shape, texShape, enableShapeUniforms) {
    if (texShape[0] === 1) {
        if (enableShapeUniforms) return `
      int getOutputCoords() {
        return int(resultUV.x * float(outTexShape[1]));
      }
    `;
        return `
      int getOutputCoords() {
        return int(resultUV.x * ${texShape[1]}.0);
      }
    `;
    }
    if (texShape[1] === 1) {
        if (enableShapeUniforms) return `
      int getOutputCoords() {
        return int(resultUV.y * float(outTexShape[0]));
      }
    `;
        return `
      int getOutputCoords() {
        return int(resultUV.y * ${texShape[0]}.0);
      }
    `;
    }
    if (enableShapeUniforms) return `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      return resTexRC.x * outTexShape[1] + resTexRC.y;
    }
  `;
    return `
    int getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      return resTexRC.x * ${texShape[1]} + resTexRC.y;
    }
  `;
}
function getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {
    if (enableShapeUniforms) return `
    ivec3 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec3(b, r, c);
    }
  `;
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    const texelsInLogicalRow = Math.ceil(shape[2] / 2);
    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
    return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;

      int b = index / ${texelsInBatch};
      index -= b * ${texelsInBatch};

      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec3(b, r, c);
    }
  `;
}
function getOutput3DCoords(shape, texShape, enableShapeUniforms) {
    if (enableShapeUniforms) {
        const coordsFromIndexSnippet = _shaderCompilerUtil.getOutputLogicalCoordinatesFromFlatIndexByUniform([
            "r",
            "c",
            "d"
        ], shape);
        return `
  ivec3 getOutputCoords() {
    ivec2 resTexRC = ivec2(resultUV.yx *
                           vec2(outTexShape[0], outTexShape[1]));
    int index = resTexRC.x * outTexShape[1] + resTexRC.y;
    ${coordsFromIndexSnippet}
    return ivec3(r, c, d);
  }
`;
    }
    const coordsFromIndexSnippet = _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndex([
        "r",
        "c",
        "d"
    ], shape);
    return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      ${coordsFromIndexSnippet}
      return ivec3(r, c, d);
    }
  `;
}
function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {
    if (enableShapeUniforms) // TODO: support 5d and 6d
    return `
    ivec4 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));
      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;

      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
      int texelsInBatchN = texelsInBatch * outShape[1];

      int b2 = index / texelsInBatchN;
      index -= b2 * texelsInBatchN;

      int b = index / texelsInBatch;
      index -= b * texelsInBatch;

      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec4(b2, b, r, c);
    }
  `;
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
    const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
    let texelsInBatchN = texelsInBatch;
    let batches = ``;
    let coords = "b, r, c";
    for(let b = 2; b < shape.length - 1; b++){
        texelsInBatchN *= shape[shape.length - b - 1];
        batches = `
      int b${b} = index / ${texelsInBatchN};
      index -= b${b} * ${texelsInBatchN};
    ` + batches;
        coords = `b${b}, ` + coords;
    }
    return `
    ivec${shape.length} getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;

      ${batches}

      int b = index / ${texelsInBatch};
      index -= b * ${texelsInBatch};

      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec${shape.length}(${coords});
    }
  `;
}
function getOutput4DCoords(shape, texShape, enableShapeUniforms) {
    if (enableShapeUniforms) {
        const coordsFromIndexSnippet = _shaderCompilerUtil.getOutputLogicalCoordinatesFromFlatIndexByUniform([
            "r",
            "c",
            "d",
            "d2"
        ], shape);
        return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      ${coordsFromIndexSnippet}
      return ivec4(r, c, d, d2);
    }
  `;
    }
    const coordsFromIndexSnippet = _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndex([
        "r",
        "c",
        "d",
        "d2"
    ], shape);
    return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      ${coordsFromIndexSnippet}
      return ivec4(r, c, d, d2);
    }
  `;
}
function getOutput5DCoords(shape, texShape) {
    const coordsFromIndexSnippet = _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndex([
        "r",
        "c",
        "d",
        "d2",
        "d3"
    ], shape);
    return `
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
                             ${texShape[1]}));

      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;

      ${coordsFromIndexSnippet}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `;
}
function getOutput6DCoords(shape, texShape) {
    const coordsFromIndexSnippet = _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndex([
        "r",
        "c",
        "d",
        "d2",
        "d3",
        "d4"
    ], shape);
    return `
    ivec6 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;

      ${coordsFromIndexSnippet}

      ivec6 result = ivec6(r, c, d, d2, d3, d4);
      return result;
    }
  `;
}
function getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    if ((0, _tfjsCore.util).arraysEqual(shape, texShape)) {
        if (enableShapeUniforms) return `
      ivec2 getOutputCoords() {
        ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
        return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
      }
    `;
        return `
      ivec2 getOutputCoords() {
        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
      }
    `;
    }
    // texels needed to accommodate a logical row
    const texelsInLogicalRow = Math.ceil(shape[1] / 2);
    /**
     * getOutputCoords
     *
     * resTexRC: The rows and columns of the texels. If you move over one
     * texel to the right in the packed texture, you are moving over one column
     * (not two).
     *
     * index: The texel index
     */ if (enableShapeUniforms) return `
    ivec2 getOutputCoords() {
      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(packedTexShape[0], packedTexShape[1]));

      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
      int r = 2 * (index / texelsInLogicalRow);
      int c = imod(index, texelsInLogicalRow) * 2;

      return ivec2(r, c);
    }
  `;
    return `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));

      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
      int r = 2 * (index / ${texelsInLogicalRow});
      int c = imod(index, ${texelsInLogicalRow}) * 2;

      return ivec2(r, c);
    }
  `;
}
function getOutput2DCoords(shape, texShape, enableShapeUniforms) {
    if ((0, _tfjsCore.util).arraysEqual(shape, texShape)) {
        if (enableShapeUniforms) return `
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
      }
    `;
        return `
      ivec2 getOutputCoords() {
        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
      }
    `;
    }
    if (shape[1] === 1) {
        if (enableShapeUniforms) return `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(index, 0);
      }
    `;
        return `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${texShape[0]}, ${texShape[1]}));
        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
        return ivec2(index, 0);
      }
    `;
    }
    if (shape[0] === 1) {
        if (enableShapeUniforms) return `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(outTexShape[0], outTexShape[1]));
        int index = resTexRC.x * outTexShape[1] + resTexRC.y;
        return ivec2(0, index);
      }
    `;
        return `
      ivec2 getOutputCoords() {
        ivec2 resTexRC = ivec2(resultUV.yx *
                               vec2(${texShape[0]}, ${texShape[1]}));
        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
        return ivec2(0, index);
      }
    `;
    }
    if (enableShapeUniforms) return `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(outTexShape[0], outTexShape[1]));
      int index = resTexRC.x * outTexShape[1] + resTexRC.y;
      int r = index / outShape[1];
      int c = index - r * outShape[1];
      return ivec2(r, c);
    }
  `;
    return `
    ivec2 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${texShape[0]}, ${texShape[1]}));
      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
      int r = index / ${shape[1]};
      int c = index - r * ${shape[1]};
      return ivec2(r, c);
    }
  `;
}
function getFlatOffsetUniformName(texName) {
    return `offset${texName}`;
}
function getPackedSamplerScalar(inputInfo) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const glsl = (0, _glslVersion.getGlslDifferences)();
    return `
    vec4 ${funcName}() {
      return ${glsl.texture2D}(${texName}, halfCR);
    }
  `;
}
function getSamplerScalar(inputInfo, enableShapeUniforms) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    if (inputInfo.shapeInfo.isUniform) return `float ${funcName}() {return ${texName};}`;
    const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;
    if (texNumR === 1 && texNumC === 1) return `
      float ${funcName}() {
        return sampleTexture(${texName}, halfCR);
      }
    `;
    const offset = getFlatOffsetUniformName(texName);
    if (enableShapeUniforms) return `
    float ${funcName}() {
      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
    const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;
    return `
    float ${funcName}() {
      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
}
function getPackedSampler1D(inputInfo, enableShapeUniforms) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    const glsl = (0, _glslVersion.getGlslDifferences)();
    if (enableShapeUniforms) return `
    vec4 ${funcName}(int index) {
      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
      vec2 uv = packedUVfrom1D(
        packedTexShape[0], packedTexShape[1], index);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    return `
    vec4 ${funcName}(int index) {
      vec2 uv = packedUVfrom1D(
        ${packedTexShape[0]}, ${packedTexShape[1]}, index);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
}
function getSampler1D(inputInfo, enableShapeUniforms) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    if (inputInfo.shapeInfo.isUniform) // Uniform arrays will be less than 65505 (no risk of float16 overflow).
    return `
      float ${funcName}(int index) {
        ${getUniformSampler(inputInfo)}
      }
    `;
    const texShape = inputInfo.shapeInfo.texShape;
    const tNumR = texShape[0];
    const tNumC = texShape[1];
    if (tNumC === 1 && tNumR === 1) return `
      float ${funcName}(int index) {
        return sampleTexture(${texName}, halfCR);
      }
    `;
    const offset = getFlatOffsetUniformName(texName);
    if (tNumC === 1) {
        if (enableShapeUniforms) return `
      float ${funcName}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
      float ${funcName}(int index) {
        vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    if (tNumR === 1) {
        if (enableShapeUniforms) return `
      float ${funcName}(int index) {
        vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
      float ${funcName}(int index) {
        vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    if (enableShapeUniforms) return `
    float ${funcName}(int index) {
      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
    return `
    float ${funcName}(int index) {
      vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
}
function getPackedSampler2D(inputInfo, enableShapeUniforms) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    const glsl = (0, _glslVersion.getGlslDifferences)();
    if (texShape != null && (0, _tfjsCore.util).arraysEqual(shape, texShape)) {
        if (enableShapeUniforms) return `
      vec4 ${funcName}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);

        return ${glsl.texture2D}(${texName}, uv);
      }
    `;
        return `
      vec4 ${funcName}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);

        return ${glsl.texture2D}(${texName}, uv);
      }
    `;
    }
    if (enableShapeUniforms) return `
    vec4 ${funcName}(int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    const valuesPerRow = Math.ceil(shape[1] / 2);
    return `
    vec4 ${funcName}(int row, int col) {
      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
}
function getSampler2D(inputInfo, enableShapeUniforms) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    if (texShape != null && (0, _tfjsCore.util).arraysEqual(shape, texShape)) {
        if (enableShapeUniforms) return `
      float ${funcName}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
        return sampleTexture(${texName}, uv);
      }
    `;
        const texNumR = texShape[0];
        const texNumC = texShape[1];
        return `
    float ${funcName}(int row, int col) {
      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    const { newShape, keptDims } = (0, _tfjsCore.util).squeezeShape(shape);
    const squeezedShape = newShape;
    if (squeezedShape.length < shape.length) {
        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
        const params = [
            "row",
            "col"
        ];
        return `
      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
      float ${funcName}(int row, int col) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    if (inputInfo.shapeInfo.isUniform) // Uniform arrays will be less than 65505 (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    const offset = getFlatOffsetUniformName(texName);
    if (texNumC === 1) {
        // index is used directly as physical (no risk of float16 overflow).
        if (enableShapeUniforms) return `
      float ${funcName}(int row, int col) {
        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
        vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
    float ${funcName}(int row, int col) {
      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    if (texNumR === 1) {
        // index is used directly as physical (no risk of float16 overflow).
        if (enableShapeUniforms) return `
      float ${funcName}(int row, int col) {
        float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
        vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
    float ${funcName}(int row, int col) {
      float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    if (enableShapeUniforms) return `
      float ${funcName}(int row, int col) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${texName}Shape[1] + col + ${offset};
        vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
        return sampleTexture(${texName}, uv);
      }
    `;
    return `
  float ${funcName}(int row, int col) {
    // Explicitly use integer operations as dot() only works on floats.
    int index = row * ${shape[1]} + col + ${offset};
    vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
    return sampleTexture(${texName}, uv);
  }
`;
}
function getPackedSampler3D(inputInfo, enableShapeUniforms) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const texShape = inputInfo.shapeInfo.texShape;
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    if (shape[0] === 1) {
        const squeezedShape = shape.slice(1);
        const keptDims = [
            1,
            2
        ];
        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
        const params = [
            "b",
            "row",
            "col"
        ];
        return `
        ${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
        vec4 ${funcName}(int b, int row, int col) {
          return ${funcName}(${getSqueezedParams(params, keptDims)});
        }
      `;
    }
    const glsl = (0, _glslVersion.getGlslDifferences)();
    if (enableShapeUniforms) return `
    vec4 ${funcName}(int b, int row, int col) {
      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
      int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));
      vec2 uv = packedUVfrom3D(
        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
    const texNumR = packedTexShape[0];
    const texNumC = packedTexShape[1];
    const valuesPerRow = Math.ceil(shape[2] / 2);
    const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
    return `
    vec4 ${funcName}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
}
function getSampler3D(inputInfo, enableShapeUniforms) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const stride0 = shape[1] * shape[2];
    const stride1 = shape[2];
    const { newShape, keptDims } = (0, _tfjsCore.util).squeezeShape(shape);
    const squeezedShape = newShape;
    if (squeezedShape.length < shape.length) {
        const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
        const params = [
            "row",
            "col",
            "depth"
        ];
        return `
        ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
        float ${funcName}(int row, int col, int depth) {
          return ${funcName}(${getSqueezedParams(params, keptDims)});
        }
      `;
    }
    if (inputInfo.shapeInfo.isUniform) // Uniform arrays will be less than 65505 (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth) {
        int index = round(dot(vec3(row, col, depth),
                          vec3(${stride0}, ${stride1}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    if (texNumC === stride0 && flatOffset == null) {
        // texC is used directly as physical (no risk of float16 overflow).
        if (enableShapeUniforms) return `
      float ${funcName}(int row, int col, int depth) {
        int stride1 = ${texName}Shape[2];
        float texR = float(row);
        float texC = dot(vec2(col, depth), vec2(stride1, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
        float ${funcName}(int row, int col, int depth) {
          float texR = float(row);
          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
          vec2 uv = (vec2(texC, texR) + halfCR) /
                     vec2(${texNumC}.0, ${texNumR}.0);
          return sampleTexture(${texName}, uv);
        }
      `;
    }
    if (texNumC === stride1 && flatOffset == null) {
        // texR is used directly as physical (no risk of float16 overflow).
        if (enableShapeUniforms) return `
      float ${funcName}(int row, int col, int depth) {
        float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));
        float texC = float(depth);
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
    float ${funcName}(int row, int col, int depth) {
      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
      float texC = float(depth);
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
      return sampleTexture(${texName}, uv);
    }
  `;
    }
    const offset = getFlatOffsetUniformName(texName);
    if (enableShapeUniforms) return `
    float ${funcName}(int row, int col, int depth) {
      // Explicitly use integer operations as dot() only works on floats.
      int stride0 = ${texName}Shape[1] * ${texName}Shape[2];
      int stride1 = ${texName}Shape[2];
      int index = row * stride0 + col * stride1 + depth + ${offset};
      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
      return sampleTexture(${texName}, uv);
    }
    `;
    return `
      float ${funcName}(int row, int col, int depth) {
        // Explicitly use integer operations as dot() only works on floats.
        int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
        vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
        return sampleTexture(${texName}, uv);
      }
  `;
}
function getPackedSamplerND(inputInfo, enableShapeUniforms) {
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const glsl = (0, _glslVersion.getGlslDifferences)();
    if (enableShapeUniforms) // TODO: support 5d and 6d
    return `
    vec4 ${funcName}(int b2, int b, int row, int col) {
      int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));
      int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));
      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
      texelsInBatch *= ${texName}Shape[1];
      index = b2 * texelsInBatch + index;
      ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
      int texR = index / packedTexShape[1];
      int texC = index - texR * packedTexShape[1];
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);
    }
  `;
    const shape = inputInfo.shapeInfo.logicalShape;
    const rank = shape.length;
    const texShape = inputInfo.shapeInfo.texShape;
    const packedTexShape = [
        Math.ceil(texShape[0] / 2),
        Math.ceil(texShape[1] / 2)
    ];
    const texNumR = packedTexShape[0];
    const texNumC = packedTexShape[1];
    const valuesPerRow = Math.ceil(shape[rank - 1] / 2);
    let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
    let params = `int b, int row, int col`;
    let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;
    for(let b = 2; b < rank - 1; b++){
        params = `int b${b}, ` + params;
        texelsInBatch *= shape[rank - b - 1];
        index = `b${b} * ${texelsInBatch} + ` + index;
    }
    return `
    vec4 ${funcName}(${params}) {
      int index = ${index};
      int texR = index / ${texNumC};
      int texC = index - texR * ${texNumC};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
      return ${glsl.texture2D}(${texName}, uv);
    }
  `;
}
function getSampler4D(inputInfo, enableShapeUniforms) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const stride2 = shape[3];
    const stride1 = shape[2] * stride2;
    const stride0 = shape[1] * stride1;
    const { newShape, keptDims } = (0, _tfjsCore.util).squeezeShape(shape);
    if (newShape.length < shape.length) {
        const newInputInfo = squeezeInputInfo(inputInfo, newShape);
        const params = [
            "row",
            "col",
            "depth",
            "depth2"
        ];
        return `
      ${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
      float ${funcName}(int row, int col, int depth, int depth2) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    if (inputInfo.shapeInfo.isUniform) // Uniform arrays will be less than 65505 (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        int index = round(dot(vec4(row, col, depth, depth2),
                          vec4(${stride0}, ${stride1}, ${stride2}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    const stride2Str = `int stride2 = ${texName}Shape[3];`;
    const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;
    const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;
    if (texNumC === stride0 && flatOffset == null) {
        // texC is used directly as physical (no risk of float16 overflow).
        if (enableShapeUniforms) return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        ${stride2Str}
        ${stride1Str}
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(stride1, stride2, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texName}TexShape[1], ${texName}TexShape[0]);
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        float texR = float(row);
        float texC =
            dot(vec3(col, depth, depth2),
                vec3(${stride1}, ${stride2}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    if (texNumC === stride2 && flatOffset == null) {
        // texR is used directly as physical (no risk of float16 overflow).
        if (enableShapeUniforms) return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texName}TexShape[1], ${texName}TexShape[0]);
        return sampleTexture(${texName}, uv);
      }
    `;
        return `
      float ${funcName}(int row, int col, int depth, int depth2) {
        float texR = dot(vec3(row, col, depth),
                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));
        float texC = float(depth2);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    }
    const offset = getFlatOffsetUniformName(texName);
    if (enableShapeUniforms) return `
    float ${funcName}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      ${stride2Str}
      ${stride1Str}
      ${stride0Str}
      int index = row * stride0 + col * stride1 +
          depth * stride2 + depth2;
      vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
    return `
    float ${funcName}(int row, int col, int depth, int depth2) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} +
          depth * ${stride2} + depth2;
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
      return sampleTexture(${texName}, uv);
    }
  `;
}
function getSampler5D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const stride3 = shape[4];
    const stride2 = shape[3] * stride3;
    const stride1 = shape[2] * stride2;
    const stride0 = shape[1] * stride1;
    const { newShape, keptDims } = (0, _tfjsCore.util).squeezeShape(shape);
    if (newShape.length < shape.length) {
        const newInputInfo = squeezeInputInfo(inputInfo, newShape);
        const params = [
            "row",
            "col",
            "depth",
            "depth2",
            "depth3"
        ];
        return `
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    if (inputInfo.shapeInfo.isUniform) // Uniform arrays will be less than 65505 (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        float index = dot(
          vec4(row, col, depth, depth2),
          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
          depth3;
        ${getUniformSampler(inputInfo)}
      }
    `;
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    if (texNumC === stride0 && flatOffset == null) // texC is used directly as physical (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
                         vec4(${stride1}, ${stride2}, ${stride3}, 1));
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    if (texNumC === stride3 && flatOffset == null) // texR is used directly as physical (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
        float texR = dot(
          vec4(row, col, depth, depth2),
          vec4(${shape[1] * shape[2] * shape[3]},
               ${shape[2] * shape[3]}, ${shape[3]}, 1));
        int texC = depth3;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    const offset = getFlatOffsetUniformName(texName);
    return `
    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
          depth2 * ${stride3} + depth3 + ${offset};
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
      return sampleTexture(${texName}, uv);
    }
  `;
}
function getSampler6D(inputInfo) {
    const shape = inputInfo.shapeInfo.logicalShape;
    const texName = inputInfo.name;
    const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
    const { newShape, keptDims } = (0, _tfjsCore.util).squeezeShape(shape);
    if (newShape.length < shape.length) {
        const newInputInfo = squeezeInputInfo(inputInfo, newShape);
        const params = [
            "row",
            "col",
            "depth",
            "depth2",
            "depth3",
            "depth4"
        ];
        return `
      ${getSamplerFromInInfo(newInputInfo)}
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${funcName}(${getSqueezedParams(params, keptDims)});
      }
    `;
    }
    const stride4 = shape[5];
    const stride3 = shape[4] * stride4;
    const stride2 = shape[3] * stride3;
    const stride1 = shape[2] * stride2;
    const stride0 = shape[1] * stride1;
    if (inputInfo.shapeInfo.isUniform) // Uniform arrays will be less than 65505 (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
        int index = round(dot(
          vec4(row, col, depth, depth2),
          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
          dot(
            vec2(depth3, depth4),
            vec2(${stride4}, 1)));
        ${getUniformSampler(inputInfo)}
      }
    `;
    const flatOffset = inputInfo.shapeInfo.flatOffset;
    const texShape = inputInfo.shapeInfo.texShape;
    const texNumR = texShape[0];
    const texNumC = texShape[1];
    if (texNumC === stride0 && flatOffset == null) // texC is used directly as physical (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        int texR = row;
        float texC = dot(vec4(col, depth, depth2, depth3),
          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
               float(depth4);
        vec2 uv = (vec2(texC, texR) + halfCR) /
                   vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    if (texNumC === stride4 && flatOffset == null) // texR is used directly as physical (no risk of float16 overflow).
    return `
      float ${funcName}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        float texR = dot(vec4(row, col, depth, depth2),
          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},
               ${shape[2] * shape[3] * shape[4]},
               ${shape[3] * shape[4]},
               ${shape[4]})) + float(depth3);
        int texC = depth4;
        vec2 uv = (vec2(texC, texR) + halfCR) /
                  vec2(${texNumC}.0, ${texNumR}.0);
        return sampleTexture(${texName}, uv);
      }
    `;
    const offset = getFlatOffsetUniformName(texName);
    return `
    float ${funcName}(int row, int col, int depth,
                  int depth2, int depth3, int depth4) {
      // Explicitly use integer operations as dot() only works on floats.
      int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
          depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
      vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
      return sampleTexture(${texName}, uv);
    }
  `;
}
function getUniformSampler(inputInfo) {
    const texName = inputInfo.name;
    const inSize = (0, _tfjsCore.util).sizeFromShape(inputInfo.shapeInfo.logicalShape);
    if (inSize < 2) return `return ${texName};`;
    return `
    for (int i = 0; i < ${inSize}; i++) {
      if (i == index) {
        return ${texName}[i];
      }
    }
  `;
}
function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
    const texName = inputInfo.name;
    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
    const funcName = "get" + texFuncSnippet + "AtOutCoords";
    const inRank = inputInfo.shapeInfo.logicalShape.length;
    const outRank = outShapeInfo.logicalShape.length;
    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
    const type = getCoordsDataType(outRank);
    const rankDiff = outRank - inRank;
    let coordsSnippet;
    const fields = [
        "x",
        "y",
        "z",
        "w",
        "u",
        "v"
    ];
    if (inRank === 0) coordsSnippet = "";
    else if (outRank < 2 && broadcastDims.length >= 1) coordsSnippet = "coords = 0;";
    else coordsSnippet = broadcastDims.map((d)=>`coords.${fields[d + rankDiff]} = 0;`).join("\n");
    let unpackedCoordsSnippet = "";
    if (outRank < 2 && inRank > 0) unpackedCoordsSnippet = "coords";
    else unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i)=>`coords.${fields[i + rankDiff]}`).join(", ");
    let output = `return outputValue;`;
    const inSize = (0, _tfjsCore.util).sizeFromShape(inputInfo.shapeInfo.logicalShape);
    const isInputScalar = inSize === 1;
    const outSize = (0, _tfjsCore.util).sizeFromShape(outShapeInfo.logicalShape);
    const isOutputScalar = outSize === 1;
    if (inRank === 1 && !isInputScalar && !isOutputScalar) output = `
      return vec4(outputValue.xy, outputValue.xy);
    `;
    else if (isInputScalar && !isOutputScalar) {
        if (outRank === 1) output = `
        return vec4(outputValue.x, outputValue.x, 0., 0.);
      `;
        else output = `
        return vec4(outputValue.x);
      `;
    } else if (broadcastDims.length) {
        const rows = inRank - 2;
        const cols = inRank - 1;
        if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) output = `return vec4(outputValue.x);`;
        else if (broadcastDims.indexOf(rows) > -1) output = `return vec4(outputValue.x, outputValue.y, ` + `outputValue.x, outputValue.y);`;
        else if (broadcastDims.indexOf(cols) > -1) output = `return vec4(outputValue.xx, outputValue.zz);`;
    }
    return `
    vec4 ${funcName}() {
      ${type} coords = getOutputCoords();
      ${coordsSnippet}
      vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
      ${output}
    }
  `;
}
function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
    const texName = inputInfo.name;
    const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
    const funcName = "get" + texFuncSnippet + "AtOutCoords";
    const outTexShape = outShapeInfo.texShape;
    const inTexShape = inputInfo.shapeInfo.texShape;
    const inRank = inputInfo.shapeInfo.logicalShape.length;
    const outRank = outShapeInfo.logicalShape.length;
    if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && (0, _tfjsCore.util).arraysEqual(inTexShape, outTexShape)) return `
      float ${funcName}() {
        return sampleTexture(${texName}, resultUV);
      }
    `;
    const type = getCoordsDataType(outRank);
    const broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
    const rankDiff = outRank - inRank;
    let coordsSnippet;
    const fields = [
        "x",
        "y",
        "z",
        "w",
        "u",
        "v"
    ];
    if (inRank === 0) coordsSnippet = "";
    else if (outRank < 2 && broadcastDims.length >= 1) coordsSnippet = "coords = 0;";
    else coordsSnippet = broadcastDims.map((d)=>`coords.${fields[d + rankDiff]} = 0;`).join("\n");
    let unpackedCoordsSnippet = "";
    if (outRank < 2 && inRank > 0) unpackedCoordsSnippet = "coords";
    else unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i)=>`coords.${fields[i + rankDiff]}`).join(", ");
    return `
    float ${funcName}() {
      ${type} coords = getOutputCoords();
      ${coordsSnippet}
      return get${texFuncSnippet}(${unpackedCoordsSnippet});
    }
  `;
}
function getCoordsDataType(rank) {
    if (rank <= 1) return "int";
    else if (rank === 2) return "ivec2";
    else if (rank === 3) return "ivec3";
    else if (rank === 4) return "ivec4";
    else if (rank === 5) return "ivec5";
    else if (rank === 6) return "ivec6";
    else throw Error(`GPU for rank ${rank} is not yet supported`);
}
function getUniformInfoFromShape(isPacked, shape, texShape) {
    const { newShape, keptDims } = (0, _tfjsCore.util).squeezeShape(shape);
    const rank = shape.length;
    const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;
    const squeezeShape = useSqueezePackedShape ? shape.slice(1) : newShape;
    const useSqueezeShape = !isPacked && rank > 1 && !(0, _tfjsCore.util).arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;
    const uniformShape = useSqueezeShape ? squeezeShape : shape;
    return {
        useSqueezeShape,
        uniformShape,
        keptDims
    };
}
function squeezeInputInfo(inInfo, squeezedShape) {
    // Deep copy.
    const newInputInfo = JSON.parse(JSON.stringify(inInfo));
    newInputInfo.shapeInfo.logicalShape = squeezedShape;
    return newInputInfo;
}
function getSqueezedParams(params, keptDims) {
    return keptDims.map((d)=>params[d]).join(", ");
}

},{"@tensorflow/tfjs-core":"21Ftl","./glsl_version":"kG2vE","./shader_compiler_util":"4ROqF","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4ROqF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Produces GLSL code that derives logical coordinates from a flat
 * index. The code performs integer division with each stride and decrements
 * the index until the index equals the final dimension coordinate.
 */ parcelHelpers.export(exports, "getLogicalCoordinatesFromFlatIndex", ()=>getLogicalCoordinatesFromFlatIndex);
parcelHelpers.export(exports, "getOutputLogicalCoordinatesFromFlatIndexByUniform", ()=>getOutputLogicalCoordinatesFromFlatIndexByUniform);
parcelHelpers.export(exports, "getLogicalCoordinatesFromFlatIndexByUniform", ()=>getLogicalCoordinatesFromFlatIndexByUniform);
/**
 * Produces GLSL code that computes the dot product of the input x and y
 * vectors. Handles splitting inputs into increments of vec4s when necessary.
 */ parcelHelpers.export(exports, "dotify", ()=>dotify);
/**
 * Produces GLSL that computes the flat index from 3D coordinates.
 */ parcelHelpers.export(exports, "getFlatIndexFrom3D", ()=>getFlatIndexFrom3D);
parcelHelpers.export(exports, "getFlatIndexFrom3DOutput", ()=>getFlatIndexFrom3DOutput);
parcelHelpers.export(exports, "ENCODE_FLOAT_SNIPPET", ()=>ENCODE_FLOAT_SNIPPET);
var _tfjsCore = require("@tensorflow/tfjs-core");
function getLogicalCoordinatesFromFlatIndex(coords, shape, index = "index") {
    const strides = (0, _tfjsCore.util).computeStrides(shape);
    return strides.map((stride, i)=>{
        const line1 = `int ${coords[i]} = ${index} / ${stride}`;
        const line2 = i === strides.length - 1 ? `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` : `index -= ${coords[i]} * ${stride}`;
        return `${line1}; ${line2};`;
    }).join("");
}
function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords, shape, index = "index") {
    const strides = (0, _tfjsCore.util).computeStrides(shape);
    return strides.map((_, i)=>{
        const line1 = `int ${coords[i]} = ${index} / outShapeStrides[${i}]`;
        const line2 = i === strides.length - 1 ? `int ${coords[i + 1]} = ${index} - ${coords[i]} * outShapeStrides[${i}]` : `index -= ${coords[i]} * outShapeStrides[${i}]`;
        return `${line1}; ${line2};`;
    }).join("");
}
// Produces GLSL code that computes strides.
function symbolicallyComputeStrides(indicesArr, variableName) {
    const numCoords = indicesArr.length;
    const shape = indicesArr.map((d)=>`${variableName}[${d}]`);
    const strides = new Array(numCoords - 1);
    strides[numCoords - 2] = shape[numCoords - 1];
    for(let i = numCoords - 3; i >= 0; --i)strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;
    return strides;
}
function getLogicalCoordinatesFromFlatIndexByUniform(coords, variableName, index = "index") {
    const indicesArray = coords.map((_, i)=>i);
    const strides = symbolicallyComputeStrides(indicesArray, variableName);
    return strides.map((_, i)=>{
        const line1 = `int ${coords[i]} = ${index} / ${strides[i]}`;
        const line2 = i === strides.length - 1 ? `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${strides[i]}` : `index -= ${coords[i]} * ${strides[i]}`;
        return `${line1}; ${line2};`;
    }).join("");
}
function buildVec(x) {
    if (x.length === 1) return `${x[0]}`;
    return `vec${x.length}(${x.join(",")})`;
}
function dotify(x, y) {
    if (x.length !== y.length) throw new Error(`Vectors to be dotted must be of the same length -` + `got ${x.length} and ${y.length}`);
    const slices = [];
    const nearestVec4 = Math.floor(x.length / 4);
    const nearestVec4Remainder = x.length % 4;
    for(let i = 0; i < nearestVec4; i++){
        const xSlice = x.slice(i * 4, i * 4 + 4);
        const ySlice = y.slice(i * 4, i * 4 + 4);
        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);
    }
    if (nearestVec4Remainder !== 0) {
        let xSlice = x.slice(nearestVec4 * 4);
        let ySlice = y.slice(nearestVec4 * 4);
        if (xSlice.length === 1) {
            xSlice = xSlice.map((d)=>`float(${d})`);
            ySlice = ySlice.map((d)=>`float(${d})`);
        }
        slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);
    }
    return slices.map((d, i)=>`dot(${d})`).join("+");
}
function getFlatIndexFrom3D(shape) {
    const strides = (0, _tfjsCore.util).computeStrides(shape).map((d)=>d.toString());
    return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
  }
`;
}
function getFlatIndexFrom3DOutput() {
    return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`;
}
const ENCODE_FLOAT_SNIPPET = `
  const float FLOAT_MAX = 1.70141184e38;
  const float FLOAT_MIN = 1.17549435e-38;

  lowp vec4 encode_float(highp float v) {
    if (isnan(v)) {
      return vec4(255, 255, 255, 255);
    }

    highp float av = abs(v);

    if(av < FLOAT_MIN) {
      return vec4(0.0, 0.0, 0.0, 0.0);
    } else if(v > FLOAT_MAX) {
      return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
    } else if(v < -FLOAT_MAX) {
      return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;
    }

    highp vec4 c = vec4(0,0,0,0);

    highp float e = floor(log2(av));
    highp float m = exp2(fract(log2(av))) - 1.0;

    c[2] = floor(128.0 * m);
    m -= c[2] / 128.0;
    c[1] = floor(32768.0 * m);
    m -= c[1] / 32768.0;
    c[0] = floor(8388608.0 * m);

    highp float ebias = e + 127.0;
    c[3] = floor(ebias / 2.0);
    ebias -= c[3] * 2.0;
    c[2] += floor(ebias) * 128.0;

    c[3] += 128.0 * step(0.0, -v);

    return c / 255.0;
  }
`;

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gZFiF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DecodeMatrixPackedProgram", ()=>DecodeMatrixPackedProgram);
var _glslVersion = require("./glsl_version");
var _gpgpuMath = require("./gpgpu_math");
var _shaderCompilerUtil = require("./shader_compiler_util");
var _texUtil = require("./tex_util");
class DecodeMatrixPackedProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outPackingScheme = (0, _texUtil.PackingScheme).DENSE;
        this.customUniforms = [
            {
                name: "texShape",
                type: "ivec2"
            }
        ];
        const glsl = (0, _glslVersion.getGlslDifferences)();
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? _shaderCompilerUtil.getOutputLogicalCoordinatesFromFlatIndexByUniform([
            "r",
            "c",
            "d"
        ], outputShape) : _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndex([
            "r",
            "c",
            "d"
        ], outputShape)}
        return ivec3(r, c, d);
      }

      void main() {
        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);

        vec4 result = vec4(0.);

        for (int i=0; i<4; i++) {
          int flatIndex = index + i;
          ivec3 rc = outCoordsFromFlatIndex(flatIndex);
          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
        }

        ${glsl.output} = result;
      }
    `;
    }
}

},{"./glsl_version":"kG2vE","./gpgpu_math":"btJo5","./shader_compiler_util":"4ROqF","./tex_util":"1oYRe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g6NXt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "EncodeFloatProgram", ()=>EncodeFloatProgram);
var _glslVersion = require("./glsl_version");
var _shaderCompilerUtil = require("./shader_compiler_util");
var _texUtil = require("./tex_util");
class EncodeFloatProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        this.outTexUsage = (0, _texUtil.TextureUsage).DOWNLOAD;
        const glsl = (0, _glslVersion.getGlslDifferences)();
        this.outputShape = outputShape;
        this.userCode = `
      ${0, _shaderCompilerUtil.ENCODE_FLOAT_SNIPPET}

      void main() {
        float x = getAAtOutCoords();
        ${glsl.output} = encode_float(x);
      }
    `;
    }
}

},{"./glsl_version":"kG2vE","./shader_compiler_util":"4ROqF","./tex_util":"1oYRe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8lMpY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "EncodeFloatPackedProgram", ()=>EncodeFloatPackedProgram);
var _glslVersion = require("./glsl_version");
var _shaderCompilerUtil = require("./shader_compiler_util");
var _texUtil = require("./tex_util");
class EncodeFloatPackedProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = false;
        this.outTexUsage = (0, _texUtil.TextureUsage).DOWNLOAD;
        const glsl = (0, _glslVersion.getGlslDifferences)();
        this.outputShape = outputShape;
        this.userCode = `
      ${0, _shaderCompilerUtil.ENCODE_FLOAT_SNIPPET}

      void main() {
        ivec3 coords = getOutputCoords();
        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
        ${glsl.output} = encode_float(x);
      }
    `;
    }
}

},{"./glsl_version":"kG2vE","./shader_compiler_util":"4ROqF","./tex_util":"1oYRe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4fC2J":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "EncodeMatrixProgram", ()=>EncodeMatrixProgram);
var _glslVersion = require("./glsl_version");
var _gpgpuMath = require("./gpgpu_math");
var _shaderCompilerUtil = require("./shader_compiler_util");
const CHANNEL_CHAR_TO_INDEX_MAP = {
    "R": 0,
    "G": 1,
    "B": 2,
    "A": 3
};
class EncodeMatrixProgram {
    constructor(outputShape, inputIsUnsignedByte = false, usedChannels = "RGBA"){
        this.variableNames = [
            "A"
        ];
        this.customUniforms = [
            {
                name: "texShape",
                type: "ivec2"
            }
        ];
        const glsl = (0, _glslVersion.getGlslDifferences)();
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        let output = `result`;
        if (inputIsUnsignedByte) output = `floor(result * 255. + 0.5)`;
        let mainLoop = "";
        for(let usedChannelIndex = 0; usedChannelIndex < usedChannels.length; usedChannelIndex++){
            const curChannel = usedChannels[usedChannelIndex];
            mainLoop += `
          if(offset == ${usedChannelIndex}) {
            result = values[${CHANNEL_CHAR_TO_INDEX_MAP[curChannel]}];
          }`;
        }
        this.userCode = `
      ${this.enableShapeUniforms ? _shaderCompilerUtil.getFlatIndexFrom3DOutput() : _shaderCompilerUtil.getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 coords = getOutputCoords();
        int flatIndex = getFlatIndex(coords);
        float result = 0.;
        int offset = imod(flatIndex, ${usedChannels.length});

        flatIndex = idiv(flatIndex, ${usedChannels.length}, 1.);

        int r = flatIndex / texShape[1];
        if (r < texShape[0]) {
          int c = imod(flatIndex, texShape[1]);
          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
          vec4 values = ${glsl.texture2D}(A, uv);
          ${mainLoop}
        }
        ${glsl.output} = vec4(${output}, 0., 0., 0.);
      }
    `;
    }
}

},{"./glsl_version":"kG2vE","./gpgpu_math":"btJo5","./shader_compiler_util":"4ROqF","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aBUBT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/*
This is how the shader encodes a tensor with shape = [2, 3, 5]
(indices are [batch, row, col]).

000|001   002|003   004|xxx   020|021   022|023   024|xxx
-------   -------   -------   -------   -------   -------
010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx

100|101   102|103   104|xxx   120|121   122|123   124|xxx
-------   -------   -------   -------   -------   -------
110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx

Single texels contain only values from the same batch, and from adjacent rows
and columns.
 */ parcelHelpers.export(exports, "EncodeMatrixPackedProgram", ()=>EncodeMatrixPackedProgram);
var _glslVersion = require("./glsl_version");
var _gpgpuMath = require("./gpgpu_math");
var _shaderCompilerUtil = require("./shader_compiler_util");
class EncodeMatrixPackedProgram {
    constructor(outputShape, inputIsUnsignedByte = false){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = false;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "texShape",
                type: "ivec2"
            }
        ];
        const glsl = (0, _glslVersion.getGlslDifferences)();
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        let mainLoop = "";
        let output = "result";
        if (inputIsUnsignedByte) output = "floor(result * 255. + 0.5)";
        for(let row = 0; row <= 1; row++)for(let col = 0; col <= 1; col++){
            const channel = row * 2 + col;
            mainLoop += `
          localCoords = coords;
          if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? "outShape[2]" : `${outputShape[2]}`}) {
          localCoords[2] += ${col};
          if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? "outShape[1]" : `${outputShape[1]}`}) {
            localCoords[1] += ${row};

            flatIndex = getFlatIndex(localCoords);
            offset = imod(flatIndex, 4);

            flatIndex = idiv(flatIndex, 4, 1.);

            int r = flatIndex / texShape[1];
            int c = imod(flatIndex, texShape[1]);
            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
            values = ${glsl.texture2D}(A, uv);

            if (offset == 0) {
              result[${channel}] = values[0];
            } else if (offset == 1) {
              result[${channel}] = values[1];
            } else if (offset == 2) {
              result[${channel}] = values[2];
            } else {
              result[${channel}] = values[3];
            }
          }
        }
        `;
        }
        this.userCode = `
        ${this.enableShapeUniforms ? _shaderCompilerUtil.getFlatIndexFrom3DOutput() : _shaderCompilerUtil.getFlatIndexFrom3D(outputShape)}

        void main() {
          ivec3 coords = getOutputCoords();

          vec4 result = vec4(0.);
          int flatIndex, r, c, offset;
          ivec3 localCoords;
          vec2 uv;
          vec4 values;

          ${mainLoop}

          ${glsl.output} = ${output};
        }
    `;
    }
}

},{"./glsl_version":"kG2vE","./gpgpu_math":"btJo5","./shader_compiler_util":"4ROqF","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9nvvF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "GPGPUContext", ()=>GPGPUContext);
/**
 * Finds the index of the last true element using linear search.
 * Note: We can't do binary search because Chrome expects us to explicitly
 * test all fences before download:
 * https://github.com/tensorflow/tfjs/issues/1145
 */ parcelHelpers.export(exports, "linearSearchLastTrue", ()=>linearSearchLastTrue);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _canvasUtil = require("./canvas_util");
var _gpgpuUtil = require("./gpgpu_util");
var _texUtil = require("./tex_util");
var _webglUtil = require("./webgl_util");
class GPGPUContext {
    constructor(gl){
        this.outputTexture = null;
        this.program = null;
        this.disposed = false;
        this.itemsToPoll = [];
        const glVersion = (0, _tfjsCore.env)().getNumber("WEBGL_VERSION");
        if (gl != null) {
            this.gl = gl;
            (0, _canvasUtil.setWebGLContext)(glVersion, gl);
        } else this.gl = (0, _canvasUtil.getWebGLContext)(glVersion);
        gl = this.gl;
        if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) {
            const gl2 = gl;
            this.createVertexArray = ()=>{
                return _webglUtil.callAndCheck(gl2, ()=>gl2.createVertexArray());
            };
            this.bindVertexArray = (vao)=>{
                return _webglUtil.callAndCheck(gl2, ()=>gl2.bindVertexArray(vao));
            };
            this.deleteVertexArray = (vao)=>{
                return _webglUtil.callAndCheck(gl2, ()=>gl2.deleteVertexArray(vao));
            };
            this.getVertexArray = ()=>{
                return _webglUtil.callAndCheck(gl2, ()=>gl2.getParameter(gl2.VERTEX_ARRAY_BINDING));
            };
        } else if (gl != null) {
            const ext = gl.getExtension("OES_vertex_array_object");
            if (ext == null) throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");
            this.createVertexArray = ()=>{
                return _webglUtil.callAndCheck(gl, ()=>ext.createVertexArrayOES());
            };
            this.bindVertexArray = (vao)=>{
                return _webglUtil.callAndCheck(gl, ()=>ext.bindVertexArrayOES(vao));
            };
            this.deleteVertexArray = (vao)=>{
                return _webglUtil.callAndCheck(gl, ()=>ext.deleteVertexArrayOES(vao));
            };
            this.getVertexArray = ()=>{
                return _webglUtil.callAndCheck(gl, ()=>gl.getParameter(ext.VERTEX_ARRAY_BINDING_OES));
            };
        }
        // WebGL 2.0 enables texture floats without an extension.
        let COLOR_BUFFER_FLOAT = "WEBGL_color_buffer_float";
        const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
        this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile");
        if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 1) {
            const TEXTURE_FLOAT = "OES_texture_float";
            const TEXTURE_HALF_FLOAT = "OES_texture_half_float";
            this.textureFloatExtension = _webglUtil.getExtensionOrThrow(this.gl, TEXTURE_FLOAT);
            if (_webglUtil.hasExtension(this.gl, TEXTURE_HALF_FLOAT)) this.textureHalfFloatExtension = _webglUtil.getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);
            else if ((0, _tfjsCore.env)().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
            this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
            if (_webglUtil.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) this.colorBufferHalfFloatExtension = _webglUtil.getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);
            else if ((0, _tfjsCore.env)().get("WEBGL_FORCE_F16_TEXTURES")) throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
        } else {
            COLOR_BUFFER_FLOAT = "EXT_color_buffer_float";
            if (_webglUtil.hasExtension(this.gl, COLOR_BUFFER_FLOAT)) this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
            else if (_webglUtil.hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
            else throw new Error("GL context does not support color renderable floats");
        }
        this.vertexBuffer = _gpgpuUtil.createVertexBuffer(this.gl);
        this.indexBuffer = _gpgpuUtil.createIndexBuffer(this.gl);
        this.framebuffer = _webglUtil.createFramebuffer(this.gl);
        this.textureConfig = _texUtil.getTextureConfig(this.gl, this.textureHalfFloatExtension);
    }
    get debug() {
        return (0, _tfjsCore.env)().getBool("DEBUG");
    }
    dispose() {
        if (this.disposed) return;
        if (this.program != null) console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.");
        if (this.outputTexture != null) console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");
        const gl = this.gl;
        _webglUtil.callAndCheck(gl, ()=>gl.finish());
        _webglUtil.callAndCheck(gl, ()=>gl.bindFramebuffer(gl.FRAMEBUFFER, null));
        _webglUtil.callAndCheck(gl, ()=>gl.deleteFramebuffer(this.framebuffer));
        _webglUtil.callAndCheck(gl, ()=>gl.bindBuffer(gl.ARRAY_BUFFER, null));
        _webglUtil.callAndCheck(gl, ()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));
        _webglUtil.callAndCheck(gl, ()=>gl.deleteBuffer(this.indexBuffer));
        this.disposed = true;
    }
    createFloat32MatrixTexture(rows, columns) {
        this.throwIfDisposed();
        return _gpgpuUtil.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    createFloat16MatrixTexture(rows, columns) {
        this.throwIfDisposed();
        return _gpgpuUtil.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    createUnsignedBytesMatrixTexture(rows, columns) {
        this.throwIfDisposed();
        return _gpgpuUtil.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    uploadPixelDataToTexture(texture, pixels) {
        this.throwIfDisposed();
        _gpgpuUtil.uploadPixelDataToTexture(this.gl, texture, pixels);
    }
    uploadDenseMatrixToTexture(texture, width, height, data) {
        this.throwIfDisposed();
        _gpgpuUtil.uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);
    }
    createFloat16PackedMatrixTexture(rows, columns) {
        this.throwIfDisposed();
        return _gpgpuUtil.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    createPackedMatrixTexture(rows, columns) {
        this.throwIfDisposed();
        return _gpgpuUtil.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
    }
    deleteMatrixTexture(texture) {
        this.throwIfDisposed();
        if (this.outputTexture === texture) {
            _webglUtil.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
            this.outputTexture = null;
        }
        _webglUtil.callAndCheck(this.gl, ()=>this.gl.deleteTexture(texture));
    }
    downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {
        return this.downloadMatrixDriver(texture, ()=>_gpgpuUtil.downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));
    }
    downloadPackedMatrixFromBuffer(buffer, batch, rows, columns, physicalRows, physicalCols) {
        return _gpgpuUtil.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
    }
    downloadFloat32MatrixFromBuffer(buffer, size) {
        return _gpgpuUtil.downloadFloat32MatrixFromBuffer(this.gl, buffer, size);
    }
    createBufferFromTexture(texture, rows, columns) {
        this.bindTextureToFrameBuffer(texture);
        const result = _gpgpuUtil.createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);
        this.unbindTextureToFrameBuffer();
        return result;
    }
    createAndWaitForFence() {
        const fenceContext = this.createFence(this.gl);
        return this.pollFence(fenceContext);
    }
    createFence(gl) {
        let query;
        let isFencePassed;
        if ((0, _tfjsCore.env)().getBool("WEBGL_FENCE_API_ENABLED")) {
            const gl2 = gl;
            const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);
            gl.flush();
            isFencePassed = ()=>{
                const status = gl2.clientWaitSync(sync, 0, 0);
                return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;
            };
            query = sync;
        } else if ((0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0) {
            query = this.beginQuery();
            this.endQuery();
            isFencePassed = ()=>this.isQueryAvailable(query, (0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
        } else // If we have no way to fence, return true immediately. This will fire in
        // WebGL 1.0 when there is no disjoint query timer. In this case, because
        // the fence passes immediately, we'll immediately ask for a download of
        // the texture, which will cause the UI thread to hang.
        isFencePassed = ()=>true;
        return {
            query,
            isFencePassed
        };
    }
    downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {
        return this.downloadMatrixDriver(texture, ()=>_gpgpuUtil.downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));
    }
    createProgram(fragmentShader) {
        this.throwIfDisposed();
        const gl = this.gl;
        if (this.vertexShader == null) this.vertexShader = _gpgpuUtil.createVertexShader(gl);
        const program = _webglUtil.createProgram(gl);
        _webglUtil.callAndCheck(gl, ()=>gl.attachShader(program, this.vertexShader));
        _webglUtil.callAndCheck(gl, ()=>gl.attachShader(program, fragmentShader));
        _webglUtil.linkProgram(gl, program);
        const program2 = Object.assign(program, {
            vao: this.createVertexArray()
        });
        if (this.debug) _webglUtil.validateProgram(gl, program2);
        return program2;
    }
    buildVao(program) {
        this.setProgram(program);
        this.bindVertexArray(program.vao);
        const gl = this.gl;
        // Bind index buffer, and vertex buffers based on program attrib
        // locations.
        _webglUtil.callAndCheck(gl, ()=>gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, this.indexBuffer));
        _gpgpuUtil.bindVertexProgramAttributeStreams(gl, program, this.vertexBuffer);
    }
    deleteProgram(program) {
        this.throwIfDisposed();
        if (program === this.program) this.program = null;
        if (program != null) {
            _webglUtil.callAndCheck(this.gl, ()=>this.gl.deleteProgram(program));
            this.deleteVertexArray(program.vao);
        }
    }
    setProgram(program) {
        this.throwIfDisposed();
        this.program = program;
        if (this.program != null) {
            if (this.debug) _webglUtil.validateProgram(this.gl, this.program);
        }
        _webglUtil.callAndCheck(this.gl, ()=>this.gl.useProgram(program));
    }
    getUniformLocation(program, uniformName, shouldThrow = true) {
        this.throwIfDisposed();
        if (shouldThrow) return _webglUtil.getProgramUniformLocationOrThrow(this.gl, program, uniformName);
        else return _webglUtil.getProgramUniformLocation(this.gl, program, uniformName);
    }
    getAttributeLocation(program, attribute) {
        this.throwIfDisposed();
        return _webglUtil.callAndCheck(this.gl, ()=>this.gl.getAttribLocation(program, attribute));
    }
    getUniformLocationNoThrow(program, uniformName) {
        this.throwIfDisposed();
        return this.gl.getUniformLocation(program, uniformName);
    }
    setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {
        this.throwIfDisposed();
        this.throwIfNoProgram();
        _webglUtil.bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);
    }
    setOutputMatrixTexture(outputMatrixTexture, rows, columns) {
        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
    }
    setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {
        this.throwIfDisposed();
        const [width, height] = _texUtil.getPackedMatrixTextureShapeWidthHeight(rows, columns);
        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
    }
    setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
    }
    setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
        throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
    }
    debugValidate() {
        if (this.program != null) _webglUtil.validateProgram(this.gl, this.program);
        _webglUtil.validateFramebuffer(this.gl);
    }
    executeProgram() {
        this.throwIfDisposed();
        this.throwIfNoProgram();
        const gl = this.gl;
        if (this.debug) {
            const boundVao = this.getVertexArray();
            console.assert(boundVao === this.program.vao, "VAO changed between setProgram and executeProgram!");
            this.debugValidate();
        }
        _webglUtil.callAndCheck(gl, ()=>gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));
    }
    blockUntilAllProgramsCompleted() {
        this.throwIfDisposed();
        _webglUtil.callAndCheck(this.gl, ()=>this.gl.finish());
    }
    getQueryTimerExtension() {
        if (this.disjointQueryTimerExtension == null) this.disjointQueryTimerExtension = _webglUtil.getExtensionOrThrow(this.gl, (0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query");
        return this.disjointQueryTimerExtension;
    }
    getQueryTimerExtensionWebGL2() {
        return this.getQueryTimerExtension();
    }
    getQueryTimerExtensionWebGL1() {
        return this.getQueryTimerExtension();
    }
    beginQuery() {
        if ((0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
            const gl2 = this.gl;
            const ext = this.getQueryTimerExtensionWebGL2();
            const query = gl2.createQuery();
            gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);
            return query;
        }
        const ext = this.getQueryTimerExtensionWebGL1();
        const query = ext.createQueryEXT();
        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
        return query;
    }
    endQuery() {
        if ((0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
            const gl2 = this.gl;
            const ext = this.getQueryTimerExtensionWebGL2();
            gl2.endQuery(ext.TIME_ELAPSED_EXT);
            return;
        }
        const ext = this.getQueryTimerExtensionWebGL1();
        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
    }
    async waitForQueryAndGetTime(query) {
        await (0, _tfjsCore.util).repeatedTry(()=>this.disposed || // while testing contexts are created / disposed
            // in rapid succession, so without this check we
            // may poll for the query timer indefinitely
            this.isQueryAvailable(query, (0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")));
        return this.getQueryTime(query, (0, _tfjsCore.env)().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
    }
    getQueryTime(query, queryTimerVersion) {
        if (queryTimerVersion === 0) return null;
        if (queryTimerVersion === 2) {
            const gl2 = this.gl;
            const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
            // Return milliseconds.
            return timeElapsedNanos / 1000000;
        } else {
            const ext = this.getQueryTimerExtensionWebGL1();
            const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
            // Return milliseconds.
            return timeElapsedNanos / 1000000;
        }
    }
    isQueryAvailable(query, queryTimerVersion) {
        if (queryTimerVersion === 0) return true;
        if (queryTimerVersion === 2) {
            const gl2 = this.gl;
            const ext = this.getQueryTimerExtensionWebGL2();
            const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
            if (this.disjoint == null) this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
            return available && !this.disjoint;
        } else {
            const ext = this.getQueryTimerExtensionWebGL1();
            const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
            if (this.disjoint == null) this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
            return available && !this.disjoint;
        }
    }
    pollFence(fenceContext) {
        return new Promise((resolve)=>{
            this.addItemToPoll(()=>fenceContext.isFencePassed(), ()=>resolve());
        });
    }
    pollItems() {
        // Find the last query that has finished.
        const index = linearSearchLastTrue(this.itemsToPoll.map((x)=>x.isDoneFn));
        for(let i = 0; i <= index; ++i){
            const { resolveFn } = this.itemsToPoll[i];
            resolveFn();
        }
        this.itemsToPoll = this.itemsToPoll.slice(index + 1);
    }
    addItemToPoll(isDoneFn, resolveFn) {
        this.itemsToPoll.push({
            isDoneFn,
            resolveFn
        });
        if (this.itemsToPoll.length > 1) // We already have a running loop that polls.
        return;
        // Start a new loop that polls.
        let scheduleFn = undefined;
        if ("setTimeoutCustom" in (0, _tfjsCore.env)().platform) scheduleFn = (0, _tfjsCore.env)().platform.setTimeoutCustom.bind((0, _tfjsCore.env)().platform);
        (0, _tfjsCore.util).repeatedTry(()=>{
            this.pollItems();
            // End the loop if no more items to poll.
            return this.itemsToPoll.length === 0;
        }, ()=>0, null, scheduleFn);
    }
    bindTextureToFrameBuffer(texture) {
        this.throwIfDisposed();
        _webglUtil.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
        if (this.debug) _webglUtil.validateFramebuffer(this.gl);
    }
    unbindTextureToFrameBuffer() {
        if (this.outputTexture != null) {
            _webglUtil.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
            if (this.debug) _webglUtil.validateFramebuffer(this.gl);
        } else _webglUtil.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
    }
    downloadMatrixDriver(texture, downloadAndDecode) {
        this.bindTextureToFrameBuffer(texture);
        const result = downloadAndDecode();
        this.unbindTextureToFrameBuffer();
        return result;
    }
    setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {
        this.throwIfDisposed();
        const gl = this.gl;
        _webglUtil.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
        if (this.debug) _webglUtil.validateFramebuffer(gl);
        this.outputTexture = outputMatrixTextureMaybePacked;
        _webglUtil.callAndCheck(gl, ()=>gl.viewport(0, 0, width, height));
        _webglUtil.callAndCheck(gl, ()=>gl.scissor(0, 0, width, height));
    }
    setOutputMatrixWriteRegionDriver(x, y, width, height) {
        this.throwIfDisposed();
        _webglUtil.callAndCheck(this.gl, ()=>this.gl.scissor(x, y, width, height));
    }
    throwIfDisposed() {
        if (this.disposed) throw new Error("Attempted to use disposed GPGPUContext.");
    }
    throwIfNoProgram() {
        if (this.program == null) throw new Error("No GPU program is currently set.");
    }
}
function linearSearchLastTrue(arr) {
    let i = 0;
    for(; i < arr.length; ++i){
        const isDone = arr[i]();
        if (!isDone) break;
    }
    return i - 1;
}

},{"@tensorflow/tfjs-core":"21Ftl","./canvas_util":"1tNGf","./gpgpu_util":"ezSrp","./tex_util":"1oYRe","./webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ezSrp":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "createVertexShader", ()=>createVertexShader);
parcelHelpers.export(exports, "createVertexBuffer", ()=>createVertexBuffer);
parcelHelpers.export(exports, "createIndexBuffer", ()=>createIndexBuffer);
parcelHelpers.export(exports, "getInternalFormatForFloat32MatrixTexture", ()=>getInternalFormatForFloat32MatrixTexture);
parcelHelpers.export(exports, "createFloat32MatrixTexture", ()=>createFloat32MatrixTexture);
parcelHelpers.export(exports, "getInternalFormatForFloat16MatrixTexture", ()=>getInternalFormatForFloat16MatrixTexture);
parcelHelpers.export(exports, "createFloat16MatrixTexture", ()=>createFloat16MatrixTexture);
parcelHelpers.export(exports, "getInternalFormatForUnsignedBytesMatrixTexture", ()=>getInternalFormatForUnsignedBytesMatrixTexture);
parcelHelpers.export(exports, "createUnsignedBytesMatrixTexture", ()=>createUnsignedBytesMatrixTexture);
parcelHelpers.export(exports, "getInternalFormatForPackedMatrixTexture", ()=>getInternalFormatForPackedMatrixTexture);
parcelHelpers.export(exports, "createPackedMatrixTexture", ()=>createPackedMatrixTexture);
parcelHelpers.export(exports, "getInternalFormatForFloat16PackedMatrixTexture", ()=>getInternalFormatForFloat16PackedMatrixTexture);
parcelHelpers.export(exports, "createFloat16PackedMatrixTexture", ()=>createFloat16PackedMatrixTexture);
parcelHelpers.export(exports, "bindVertexProgramAttributeStreams", ()=>bindVertexProgramAttributeStreams);
parcelHelpers.export(exports, "uploadDenseMatrixToTexture", ()=>uploadDenseMatrixToTexture);
parcelHelpers.export(exports, "uploadPixelDataToTexture", ()=>uploadPixelDataToTexture);
parcelHelpers.export(exports, "createBufferFromOutputTexture", ()=>createBufferFromOutputTexture);
parcelHelpers.export(exports, "downloadFloat32MatrixFromBuffer", ()=>downloadFloat32MatrixFromBuffer);
parcelHelpers.export(exports, "downloadByteEncodedFloatMatrixFromOutputTexture", ()=>downloadByteEncodedFloatMatrixFromOutputTexture);
parcelHelpers.export(exports, "downloadPackedMatrixFromBuffer", ()=>downloadPackedMatrixFromBuffer);
parcelHelpers.export(exports, "downloadMatrixFromPackedOutputTexture", ()=>downloadMatrixFromPackedOutputTexture);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _glslVersion = require("./glsl_version");
var _texUtil = require("./tex_util");
var _webglUtil = require("./webgl_util");
function createVertexShader(gl) {
    const glsl = (0, _glslVersion.getGlslDifferences)();
    const vertexShaderSource = `${glsl.version}
    precision highp float;
    ${glsl.attribute} vec3 clipSpacePos;
    ${glsl.attribute} vec2 uv;
    ${glsl.varyingVs} vec2 resultUV;

    void main() {
      gl_Position = vec4(clipSpacePos, 1);
      resultUV = uv;
    }`;
    return _webglUtil.createVertexShader(gl, vertexShaderSource);
}
function createVertexBuffer(gl) {
    // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]
    const vertexArray = new Float32Array([
        -1,
        1,
        0,
        0,
        1,
        -1,
        -1,
        0,
        0,
        0,
        1,
        1,
        0,
        1,
        1,
        1,
        -1,
        0,
        1,
        0
    ]);
    return _webglUtil.createStaticVertexBuffer(gl, vertexArray);
}
function createIndexBuffer(gl) {
    // OpenGL (and WebGL) have "CCW == front" winding
    const triangleVertexIndices = new Uint16Array([
        0,
        1,
        2,
        2,
        1,
        3
    ]);
    return _webglUtil.createStaticIndexBuffer(gl, triangleVertexIndices);
}
function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
    _webglUtil.validateTextureSize(width, height);
    const texture = _webglUtil.createTexture(gl);
    const tex2d = gl.TEXTURE_2D;
    _webglUtil.callAndCheck(gl, ()=>gl.bindTexture(tex2d, texture));
    _webglUtil.callAndCheck(gl, ()=>gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));
    _webglUtil.callAndCheck(gl, ()=>gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));
    _webglUtil.callAndCheck(gl, ()=>gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));
    _webglUtil.callAndCheck(gl, ()=>gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));
    if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 1) _webglUtil.callAndCheck(gl, ()=>gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));
    else _webglUtil.callAndCheck(gl, ()=>gl.texStorage2D(tex2d, 1, internalFormat, width, height));
    _webglUtil.callAndCheck(gl, ()=>gl.bindTexture(gl.TEXTURE_2D, null));
    return {
        texture,
        texShape: [
            height,
            width
        ]
    };
}
function getInternalFormatForFloat32MatrixTexture(textureConfig) {
    return textureConfig.internalFormatFloat;
}
function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = _texUtil.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);
}
function getInternalFormatForFloat16MatrixTexture(textureConfig) {
    return textureConfig.internalFormatHalfFloat;
}
function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = _texUtil.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
}
function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {
    return textureConfig.downloadTextureFormat;
}
function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = _texUtil.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);
}
function getInternalFormatForPackedMatrixTexture(textureConfig) {
    return textureConfig.internalFormatPackedFloat;
}
function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = _texUtil.getPackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);
}
function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {
    return textureConfig.internalFormatPackedHalfFloat;
}
function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
    const [width, height] = _texUtil.getPackedMatrixTextureShapeWidthHeight(rows, columns);
    return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);
}
function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
    const posOffset = 0; // x is the first buffer element
    const uvOffset = 12; // uv comes after [x y z]
    const stride = 20; // xyz + uv, each entry is 4-byte float.
    _webglUtil.callAndCheck(gl, ()=>gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));
    const success = _webglUtil.bindVertexBufferToProgramAttribute(gl, program, "clipSpacePos", vertexBuffer, 3, stride, posOffset);
    return success && _webglUtil.bindVertexBufferToProgramAttribute(gl, program, "uv", vertexBuffer, 2, stride, uvOffset);
}
function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {
    _webglUtil.callAndCheck(gl, ()=>gl.bindTexture(gl.TEXTURE_2D, texture));
    let dataForUpload, texelDataType, internalFormat;
    if (data instanceof Uint8Array) {
        dataForUpload = new Uint8Array(width * height * 4);
        texelDataType = gl.UNSIGNED_BYTE;
        internalFormat = gl.RGBA;
    } else {
        dataForUpload = new Float32Array(width * height * 4);
        texelDataType = gl.FLOAT;
        internalFormat = textureConfig.internalFormatPackedFloat;
    }
    dataForUpload.set(data);
    if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) _webglUtil.callAndCheck(gl, ()=>gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));
    else _webglUtil.callAndCheck(gl, ()=>gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));
    _webglUtil.callAndCheck(gl, ()=>gl.bindTexture(gl.TEXTURE_2D, null));
}
function uploadPixelDataToTexture(gl, texture, pixels) {
    _webglUtil.callAndCheck(gl, ()=>gl.bindTexture(gl.TEXTURE_2D, texture));
    if (pixels.data instanceof Uint8Array) {
        if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) _webglUtil.callAndCheck(gl, ()=>gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
        else _webglUtil.callAndCheck(gl, ()=>gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
    } else if ((0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2) _webglUtil.callAndCheck(gl, ()=>gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
    else _webglUtil.callAndCheck(gl, ()=>gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
    _webglUtil.callAndCheck(gl, ()=>gl.bindTexture(gl.TEXTURE_2D, null));
}
function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {
    // Create and bind the buffer.
    const buffer = gl2.createBuffer();
    _webglUtil.callAndCheck(gl2, ()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));
    // Initialize the buffer to the size of the texture in bytes.
    const bytesPerFloat = 4;
    const valuesPerTexel = 4;
    const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
    _webglUtil.callAndCheck(gl2, ()=>gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));
    // Enqueue a command on the GPU command queue to copy of texture into the
    // buffer.
    _webglUtil.callAndCheck(gl2, ()=>gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));
    _webglUtil.callAndCheck(gl2, ()=>gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));
    return buffer;
}
function downloadFloat32MatrixFromBuffer(gl, buffer, size) {
    const gl2 = gl;
    const downloadTarget = new Float32Array(size);
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
    return downloadTarget;
}
function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
    const [w, h] = _texUtil.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
    const numChannels = 4;
    const downloadTarget = new Uint8Array(_texUtil.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
    _webglUtil.callAndCheck(gl, ()=>gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));
    // By wrapping the buffer in a Float32Array, we use native browser IEEE 754
    // decoding of the 4 bytes that back each 32 bit float.
    return new Float32Array(downloadTarget.buffer);
}
function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
    const gl2 = gl;
    const downloadTarget = new Float32Array(_texUtil.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer);
    gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
    gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
    return downloadTarget;
}
function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {
    const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
    _webglUtil.callAndCheck(gl, ()=>gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));
    return packedRGBA;
}

},{"@tensorflow/tfjs-core":"21Ftl","./glsl_version":"kG2vE","./tex_util":"1oYRe","./webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"e4M7v":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Import shared functionality from tfjs-backend-cpu without triggering
// side effects.
// tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "addImplCPU", ()=>addImplCPU);
parcelHelpers.export(exports, "bincountImplCPU", ()=>bincountImplCPU);
parcelHelpers.export(exports, "bincountReduceImplCPU", ()=>bincountReduceImplCPU);
parcelHelpers.export(exports, "bitwiseAndImplCPU", ()=>bitwiseAndImplCPU);
parcelHelpers.export(exports, "castImplCPU", ()=>castImplCPU);
parcelHelpers.export(exports, "ceilImplCPU", ()=>ceilImplCPU);
parcelHelpers.export(exports, "concatImplCPU", ()=>concatImplCPU);
parcelHelpers.export(exports, "equalImplCPU", ()=>equalImplCPU);
parcelHelpers.export(exports, "expImplCPU", ()=>expImplCPU);
parcelHelpers.export(exports, "expm1ImplCPU", ()=>expm1ImplCPU);
parcelHelpers.export(exports, "floorImplCPU", ()=>floorImplCPU);
parcelHelpers.export(exports, "gatherNdImplCPU", ()=>gatherNdImplCPU);
parcelHelpers.export(exports, "gatherV2ImplCPU", ()=>gatherV2ImplCPU);
parcelHelpers.export(exports, "greaterEqualImplCPU", ()=>greaterEqualImplCPU);
parcelHelpers.export(exports, "greaterImplCPU", ()=>greaterImplCPU);
parcelHelpers.export(exports, "lessEqualImplCPU", ()=>lessEqualImplCPU);
parcelHelpers.export(exports, "lessImplCPU", ()=>lessImplCPU);
parcelHelpers.export(exports, "linSpaceImplCPU", ()=>linSpaceImplCPU);
parcelHelpers.export(exports, "logImplCPU", ()=>logImplCPU);
parcelHelpers.export(exports, "maxImplCPU", ()=>maxImplCPU);
parcelHelpers.export(exports, "maximumImplCPU", ()=>maximumImplCPU);
parcelHelpers.export(exports, "minimumImplCPU", ()=>minimumImplCPU);
parcelHelpers.export(exports, "multiplyImplCPU", ()=>multiplyImplCPU);
parcelHelpers.export(exports, "negImplCPU", ()=>negImplCPU);
parcelHelpers.export(exports, "notEqualImplCPU", ()=>notEqualImplCPU);
parcelHelpers.export(exports, "prodImplCPU", ()=>prodImplCPU);
parcelHelpers.export(exports, "raggedGatherImplCPU", ()=>raggedGatherImplCPU);
parcelHelpers.export(exports, "raggedRangeImplCPU", ()=>raggedRangeImplCPU);
parcelHelpers.export(exports, "raggedTensorToTensorImplCPU", ()=>raggedTensorToTensorImplCPU);
parcelHelpers.export(exports, "scatterImplCPU", ()=>scatterImplCPU);
parcelHelpers.export(exports, "sigmoidImplCPU", ()=>sigmoidImplCPU);
parcelHelpers.export(exports, "simpleAbsImplCPU", ()=>simpleAbsImplCPU);
parcelHelpers.export(exports, "sliceImplCPU", ()=>sliceImplCPU);
parcelHelpers.export(exports, "sparseFillEmptyRowsImplCPU", ()=>sparseFillEmptyRowsImplCPU);
parcelHelpers.export(exports, "sparseReshapeImplCPU", ()=>sparseReshapeImplCPU);
parcelHelpers.export(exports, "sparseSegmentReductionImplCPU", ()=>sparseSegmentReductionImplCPU);
parcelHelpers.export(exports, "sqrtImplCPU", ()=>sqrtImplCPU);
parcelHelpers.export(exports, "staticRegexReplaceImplCPU", ()=>staticRegexReplaceImplCPU);
parcelHelpers.export(exports, "stridedSliceImplCPU", ()=>stridedSliceImplCPU);
parcelHelpers.export(exports, "stringNGramsImplCPU", ()=>stringNGramsImplCPU);
parcelHelpers.export(exports, "stringSplitImplCPU", ()=>stringSplitImplCPU);
parcelHelpers.export(exports, "stringToHashBucketFastImplCPU", ()=>stringToHashBucketFastImplCPU);
parcelHelpers.export(exports, "subImplCPU", ()=>subImplCPU);
parcelHelpers.export(exports, "rangeImplCPU", ()=>rangeImplCPU);
parcelHelpers.export(exports, "rsqrtImplCPU", ()=>rsqrtImplCPU);
parcelHelpers.export(exports, "tileImplCPU", ()=>tileImplCPU);
parcelHelpers.export(exports, "topKImplCPU", ()=>topKImplCPU);
parcelHelpers.export(exports, "transposeImplCPU", ()=>transposeImplCPU);
parcelHelpers.export(exports, "uniqueImplCPU", ()=>uniqueImplCPU);
var _shared = require("@tensorflow/tfjs-backend-cpu/dist/shared");
const { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, bitwiseAndImpl: bitwiseAndImplCPU, castImpl: castImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, raggedGatherImpl: raggedGatherImplCPU, raggedRangeImpl: raggedRangeImplCPU, raggedTensorToTensorImpl: raggedTensorToTensorImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, staticRegexReplaceImpl: staticRegexReplaceImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = _shared;

},{"@tensorflow/tfjs-backend-cpu/dist/shared":"YbUp7","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"YbUp7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // Shared functionality among backends.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "simpleAbsImpl", ()=>(0, _abs.simpleAbsImpl));
parcelHelpers.export(exports, "addImpl", ()=>(0, _add.addImpl));
parcelHelpers.export(exports, "bincountImpl", ()=>(0, _bincountImpl.bincountImpl));
parcelHelpers.export(exports, "bincountReduceImpl", ()=>(0, _bincountImpl.bincountReduceImpl));
parcelHelpers.export(exports, "bitwiseAndImpl", ()=>(0, _bitwiseAnd.bitwiseAndImpl));
parcelHelpers.export(exports, "castImpl", ()=>(0, _cast.castImpl));
parcelHelpers.export(exports, "ceilImpl", ()=>(0, _ceil.ceilImpl));
parcelHelpers.export(exports, "concatImpl", ()=>(0, _concatImpl.concatImpl));
parcelHelpers.export(exports, "equalImpl", ()=>(0, _equal.equalImpl));
parcelHelpers.export(exports, "expImpl", ()=>(0, _exp.expImpl));
parcelHelpers.export(exports, "expm1Impl", ()=>(0, _expm1.expm1Impl));
parcelHelpers.export(exports, "floorImpl", ()=>(0, _floor.floorImpl));
parcelHelpers.export(exports, "floorDivImpl", ()=>(0, _floorDiv.floorDivImpl));
parcelHelpers.export(exports, "gatherNdImpl", ()=>(0, _gatherNdImpl.gatherNdImpl));
parcelHelpers.export(exports, "gatherV2Impl", ()=>(0, _gatherV2Impl.gatherV2Impl));
parcelHelpers.export(exports, "greaterImpl", ()=>(0, _greater.greaterImpl));
parcelHelpers.export(exports, "greaterEqualImpl", ()=>(0, _greaterEqual.greaterEqualImpl));
parcelHelpers.export(exports, "lessImpl", ()=>(0, _less.lessImpl));
parcelHelpers.export(exports, "lessEqualImpl", ()=>(0, _lessEqual.lessEqualImpl));
parcelHelpers.export(exports, "linSpaceImpl", ()=>(0, _linSpaceImpl.linSpaceImpl));
parcelHelpers.export(exports, "logImpl", ()=>(0, _log.logImpl));
parcelHelpers.export(exports, "maxImpl", ()=>(0, _maxImpl.maxImpl));
parcelHelpers.export(exports, "maximumImpl", ()=>(0, _maximum.maximumImpl));
parcelHelpers.export(exports, "minimumImpl", ()=>(0, _minimum.minimumImpl));
parcelHelpers.export(exports, "multiplyImpl", ()=>(0, _multiply.multiplyImpl));
parcelHelpers.export(exports, "negImpl", ()=>(0, _neg.negImpl));
parcelHelpers.export(exports, "notEqualImpl", ()=>(0, _notEqual.notEqualImpl));
parcelHelpers.export(exports, "prodImpl", ()=>(0, _prod.prodImpl));
parcelHelpers.export(exports, "raggedGatherImpl", ()=>(0, _raggedGatherImpl.raggedGatherImpl));
parcelHelpers.export(exports, "raggedRangeImpl", ()=>(0, _raggedRangeImpl.raggedRangeImpl));
parcelHelpers.export(exports, "raggedTensorToTensorImpl", ()=>(0, _raggedTensorToTensorImpl.raggedTensorToTensorImpl));
parcelHelpers.export(exports, "rangeImpl", ()=>(0, _rangeImpl.rangeImpl));
parcelHelpers.export(exports, "rsqrtImpl", ()=>(0, _rsqrt.rsqrtImpl));
parcelHelpers.export(exports, "scatterImpl", ()=>(0, _scatterImpl.scatterImpl));
parcelHelpers.export(exports, "sigmoidImpl", ()=>(0, _sigmoid.sigmoidImpl));
parcelHelpers.export(exports, "sliceImpl", ()=>(0, _slice.sliceImpl));
parcelHelpers.export(exports, "sparseFillEmptyRowsImpl", ()=>(0, _sparseFillEmptyRowsImpl.sparseFillEmptyRowsImpl));
parcelHelpers.export(exports, "sparseReshapeImpl", ()=>(0, _sparseReshapeImpl.sparseReshapeImpl));
parcelHelpers.export(exports, "sparseSegmentReductionImpl", ()=>(0, _sparseSegmentReductionImpl.sparseSegmentReductionImpl));
parcelHelpers.export(exports, "sqrtImpl", ()=>(0, _sqrt.sqrtImpl));
parcelHelpers.export(exports, "squaredDifferenceImpl", ()=>(0, _squaredDifference.squaredDifferenceImpl));
parcelHelpers.export(exports, "staticRegexReplaceImpl", ()=>(0, _staticRegexReplace.staticRegexReplaceImpl));
parcelHelpers.export(exports, "stridedSliceImpl", ()=>(0, _stridedSliceImpl.stridedSliceImpl));
parcelHelpers.export(exports, "stringNGramsImpl", ()=>(0, _stringNGramsImpl.stringNGramsImpl));
parcelHelpers.export(exports, "stringSplitImpl", ()=>(0, _stringSplitImpl.stringSplitImpl));
parcelHelpers.export(exports, "stringToHashBucketFastImpl", ()=>(0, _stringToHashBucketFastImpl.stringToHashBucketFastImpl));
parcelHelpers.export(exports, "subImpl", ()=>(0, _sub.subImpl));
parcelHelpers.export(exports, "tileImpl", ()=>(0, _tileImpl.tileImpl));
parcelHelpers.export(exports, "topKImpl", ()=>(0, _topKImpl.topKImpl));
parcelHelpers.export(exports, "transposeImpl", ()=>(0, _transposeImpl.transposeImpl));
parcelHelpers.export(exports, "uniqueImpl", ()=>(0, _uniqueImpl.uniqueImpl));
var _abs = require("./kernels/Abs");
var _add = require("./kernels/Add");
var _bincountImpl = require("./kernels/Bincount_impl");
var _bitwiseAnd = require("./kernels/BitwiseAnd");
var _cast = require("./kernels/Cast");
var _ceil = require("./kernels/Ceil");
var _concatImpl = require("./kernels/Concat_impl");
var _equal = require("./kernels/Equal");
var _exp = require("./kernels/Exp");
var _expm1 = require("./kernels/Expm1");
var _floor = require("./kernels/Floor");
var _floorDiv = require("./kernels/FloorDiv");
var _gatherNdImpl = require("./kernels/GatherNd_Impl");
var _gatherV2Impl = require("./kernels/GatherV2_impl");
var _greater = require("./kernels/Greater");
var _greaterEqual = require("./kernels/GreaterEqual");
var _less = require("./kernels/Less");
var _lessEqual = require("./kernels/LessEqual");
var _linSpaceImpl = require("./kernels/LinSpace_impl");
var _log = require("./kernels/Log");
var _maxImpl = require("./kernels/Max_impl");
var _maximum = require("./kernels/Maximum");
var _minimum = require("./kernels/Minimum");
var _multiply = require("./kernels/Multiply");
var _neg = require("./kernels/Neg");
var _notEqual = require("./kernels/NotEqual");
var _prod = require("./kernels/Prod");
var _raggedGatherImpl = require("./kernels/RaggedGather_impl");
var _raggedRangeImpl = require("./kernels/RaggedRange_impl");
var _raggedTensorToTensorImpl = require("./kernels/RaggedTensorToTensor_impl");
var _rangeImpl = require("./kernels/Range_impl");
var _rsqrt = require("./kernels/Rsqrt");
var _scatterImpl = require("./kernels/Scatter_impl");
var _sigmoid = require("./kernels/Sigmoid");
var _slice = require("./kernels/Slice");
var _sparseFillEmptyRowsImpl = require("./kernels/SparseFillEmptyRows_impl");
var _sparseReshapeImpl = require("./kernels/SparseReshape_impl");
var _sparseSegmentReductionImpl = require("./kernels/SparseSegmentReduction_impl");
var _sqrt = require("./kernels/Sqrt");
var _squaredDifference = require("./kernels/SquaredDifference");
var _staticRegexReplace = require("./kernels/StaticRegexReplace");
var _stridedSliceImpl = require("./kernels/StridedSlice_impl");
var _stringNGramsImpl = require("./kernels/StringNGrams_impl");
var _stringSplitImpl = require("./kernels/StringSplit_impl");
var _stringToHashBucketFastImpl = require("./kernels/StringToHashBucketFast_impl");
var _sub = require("./kernels/Sub");
var _tileImpl = require("./kernels/Tile_impl");
var _topKImpl = require("./kernels/TopK_impl");
var _transposeImpl = require("./kernels/Transpose_impl");
var _uniqueImpl = require("./kernels/Unique_impl");

},{"./kernels/Abs":"2vVaE","./kernels/Add":"dippY","./kernels/Bincount_impl":"4EF2S","./kernels/BitwiseAnd":"lFHUr","./kernels/Cast":"fAtIw","./kernels/Ceil":"dPS5d","./kernels/Concat_impl":"50NO7","./kernels/Equal":"jK2O7","./kernels/Exp":"6eexr","./kernels/Expm1":"pXtet","./kernels/Floor":"ftFzk","./kernels/FloorDiv":"kMZnS","./kernels/GatherNd_Impl":"klZow","./kernels/GatherV2_impl":"5y26k","./kernels/Greater":"94zPb","./kernels/GreaterEqual":"a9aqR","./kernels/Less":"anwWy","./kernels/LessEqual":"nFlBF","./kernels/LinSpace_impl":"4tRcP","./kernels/Log":"dNlq0","./kernels/Max_impl":"6BNe1","./kernels/Maximum":"73P1X","./kernels/Minimum":"2gO4x","./kernels/Multiply":"FrRi2","./kernels/Neg":"4sfM4","./kernels/NotEqual":"i62sj","./kernels/Prod":"8NwGT","./kernels/RaggedGather_impl":"59HGn","./kernels/RaggedRange_impl":"l2R3s","./kernels/RaggedTensorToTensor_impl":"jDwSG","./kernels/Range_impl":"gJNJp","./kernels/Rsqrt":"gcVCI","./kernels/Scatter_impl":"kMt6f","./kernels/Sigmoid":"1a9v3","./kernels/Slice":"9L9GN","./kernels/SparseFillEmptyRows_impl":"72f9e","./kernels/SparseReshape_impl":"ecdf6","./kernels/SparseSegmentReduction_impl":"ie2pQ","./kernels/Sqrt":"cnnoj","./kernels/SquaredDifference":"77Ql7","./kernels/StaticRegexReplace":"9G2f2","./kernels/StridedSlice_impl":"kCJo9","./kernels/StringNGrams_impl":"3rAXp","./kernels/StringSplit_impl":"8xmpZ","./kernels/StringToHashBucketFast_impl":"6jwLv","./kernels/Sub":"4DByI","./kernels/Tile_impl":"7g2re","./kernels/TopK_impl":"dl1sf","./kernels/Transpose_impl":"2fFZe","./kernels/Unique_impl":"gLexL","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2vVaE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "simpleAbsImpl", ()=>simpleAbsImpl);
parcelHelpers.export(exports, "abs", ()=>abs);
parcelHelpers.export(exports, "absConfig", ()=>absConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function simpleAbsImpl(vals) {
    const resultValues = new Float32Array(vals.length);
    for(let i = 0; i < vals.length; ++i)resultValues[i] = Math.abs(vals[i]);
    return resultValues;
}
const abs = (args)=>{
    const { x } = args.inputs;
    const cpuBackend = args.backend;
    (0, _cpuUtil.assertNotComplex)(x, "abs");
    let resultValues = new Float32Array((0, _tfjsCore.util).sizeFromShape(x.shape));
    const values = cpuBackend.data.get(x.dataId).values;
    resultValues = simpleAbsImpl(values);
    return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);
};
const absConfig = {
    kernelName: (0, _tfjsCore.Abs),
    backendName: "cpu",
    kernelFunc: abs
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1l037":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "assertNotComplex", ()=>assertNotComplex);
var _tfjsCore = require("@tensorflow/tfjs-core");
function assertNotComplex(tensor, opName) {
    if (!Array.isArray(tensor)) tensor = [
        tensor
    ];
    tensor.forEach((t)=>{
        if (t != null) (0, _tfjsCore.util).assert(t.dtype !== "complex64", ()=>`${opName} does not support complex64 tensors in the CPU backend.`);
    });
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dippY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "addImpl", ()=>addImpl);
parcelHelpers.export(exports, "addComplexImpl", ()=>addComplexImpl);
parcelHelpers.export(exports, "add", ()=>add);
parcelHelpers.export(exports, "addConfig", ()=>addConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const addImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a + b);
const addComplexImpl = (0, _binaryUtils.createComplexBinaryKernelImpl)((aReal, aImag, bReal, bImag)=>{
    return {
        real: aReal + bReal,
        imag: aImag + bImag
    };
});
const add = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Add), addImpl, addComplexImpl);
const addConfig = {
    kernelName: (0, _tfjsCore.Add),
    backendName: "cpu",
    kernelFunc: add
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7WPlP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Template that creates implementation for binary ops. Supports broadcast.
 */ parcelHelpers.export(exports, "createSimpleBinaryKernelImpl", ()=>createSimpleBinaryKernelImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function createSimpleBinaryKernelImpl(op) {
    return (aShape, bShape, aVals, bVals, dtype)=>{
        const newShape = (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(aShape, bShape);
        const resultRank = newShape.length;
        const resultStrides = (0, _tfjsCore.util).computeStrides(newShape);
        const resultSize = (0, _tfjsCore.util).sizeFromShape(newShape);
        const result = (0, _tfjsCore.util).getTypedArrayFromDType(dtype, resultSize);
        const aRank = aShape.length;
        const bRank = bShape.length;
        const aStrides = (0, _tfjsCore.util).computeStrides(aShape);
        const bStrides = (0, _tfjsCore.util).computeStrides(bShape);
        const aBroadcastDims = (0, _tfjsCore.backend_util).getBroadcastDims(aShape, newShape);
        const bBroadcastDims = (0, _tfjsCore.backend_util).getBroadcastDims(bShape, newShape);
        if (aBroadcastDims.length + bBroadcastDims.length === 0) for(let i = 0; i < result.length; ++i)result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);
        else for(let i = 0; i < result.length; ++i){
            const loc = (0, _tfjsCore.util).indexToLoc(i, resultRank, resultStrides);
            const aLoc = loc.slice(-aRank);
            aBroadcastDims.forEach((d)=>aLoc[d] = 0);
            const aIndex = (0, _tfjsCore.util).locToIndex(aLoc, aRank, aStrides);
            const bLoc = loc.slice(-bRank);
            bBroadcastDims.forEach((d)=>bLoc[d] = 0);
            const bIndex = (0, _tfjsCore.util).locToIndex(bLoc, bRank, bStrides);
            result[i] = op(aVals[aIndex], bVals[bIndex]);
        }
        return [
            result,
            newShape
        ];
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dUQGx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Template that creates a `KernelFunc` for binary ops.
 * @param name Kernel name.
 * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel.
 * @param binaryKernelComplexImpl Optional. If exists, represents a
 *     `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype
 *     is `complex64`.
 * @param dtype Optional. If set, the result has this dtype. Otherwise, the
 *     result has the same dtype as the first input. This is mainly used in
 *     comparison kernels, such as Equal, Less, Greater, etc.
 */ parcelHelpers.export(exports, "binaryKernelFunc", ()=>binaryKernelFunc);
/**
 * Template that creates the complex type implementation for binary ops.
 * Supports broadcast.
 */ parcelHelpers.export(exports, "createComplexBinaryKernelImpl", ()=>createComplexBinaryKernelImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _cast = require("../kernels/Cast");
var _complex = require("../kernels/Complex");
function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {
    if (complexImpl == null) return ({ inputs, backend })=>{
        const { a, b } = inputs;
        const cpuBackend = backend;
        (0, _cpuUtil.assertNotComplex)([
            a,
            b
        ], name);
        const aVals = cpuBackend.data.get(a.dataId).values;
        const bVals = cpuBackend.data.get(b.dataId).values;
        const decodedAVals = a.dtype === "string" ? // tslint:disable-next-line: no-any
        (0, _tfjsCore.backend_util).fromUint8ToStringArray(aVals) : aVals;
        const decodedBVals = a.dtype === "string" ? // tslint:disable-next-line: no-any
        (0, _tfjsCore.backend_util).fromUint8ToStringArray(bVals) : bVals;
        const $dtype = dtype || a.dtype;
        const [resultData, resultShape] = simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);
        return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
    };
    return ({ inputs, backend })=>{
        const { a, b } = inputs;
        const cpuBackend = backend;
        if (a.dtype === "complex64" || b.dtype === "complex64") {
            const $aComplex = (0, _cast.cast)({
                inputs: {
                    x: a
                },
                backend: cpuBackend,
                attrs: {
                    dtype: "complex64"
                }
            });
            const $aComplexVals = cpuBackend.data.get($aComplex.dataId);
            const aReal = $aComplexVals.complexTensorInfos.real;
            const aImag = $aComplexVals.complexTensorInfos.imag;
            const aRealVals = cpuBackend.data.get(aReal.dataId).values;
            const aImagVals = cpuBackend.data.get(aImag.dataId).values;
            const $bComplex = (0, _cast.cast)({
                inputs: {
                    x: b
                },
                backend: cpuBackend,
                attrs: {
                    dtype: "complex64"
                }
            });
            const $bComplexVals = cpuBackend.data.get($bComplex.dataId);
            const bReal = $bComplexVals.complexTensorInfos.real;
            const bImag = $bComplexVals.complexTensorInfos.imag;
            const bRealVals = cpuBackend.data.get(bReal.dataId).values;
            const bImagVals = cpuBackend.data.get(bImag.dataId).values;
            const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);
            const resultReal = cpuBackend.makeTensorInfo(resultShape, "float32", resultRealData);
            const resultImag = cpuBackend.makeTensorInfo(resultShape, "float32", resultImagData);
            const result = (0, _complex.complex)({
                inputs: {
                    real: resultReal,
                    imag: resultImag
                },
                backend: cpuBackend
            });
            cpuBackend.disposeIntermediateTensorInfo($aComplex);
            cpuBackend.disposeIntermediateTensorInfo($bComplex);
            cpuBackend.disposeIntermediateTensorInfo(resultReal);
            cpuBackend.disposeIntermediateTensorInfo(resultImag);
            return result;
        } else {
            const aVals = cpuBackend.data.get(a.dataId).values;
            const bVals = cpuBackend.data.get(b.dataId).values;
            const $dtype = dtype || a.dtype;
            const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);
            return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
        }
    };
}
function createComplexBinaryKernelImpl(op) {
    return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals)=>{
        const resultShape = (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(aShape, bShape);
        const resultSize = (0, _tfjsCore.util).sizeFromShape(resultShape);
        const resultRank = resultShape.length;
        const resultStrides = (0, _tfjsCore.util).computeStrides(resultShape);
        const resultRealVals = (0, _tfjsCore.util).getTypedArrayFromDType("float32", resultSize);
        const resultImagVals = (0, _tfjsCore.util).getTypedArrayFromDType("float32", resultSize);
        const aBroadcastDims = (0, _tfjsCore.backend_util).getBroadcastDims(aShape, resultShape);
        const bBroadcastDims = (0, _tfjsCore.backend_util).getBroadcastDims(bShape, resultShape);
        const aVals = (0, _tfjsCore.backend_util).mergeRealAndImagArrays(aRealVals, aImagVals);
        const bVals = (0, _tfjsCore.backend_util).mergeRealAndImagArrays(bRealVals, bImagVals);
        const aRank = aShape.length;
        const aStrides = (0, _tfjsCore.util).computeStrides(aShape);
        const bRank = bShape.length;
        const bStrides = (0, _tfjsCore.util).computeStrides(bShape);
        if (aBroadcastDims.length + bBroadcastDims.length === 0) for(let i = 0; i < resultRealVals.length; i++){
            const aIdx = i % aVals.length;
            const bIdx = i % bVals.length;
            const result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
            resultRealVals[i] = result.real;
            resultImagVals[i] = result.imag;
        }
        else for(let i = 0; i < resultRealVals.length; i++){
            const loc = (0, _tfjsCore.util).indexToLoc(i, resultRank, resultStrides);
            const aLoc = loc.slice(-aRank);
            aBroadcastDims.forEach((d)=>aLoc[d] = 0);
            const aIndex = (0, _tfjsCore.util).locToIndex(aLoc, aRank, aStrides);
            const bLoc = loc.slice(-bRank);
            bBroadcastDims.forEach((d)=>bLoc[d] = 0);
            const bIndex = (0, _tfjsCore.util).locToIndex(bLoc, bRank, bStrides);
            const opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
            resultRealVals[i] = opResult.real;
            resultImagVals[i] = opResult.imag;
        }
        return [
            resultRealVals,
            resultImagVals,
            resultShape
        ];
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../kernels/Cast":"fAtIw","../kernels/Complex":"3DPmh","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fAtIw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "castImpl", ()=>castImpl);
parcelHelpers.export(exports, "cast", ()=>cast);
parcelHelpers.export(exports, "castConfig", ()=>castConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _zerosImpl = require("../utils/zeros_impl");
var _complex = require("./Complex");
var _identity = require("./Identity");
var _real = require("./Real");
function castImpl(values, shape, inputType, dtype) {
    if (dtype === "int32") {
        const resultValues = Int32Array.from(values);
        return [
            shape,
            "int32",
            resultValues
        ];
    }
    if (dtype === "bool") {
        // This is essentially the result of notEqual(x, 0). We avoid using
        // kernel notEqual to avoid circular dependency, i.e. binary_utils ->
        // cast -> notEqual -> binary_utils.
        const zero = (0, _tfjsCore.util).toTypedArray([
            0
        ], inputType);
        const [resultData, resultShape] = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a !== b ? 1 : 0)(shape, [], values, zero, "bool");
        return [
            resultShape,
            "bool",
            resultData
        ];
    }
    throw new Error(`Error in Cast: failed to cast ${inputType} to ${dtype}`);
}
function cast(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { dtype } = attrs;
    // Casting to complex64.
    if (dtype === "complex64") {
        if (x.dtype === "complex64") return (0, _identity.identity)({
            inputs: {
                x
            },
            backend
        });
        const zerosTensorInfo = (0, _zerosImpl.zeros)(backend, x.shape, x.dtype);
        const floatX = cast({
            inputs: {
                x
            },
            backend,
            attrs: {
                dtype: "float32"
            }
        });
        const result = (0, _complex.complex)({
            inputs: {
                real: floatX,
                imag: zerosTensorInfo
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(zerosTensorInfo);
        backend.disposeIntermediateTensorInfo(floatX);
        return result;
    }
    // Casting from complex64
    if (x.dtype === "complex64") {
        const realPart = (0, _real.real)({
            inputs: {
                input: x
            },
            backend
        });
        const result = cast({
            inputs: {
                x: realPart
            },
            backend,
            attrs: {
                dtype
            }
        });
        backend.disposeIntermediateTensorInfo(realPart);
        return result;
    }
    if (!(0, _tfjsCore.util).hasEncodingLoss(x.dtype, dtype)) {
        // We don't change the underlying data, since we cast to higher
        // precision.
        const result = (0, _identity.identity)({
            inputs: {
                x
            },
            backend
        });
        return {
            dataId: result.dataId,
            shape: result.shape,
            dtype
        };
    }
    const values = backend.data.get(x.dataId).values;
    const [resultShape, resultType, resultData] = castImpl(values, x.shape, x.dtype, dtype);
    return backend.makeTensorInfo(resultShape, resultType, resultData);
}
const castConfig = {
    kernelName: (0, _tfjsCore.Cast),
    backendName: "cpu",
    kernelFunc: cast
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/zeros_impl":"6us7T","./Complex":"3DPmh","./Identity":"btREJ","./Real":"eZ7aJ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6us7T":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Generates a tensorInfo with all zeros value.
 * @param backend cpu backend.
 * @param shape Shape for the zeros tensor.
 * @param dtype Optional. If set, the result has this dtype.
 */ parcelHelpers.export(exports, "zeros", ()=>zeros);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _complex = require("../kernels/Complex");
function zeros(backend, shape, dtype = "float32") {
    if (dtype === "complex64") {
        const real = zeros(backend, shape, "float32");
        const imag = zeros(backend, shape, "float32");
        return (0, _complex.complex)({
            inputs: {
                real,
                imag
            },
            backend
        });
    }
    const values = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape(shape), dtype);
    return backend.makeTensorInfo(shape, dtype, values);
}

},{"@tensorflow/tfjs-core":"21Ftl","../kernels/Complex":"3DPmh","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3DPmh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "complex", ()=>complex);
parcelHelpers.export(exports, "complexConfig", ()=>complexConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function complex(args) {
    const { inputs, backend } = args;
    const { real, imag } = inputs;
    const realVals = backend.data.get(real.dataId).values;
    const imagVals = backend.data.get(imag.dataId).values;
    const complexInfo = backend.makeTensorInfo(real.shape, "complex64");
    const complex = backend.data.get(complexInfo.dataId);
    // The complex tensor owns the underlying real and imag tensorInfos, only the
    // complex tensor tracks refCount, when complexData is disposed the
    // underlying tensorData will be disposed.
    complex.complexTensorInfos = {
        real: backend.makeTensorInfo(real.shape, "float32", realVals),
        imag: backend.makeTensorInfo(imag.shape, "float32", imagVals)
    };
    return complexInfo;
}
const complexConfig = {
    kernelName: (0, _tfjsCore.Complex),
    backendName: "cpu",
    kernelFunc: complex
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"btREJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "identity", ()=>identity);
parcelHelpers.export(exports, "identityConfig", ()=>identityConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function identity(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    backend.incRef(x.dataId);
    return {
        dataId: x.dataId,
        shape: x.shape,
        dtype: x.dtype
    };
}
const identityConfig = {
    kernelName: (0, _tfjsCore.Identity),
    backendName: "cpu",
    kernelFunc: identity
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eZ7aJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "real", ()=>real);
parcelHelpers.export(exports, "realConfig", ()=>realConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function real(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    const real = backend.data.get(input.dataId).complexTensorInfos.real;
    const realVal = backend.data.get(real.dataId).values;
    // When complex tensor is disposed, its underlying parts will be disposed too.
    // Make new tensor out of the real value of the complex. This makes sure the
    // value is still accessible even if complex tensor is disposed.
    return backend.makeTensorInfo(real.shape, real.dtype, realVal);
}
const realConfig = {
    kernelName: (0, _tfjsCore.Real),
    backendName: "cpu",
    kernelFunc: real
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4EF2S":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "bincountImpl", ()=>bincountImpl);
parcelHelpers.export(exports, "bincountReduceImpl", ()=>bincountReduceImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
    const weightsSize = (0, _tfjsCore.util).sizeFromShape(weightsShape);
    const outVals = (0, _tfjsCore.util).makeZerosTypedArray(size, weightsDtype);
    for(let i = 0; i < xVals.length; i++){
        const value = xVals[i];
        if (value < 0) throw new Error("Input x must be non-negative!");
        if (value >= size) continue;
        if (weightsSize > 0) outVals[value] += weightsVals[i];
        else outVals[value] += 1;
    }
    return outVals;
}
function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
    const numRows = xBuf.shape[0];
    const numCols = xBuf.shape[1];
    const outBuf = (0, _tfjsCore.buffer)([
        numRows,
        size
    ], weightsBuf.dtype);
    for(let i = 0; i < numRows; i++)for(let j = 0; j < numCols; j++){
        const value = xBuf.get(i, j);
        if (value < 0) throw new Error("Input x must be non-negative!");
        if (value >= size) continue;
        if (binaryOutput) outBuf.set(1, i, value);
        else if (weightsBuf.size > 0) outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);
        else outBuf.set(outBuf.get(i, value) + 1, i, value);
    }
    return outBuf;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lFHUr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "bitwiseAndImpl", ()=>bitwiseAndImpl);
parcelHelpers.export(exports, "bitwiseAnd", ()=>bitwiseAnd);
parcelHelpers.export(exports, "bitwiseAndConfig", ()=>bitwiseAndConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const bitwiseAndImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a & b);
const bitwiseAnd = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.BitwiseAnd), bitwiseAndImpl);
const bitwiseAndConfig = {
    kernelName: (0, _tfjsCore.BitwiseAnd),
    backendName: "cpu",
    kernelFunc: bitwiseAnd
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dPS5d":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ceilImpl", ()=>ceilImpl);
parcelHelpers.export(exports, "ceil", ()=>ceil);
parcelHelpers.export(exports, "ceilConfig", ()=>ceilConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const ceilImpl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>Math.ceil(xi));
const ceil = (0, _unaryUtils.unaryKernelFuncFromImpl)((0, _tfjsCore.Ceil), ceilImpl);
const ceilConfig = {
    kernelName: (0, _tfjsCore.Ceil),
    backendName: "cpu",
    kernelFunc: ceil
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jdtAN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Template that creates implementation for unary op.
 */ parcelHelpers.export(exports, "createSimpleUnaryImpl", ()=>createSimpleUnaryImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function createSimpleUnaryImpl(op) {
    return (values, dtype, attrs)=>{
        const newValues = (0, _tfjsCore.util).getArrayFromDType(dtype, values.length);
        for(let i = 0; i < values.length; ++i)newValues[i] = op(values[i], attrs);
        return newValues;
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5johK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Template that creates a `KernelFunc` for unary ops.
 * @param name Kernel name.
 * @param op A `SimpleUnaryOperation` for the kernel.
 * @param dtype Optional. If set, the result has this dtype. Otherwise, the
 *     result has the same dtype as the input. This is mainly used in certain
 *     kernels that return bool type, such as isFinite, isInf, etc.
 */ parcelHelpers.export(exports, "unaryKernelFunc", ()=>unaryKernelFunc);
/**
 * Template that creates a `KernelFunc` for unary ops from the given
 * `SimpleUnaryImpl`..
 * @param name Kernel name.
 * @param unaryImpl A `SimpleUnaryImpl` that implements the op.
 * @param dtype Optional. If set, the result has this dtype. Otherwise, the
 *     result has the same dtype as the input. This is mainly used in certain
 *     kernels that return bool type, such as isFinite, isInf, etc.
 */ parcelHelpers.export(exports, "unaryKernelFuncFromImpl", ()=>unaryKernelFuncFromImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _unaryImpl = require("./unary_impl");
function unaryKernelFunc(name, op, dtype) {
    const impl = (0, _unaryImpl.createSimpleUnaryImpl)(op);
    return unaryKernelFuncFromImpl(name, impl, dtype);
}
function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {
    return ({ inputs, attrs, backend })=>{
        const { x } = inputs;
        (0, _cpuUtil.assertNotComplex)(x, name);
        const cpuBackend = backend;
        const values = cpuBackend.data.get(x.dataId).values;
        let decoded;
        if (x.dtype === "string") {
            if (!Array.isArray(values)) throw new Error("String tensor's value was not an instance of Array");
            decoded = (0, _tfjsCore.backend_util).fromUint8ToStringArray(values);
        } else decoded = values;
        const $dtype = dtype || x.dtype;
        const newValues = unaryImpl(decoded, $dtype, attrs);
        return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./unary_impl":"jdtAN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"50NO7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concatImpl", ()=>concatImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function concatImpl(inputs, outShape, dtype, simplyConcat) {
    const outVals = (0, _tfjsCore.util).getArrayFromDType(dtype, (0, _tfjsCore.util).sizeFromShape(outShape));
    if (simplyConcat && dtype !== "string") {
        // Use built-in TypedArray.set() method for speed.
        let offset = 0;
        inputs.forEach((input)=>{
            const size = (0, _tfjsCore.util).sizeFromShape(input.shape);
            outVals.set(input.vals, offset);
            offset += size;
        });
    } else {
        let colOffset = 0;
        inputs.forEach((input)=>{
            const decodedData = dtype === "string" ? (0, _tfjsCore.backend_util).fromUint8ToStringArray(input.vals) : input.vals;
            let tIdx = 0;
            for(let row = 0; row < input.shape[0]; ++row){
                const resIdx = row * outShape[1] + colOffset;
                for(let col = 0; col < input.shape[1]; ++col)outVals[resIdx + col] = decodedData[tIdx++];
            }
            colOffset += input.shape[1];
        });
    }
    return outVals;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jK2O7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "equalImpl", ()=>equalImpl);
parcelHelpers.export(exports, "equal", ()=>equal);
parcelHelpers.export(exports, "equalConfig", ()=>equalConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const equalImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a === b ? 1 : 0);
const equal = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Equal), equalImpl, null, "bool");
const equalConfig = {
    kernelName: (0, _tfjsCore.Equal),
    backendName: "cpu",
    kernelFunc: equal
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6eexr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "expImpl", ()=>expImpl);
parcelHelpers.export(exports, "exp", ()=>exp);
parcelHelpers.export(exports, "expConfig", ()=>expConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const expImpl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>Math.exp(xi));
const exp = (0, _unaryUtils.unaryKernelFuncFromImpl)((0, _tfjsCore.Exp), expImpl, "float32");
const expConfig = {
    kernelName: (0, _tfjsCore.Exp),
    backendName: "cpu",
    kernelFunc: exp
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"pXtet":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "expm1Impl", ()=>expm1Impl);
parcelHelpers.export(exports, "expm1", ()=>expm1);
parcelHelpers.export(exports, "expm1Config", ()=>expm1Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const expm1Impl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>Math.expm1(xi));
const expm1 = (0, _unaryUtils.unaryKernelFuncFromImpl)((0, _tfjsCore.Expm1), expm1Impl);
const expm1Config = {
    kernelName: (0, _tfjsCore.Expm1),
    backendName: "cpu",
    kernelFunc: expm1
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ftFzk":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "floorImpl", ()=>floorImpl);
parcelHelpers.export(exports, "floor", ()=>floor);
parcelHelpers.export(exports, "floorConfig", ()=>floorConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const floorImpl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>Math.floor(xi));
const floor = (0, _unaryUtils.unaryKernelFuncFromImpl)((0, _tfjsCore.Floor), floorImpl);
const floorConfig = {
    kernelName: (0, _tfjsCore.Floor),
    backendName: "cpu",
    kernelFunc: floor
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kMZnS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "floorDivImpl", ()=>floorDivImpl);
parcelHelpers.export(exports, "floorDiv", ()=>floorDiv);
parcelHelpers.export(exports, "floorDivConfig", ()=>floorDivConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const floorDivImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>Math.floor(a / b));
const floorDiv = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.FloorDiv), floorDivImpl, null, "int32");
const floorDivConfig = {
    kernelName: (0, _tfjsCore.FloorDiv),
    backendName: "cpu",
    kernelFunc: floorDiv
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"klZow":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gatherNdImpl", ()=>gatherNdImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {
    const outBuf = (0, _tfjsCore.buffer)([
        numSlices,
        sliceSize
    ], dtype);
    for(let i = 0; i < numSlices; i++){
        const index = [];
        let flattenIndex = 0;
        for(let j = 0; j < sliceRank; j++){
            const dim = indicesData[i * sliceRank + j];
            flattenIndex += dim * strides[j];
            index.push(dim);
        }
        if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);
        for(let k = 0; k < sliceSize; k++)outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
    }
    return outBuf;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5y26k":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gatherV2Impl", ()=>gatherV2Impl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
    const outBuf = (0, _tfjsCore.buffer)(flattenOutputShape, xBuf.dtype);
    for(let i = 0; i < outBuf.size; ++i){
        const newLoc = outBuf.indexToLoc(i);
        const originalLoc = newLoc.slice();
        const batchIdx = originalLoc[0];
        const indicesIdx = originalLoc[2];
        const indicesIndex = indicesBuf.locToIndex([
            batchIdx,
            indicesIdx
        ]);
        originalLoc[2] = indicesBuf.values[indicesIndex];
        const originalIndex = xBuf.locToIndex(originalLoc);
        if (0 <= originalIndex && originalIndex < xBuf.values.length) outBuf.values[i] = xBuf.values[originalIndex];
         // Else, index is out of bounds, so leave the default zero val in outBuf.
    }
    return outBuf;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"94zPb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "greaterImpl", ()=>greaterImpl);
parcelHelpers.export(exports, "greater", ()=>greater);
parcelHelpers.export(exports, "greaterConfig", ()=>greaterConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const greaterImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a > b ? 1 : 0);
const greater = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Greater), greaterImpl, null, "bool");
const greaterConfig = {
    kernelName: (0, _tfjsCore.Greater),
    backendName: "cpu",
    kernelFunc: greater
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a9aqR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "greaterEqualImpl", ()=>greaterEqualImpl);
parcelHelpers.export(exports, "greaterEqual", ()=>greaterEqual);
parcelHelpers.export(exports, "greaterEqualConfig", ()=>greaterEqualConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const greaterEqualImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a >= b ? 1 : 0);
const greaterEqual = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.GreaterEqual), greaterEqualImpl, null, "bool");
const greaterEqualConfig = {
    kernelName: (0, _tfjsCore.GreaterEqual),
    backendName: "cpu",
    kernelFunc: greaterEqual
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"anwWy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "lessImpl", ()=>lessImpl);
parcelHelpers.export(exports, "less", ()=>less);
parcelHelpers.export(exports, "lessConfig", ()=>lessConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const lessImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a < b ? 1 : 0);
const less = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Less), lessImpl, null, "bool");
const lessConfig = {
    kernelName: (0, _tfjsCore.Less),
    backendName: "cpu",
    kernelFunc: less
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"nFlBF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "lessEqualImpl", ()=>lessEqualImpl);
parcelHelpers.export(exports, "lessEqual", ()=>lessEqual);
parcelHelpers.export(exports, "lessEqualConfig", ()=>lessEqualConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const lessEqualImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a <= b ? 1 : 0);
const lessEqual = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.LessEqual), lessEqualImpl, null, "bool");
const lessEqualConfig = {
    kernelName: (0, _tfjsCore.LessEqual),
    backendName: "cpu",
    kernelFunc: lessEqual
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4tRcP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "linSpaceImpl", ()=>linSpaceImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function linSpaceImpl(start, stop, num) {
    const step = (stop - start) / (num - 1);
    const values = (0, _tfjsCore.util).makeZerosTypedArray(num, "float32");
    values[0] = start;
    for(let i = 1; i < values.length; i++)values[i] = values[i - 1] + step;
    return values;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dNlq0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logImpl", ()=>logImpl);
parcelHelpers.export(exports, "log", ()=>log);
parcelHelpers.export(exports, "logConfig", ()=>logConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const logImpl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>Math.log(xi));
const log = (0, _unaryUtils.unaryKernelFuncFromImpl)((0, _tfjsCore.Log), logImpl);
const logConfig = {
    kernelName: (0, _tfjsCore.Log),
    backendName: "cpu",
    kernelFunc: log
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6BNe1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxImpl", ()=>maxImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function maxImpl(aVals, reduceSize, outShape, dtype) {
    const vals = (0, _tfjsCore.util).getTypedArrayFromDType(dtype, (0, _tfjsCore.util).sizeFromShape(outShape));
    for(let i = 0; i < vals.length; ++i){
        const offset = i * reduceSize;
        let max = aVals[offset];
        for(let j = 0; j < reduceSize; ++j){
            const value = aVals[offset + j];
            if (Number.isNaN(value) || value > max) max = value;
        }
        vals[i] = max;
    }
    return vals;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"73P1X":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maximumImpl", ()=>maximumImpl);
parcelHelpers.export(exports, "maximum", ()=>maximum);
parcelHelpers.export(exports, "maximumConfig", ()=>maximumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const maximumImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((aValue, bValue)=>Math.max(aValue, bValue));
const maximum = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Maximum), maximumImpl);
const maximumConfig = {
    kernelName: (0, _tfjsCore.Maximum),
    backendName: "cpu",
    kernelFunc: maximum
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2gO4x":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "minimumImpl", ()=>minimumImpl);
parcelHelpers.export(exports, "minimum", ()=>minimum);
parcelHelpers.export(exports, "minimumConfig", ()=>minimumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const minimumImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((aValue, bValue)=>Math.min(aValue, bValue));
const minimum = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Minimum), minimumImpl);
const minimumConfig = {
    kernelName: (0, _tfjsCore.Minimum),
    backendName: "cpu",
    kernelFunc: minimum
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"FrRi2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "multiplyImpl", ()=>multiplyImpl);
parcelHelpers.export(exports, "multiplyComplexImpl", ()=>multiplyComplexImpl);
parcelHelpers.export(exports, "multiply", ()=>multiply);
parcelHelpers.export(exports, "multiplyConfig", ()=>multiplyConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const multiplyImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((aValue, bValue)=>aValue * bValue);
const multiplyComplexImpl = (0, _binaryUtils.createComplexBinaryKernelImpl)((aReal, aImag, bReal, bImag)=>{
    return {
        real: aReal * bReal - aImag * bImag,
        imag: aReal * bImag + aImag * bReal
    };
});
const multiply = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Multiply), multiplyImpl, multiplyComplexImpl);
const multiplyConfig = {
    kernelName: (0, _tfjsCore.Multiply),
    backendName: "cpu",
    kernelFunc: multiply
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4sfM4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "negImpl", ()=>negImpl);
parcelHelpers.export(exports, "neg", ()=>neg);
parcelHelpers.export(exports, "negConfig", ()=>negConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _multiply = require("./Multiply");
function negImpl(xVals, xShape, xDtype) {
    const minusOne = (0, _tfjsCore.util).createScalarValue(-1, xDtype);
    return (0, _multiply.multiplyImpl)([], xShape, minusOne, xVals, xDtype);
}
function neg(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    (0, _cpuUtil.assertNotComplex)(x, "neg");
    const xVals = backend.data.get(x.dataId).values;
    const [res, newShape] = negImpl(xVals, x.shape, x.dtype);
    return backend.makeTensorInfo(newShape, x.dtype, res);
}
const negConfig = {
    kernelName: (0, _tfjsCore.Neg),
    backendName: "cpu",
    kernelFunc: neg
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Multiply":"FrRi2","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"i62sj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "notEqualImpl", ()=>notEqualImpl);
parcelHelpers.export(exports, "notEqual", ()=>notEqual);
parcelHelpers.export(exports, "notEqualConfig", ()=>notEqualConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const notEqualImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a !== b ? 1 : 0);
const notEqual = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.NotEqual), notEqualImpl, null, "bool");
const notEqualConfig = {
    kernelName: (0, _tfjsCore.NotEqual),
    backendName: "cpu",
    kernelFunc: notEqual
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8NwGT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "prodImpl", ()=>prodImpl);
parcelHelpers.export(exports, "prod", ()=>prod);
parcelHelpers.export(exports, "prodConfig", ()=>prodConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _transpose = require("./Transpose");
function prodImpl(xShape, xDtype, xVals, reductionAxes) {
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(xShape, reductionAxes);
    const outDtype = (0, _tfjsCore.upcastType)(xDtype, "int32");
    const outVals = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape(outShape), outDtype);
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    for(let i = 0; i < outVals.length; ++i){
        const offset = i * reduceSize;
        let prod = 1;
        for(let j = 0; j < reduceSize; ++j)prod *= xVals[offset + j];
        outVals[i] = prod;
    }
    return {
        outVals,
        outShape,
        outDtype
    };
}
function prod(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "prod");
    const xRank = x.shape.length;
    const axes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    const permutation = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    let reductionAxes = axes;
    let permutedX = x;
    const intermediateTensorInfos = [];
    if (permutation != null) {
        permutedX = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutation
            }
        });
        intermediateTensorInfos.push(permutedX);
        reductionAxes = (0, _tfjsCore.backend_util).getInnerMostAxes(reductionAxes.length, xRank);
    }
    const xVals = backend.data.get(permutedX.dataId).values;
    const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);
    let resultShape = outShape;
    if (keepDims) resultShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(outShape, axes);
    intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return backend.makeTensorInfo(resultShape, outDtype, outVals);
}
const prodConfig = {
    kernelName: (0, _tfjsCore.Prod),
    backendName: "cpu",
    kernelFunc: prod
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"61ywv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transpose", ()=>transpose);
parcelHelpers.export(exports, "transposeConfig", ()=>transposeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _transposeImpl = require("./Transpose_impl");
function transpose(args) {
    const { inputs, attrs, backend } = args;
    const { x } = inputs;
    const { perm } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "transpose");
    const xRank = x.shape.length;
    const newShape = new Array(xRank);
    for(let i = 0; i < newShape.length; i++)newShape[i] = x.shape[perm[i]];
    const values = backend.data.get(x.dataId).values;
    const result = (0, _transposeImpl.transposeImpl)(values, x.shape, x.dtype, perm, newShape);
    const dataId = backend.write(result, newShape, x.dtype);
    return {
        dataId,
        shape: newShape,
        dtype: x.dtype
    };
}
const transposeConfig = {
    kernelName: (0, _tfjsCore.Transpose),
    backendName: "cpu",
    kernelFunc: transpose
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Transpose_impl":"2fFZe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2fFZe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transposeImpl", ()=>transposeImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function transposeImpl(xVals, xShape, dtype, perm, newShape) {
    const xRank = xShape.length;
    const xSize = (0, _tfjsCore.util).sizeFromShape(xShape);
    const xStrides = (0, _tfjsCore.util).computeStrides(xShape);
    const newStrides = (0, _tfjsCore.util).computeStrides(newShape);
    const result = (0, _tfjsCore.util).getTypedArrayFromDType(dtype, (0, _tfjsCore.util).sizeFromShape(newShape));
    for(let i = 0; i < xSize; ++i){
        const loc = (0, _tfjsCore.util).indexToLoc(i, xRank, xStrides);
        // Permute location.
        const newLoc = new Array(loc.length);
        for(let i = 0; i < newLoc.length; i++)newLoc[i] = loc[perm[i]];
        const newIndex = (0, _tfjsCore.util).locToIndex(newLoc, xRank, newStrides);
        result[newIndex] = xVals[i];
    }
    return result;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"59HGn":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedGatherImpl", ()=>raggedGatherImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function validateIndices(indices, indicesShape, numParams) {
    indices.forEach((index, i)=>{
        if (index < 0 || index >= numParams) {
            const locString = (0, _tfjsCore.util).indexToLoc(i, indicesShape.length, (0, _tfjsCore.util).computeStrides(indicesShape)).join(",");
            throw new Error(`indices[${locString}] = ${index} is not in [0, ${numParams})`);
        }
    });
}
function validateSplits(paramsNestedSplits, numParamsDenseValues) {
    // Validate
    for(let dim = 0; dim < paramsNestedSplits.length; ++dim){
        const splits = paramsNestedSplits[dim];
        const lastSplit = dim === paramsNestedSplits.length - 1 ? numParamsDenseValues : paramsNestedSplits[dim + 1].length;
        if (splits.length === 0) throw new Error("Ragged splits may not be empty");
        if (splits[0] < 0) throw new Error("Ragged splits must be non-negative");
        if (splits[splits.length - 1] > lastSplit) throw new Error("Ragged splits must not point past values");
        for(let i = 1; i < splits.length; ++i){
            if (splits[i - 1] > splits[i]) throw new Error("Ragged splits must be sorted in ascending order");
        }
    }
}
// Construct the `splits` output tensors, encoded using a nested vector.
// Also find the slices of values that need to be copied, and store them
// in `valueSlices`.  The total number of values that will be copied (which
// we need for allocating the output values tensor) is stored in `numValues`.
function makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues) {
    const valueSlices = [];
    let numValues = 0;
    const numSplits = indicesShape.length - 1 + paramsNestedSplits.length;
    const outSplits = new Array(numSplits).fill(null).map(()=>[
            0
        ]);
    validateSplits(paramsNestedSplits, numParamsDenseValues);
    // Add `splits` that come from all but the last dimension of the dense
    // Tensor `indices`.  In particular, for each dimension D, we add a
    // splits tensor whose values are:
    //   range(reduceProd(splits.shape[:D]) + 1) * splits.shape[D+1]
    // E.g., if indices.shape=[2, 3, 4] then we will add splits tensors:
    //   [0, 3, 6]                    # length=2+1, stride=3
    //   [0, 4, 8, 12, 16, 20, 24]    # length=2*3+1, stride=4
    let nrows = 1;
    for(let dim = 0; dim < indicesShape.length - 1; ++dim){
        nrows *= indicesShape[dim];
        const rowLength = indicesShape[dim + 1];
        for(let i = 1; i < nrows + 1; ++i)outSplits[dim].push(i * rowLength);
    }
    // Add `splits` that come from `paramsNestedSplits`.  Starting with the
    // outermost ragged dimension (i.e., the first `splits` tensor), we work
    // our way in, finding the range of values that should be copied.  As we
    // go, we update the output `splits` for each dimension with the appropriate
    // values.  In particular, the *lengths* of the slices from `param_splits`
    // should be copied to generate corresponding slice lengths in the output
    // splits.  E.g., if we are copying a ragged row with length 4, then we
    // should add a new split point to outSplits that is 4 greater than the
    // previous split point in outSplits.
    for(let i = 0; i < indices.length; ++i){
        let start = indices[i];
        let limit = indices[i] + 1;
        // Copy splits.
        for(let dim = 0; dim < paramsNestedSplits.length; ++dim){
            const splits = paramsNestedSplits[dim];
            const outDim = dim + indicesShape.length - 1;
            if (outDim >= 0) {
                const outSplitsOutDim = outSplits[outDim];
                const delta = outSplitsOutDim[outSplitsOutDim.length - 1] - splits[start];
                for(let j = start; j < limit; ++j)outSplits[outDim].push(splits[j + 1] + delta);
            }
            start = splits[start];
            limit = splits[limit];
        }
        if (limit !== start) {
            valueSlices.push([
                start,
                limit
            ]);
            numValues += limit - start;
        }
    }
    return {
        outSplits,
        valueSlices,
        numValues
    };
}
function getSplits(outSplits) {
    const splitsOut = [];
    for(let i = 0; i < outSplits.length; ++i){
        const numSplits = outSplits[i].length;
        const splits = (0, _tfjsCore.util).getArrayFromDType("int32", numSplits);
        splitsOut.push(splits);
        outSplits[i].forEach((value, j)=>splits[j] = value);
    }
    return splitsOut;
}
function computeFlatOuterDims(orig, numOutDims) {
    const outDims = orig.slice(0, numOutDims);
    while(outDims.length < numOutDims)outDims.push(1);
    for(let inDim = numOutDims; inDim < orig.length; inDim++)outDims[numOutDims - 1] *= orig[inDim];
    return outDims;
}
// For each slice in `(start, limit)` in `valueSlices`, append
// `paramsDenseValues[start,...,limit] to `values`.  `valueSize` indicates
// the number of scalars contained in each value paramsDenseValues[i].
function writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, values, valuesShape) {
    const denseM = computeFlatOuterDims(paramsDenseValuesShape, 2)[1];
    const valuesM = computeFlatOuterDims(valuesShape, 2)[1];
    let outPos = 0;
    for (const slice of valueSlices)for(let i = slice[0]; i < slice[1]; ++i){
        for(let j = 0; j < valueSize; ++j)values[outPos * valuesM + j] = paramsDenseValues[i * denseM + j];
        ++outPos;
    }
}
function getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues) {
    const valuesShape = paramsDenseValuesShape.slice();
    valuesShape[0] = numValues;
    const valuesOut = (0, _tfjsCore.util).getArrayFromDType(paramsDenseValuesDType, (0, _tfjsCore.util).sizeFromShape(valuesShape));
    const numElements = paramsDenseValues.length;
    const valueSize = numElements === 0 ? 0 : numElements / paramsDenseValuesShape[0];
    writeValueSlices(paramsDenseValues, paramsDenseValuesShape, valueSlices, valueSize, valuesOut, valuesShape);
    return [
        valuesOut,
        valuesShape
    ];
}
function raggedGatherImpl(paramsNestedSplits, paramsNestedSplitsShapes, paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, indices, indicesShape, outputRaggedRank) {
    if (paramsNestedSplits.length === 0) throw new Error("paramsNestedSplits must be non empty");
    if (paramsNestedSplitsShapes[0].length === 0) throw new Error("Split tensors must not be scalars");
    const numParams = paramsNestedSplitsShapes[0][0] - 1;
    validateIndices(indices, indicesShape, numParams);
    if (paramsDenseValuesShape.length === 0) throw new Error("params.rank must be nonzero");
    const numParamsDenseValues = paramsDenseValuesShape[0];
    // Calculate the `splits`, and store the value slices that we need to
    // copy in `valueSlices`.
    const { outSplits, valueSlices, numValues } = makeSplits(indices, indicesShape, paramsNestedSplits, numParamsDenseValues);
    // Write the output tensors.
    const outputNestedSplits = getSplits(outSplits);
    const outputDenseValues = getValues(paramsDenseValues, paramsDenseValuesShape, paramsDenseValuesDType, valueSlices, numValues);
    return [
        outputNestedSplits,
        outputDenseValues[0],
        outputDenseValues[1]
    ];
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l2R3s":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedRangeImpl", ()=>raggedRangeImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
const INT32_MAX = 2147483647;
function raggedRangeImpl(starts, startsShape, startsDType, limits, limitsShape, deltas, deltasShape) {
    // Check input tensor shapes.
    if (startsShape.length > 1) throw new Error("starts must be a scalar or vector");
    if (limitsShape.length > 1) throw new Error("limits must be a scalar or vector");
    if (deltasShape.length > 1) throw new Error("deltas must be a scalar or vector");
    // Determine which tensors we need to broadcast.
    const broadcastStarts = startsShape.length === 0;
    const broadcastLimits = limitsShape.length === 0;
    const broadcastDeltas = deltasShape.length === 0;
    // nRows (number of output rows) is the size of the non-broadcast inputs,
    // or 1 if all inputs are scalars.
    const inSizes = [];
    if (!broadcastStarts) inSizes.push(startsShape[0]);
    if (!broadcastLimits) inSizes.push(limitsShape[0]);
    if (!broadcastDeltas) inSizes.push(deltasShape[0]);
    for(let i = 1; i < inSizes.length; ++i){
        if (inSizes[i] !== inSizes[i - 1]) throw new Error("starts, limits, and deltas must have the same shape");
    }
    const nRows = inSizes.length === 0 ? 1 : inSizes[0];
    // Construct the rtNestedSplits tensor.
    const rtNestedSplits = (0, _tfjsCore.util).getArrayFromDType("int32", nRows + 1);
    rtNestedSplits[0] = 0;
    for(let row = 0; row < nRows; ++row){
        const start = broadcastStarts ? starts[0] : starts[row];
        const limit = broadcastLimits ? limits[0] : limits[row];
        const delta = broadcastDeltas ? deltas[0] : deltas[row];
        if (delta === 0) throw new Error("Requires delta != 0");
        let size; // The number of elements in the specified range.
        if (delta > 0 && limit < start || delta < 0 && limit > start) size = 0;
        else {
            size = Math.ceil(Math.abs((limit - start) / delta));
            if (size > INT32_MAX) throw new Error(`Requires ((limit - start) / delta) <= ${INT32_MAX}`);
        }
        rtNestedSplits[row + 1] = rtNestedSplits[row] + size;
    }
    const nVals = rtNestedSplits[nRows];
    // Construct the rtDenseValues tensor.
    const rtDenseValues = (0, _tfjsCore.util).getArrayFromDType(startsDType, nVals);
    let valueIndex = 0;
    for(let row = 0; row < nRows; ++row){
        const rowSize = rtNestedSplits[row + 1] - rtNestedSplits[row];
        let value = broadcastStarts ? starts[0] : starts[row];
        const delta = broadcastDeltas ? deltas[0] : deltas[row];
        for(let i = 0; i < rowSize; ++i){
            rtDenseValues[valueIndex++] = value;
            value += delta;
        }
    }
    return [
        rtNestedSplits,
        rtDenseValues
    ];
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jDwSG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedTensorToTensorImpl", ()=>raggedTensorToTensorImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var RowPartitionType = (0, _tfjsCore.backend_util).RowPartitionType;
// Based on
// https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc
class RaggedTensorToTensorOp {
    constructor(shape, shapeShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypeStrings){
        this.shape = shape;
        this.shapeShape = shapeShape;
        this.values = values;
        this.valuesShape = valuesShape;
        this.valuesDType = valuesDType;
        this.defaultValue = defaultValue;
        this.defaultValueShape = defaultValueShape;
        this.rowPartitionValues = rowPartitionValues;
        this.rowPartitionValuesShapes = rowPartitionValuesShapes;
        this.rowPartitionTypes = (0, _tfjsCore.backend_util).getRowPartitionTypesHelper(rowPartitionTypeStrings);
        this.raggedRank = (0, _tfjsCore.backend_util).getRaggedRank(this.rowPartitionTypes);
    }
    getRowPartitionTypeByDimension(dimension) {
        if (this.rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) return this.rowPartitionTypes[dimension + 1];
        else return this.rowPartitionTypes[dimension];
    }
    // Returns the relationship between dimension and dimension + 1.
    getRowPartitionTensor(dimension) {
        if (this.rowPartitionTypes[0] === RowPartitionType.FIRST_DIM_SIZE) return this.rowPartitionValues[dimension + 1];
        else return this.rowPartitionValues[dimension];
    }
    getMaxWidth(dimension) {
        const rowPartitionTensor = this.getRowPartitionTensor(dimension - 1);
        switch(this.getRowPartitionTypeByDimension(dimension - 1)){
            case RowPartitionType.VALUE_ROWIDS:
                return RaggedTensorToTensorOp.getMaxWidthValueRowID(rowPartitionTensor);
            case RowPartitionType.ROW_SPLITS:
                return RaggedTensorToTensorOp.getMaxWidthRowSplit(rowPartitionTensor);
            default:
                throw new Error(`Cannot handle partition type ${RowPartitionType[this.getRowPartitionTypeByDimension(dimension - 1)]}`);
        }
    }
    static getMaxWidthRowSplit(rowSplit) {
        const tensorLength = rowSplit.length;
        if (tensorLength === 0 || tensorLength === 1) return 0;
        let maxWidth = 0;
        for(let i = 0; i < tensorLength - 1; ++i){
            const currentWidth = rowSplit[i + 1] - rowSplit[i];
            if (currentWidth > maxWidth) maxWidth = currentWidth;
        }
        return maxWidth;
    }
    static getMaxWidthValueRowID(valueRowIds) {
        const indexLength = valueRowIds.length;
        if (indexLength === 0) return 0;
        let firstEqualIndex = 0;
        let firstEqualIndexValue = valueRowIds[0];
        let maxWidth = 0;
        for(let i = 1; i < indexLength; ++i){
            const value = valueRowIds[i];
            if (value !== firstEqualIndexValue) {
                firstEqualIndexValue = value;
                maxWidth = Math.max(i - firstEqualIndex, maxWidth);
                firstEqualIndex = i;
            }
        }
        return Math.max(indexLength - firstEqualIndex, maxWidth);
    }
    tensorShapeFromTensor(t, tShape, isPartial = true) {
        if (tShape.length === 0) {
            if (t[0] === -1) return [];
            throw new Error(`The only valid scalar shape tensor is the fully unknown shape specified as -1.`);
        }
        // MakePartialShape/MakeShapeHelper.
        return makeShape(t, isPartial);
    }
    calculateOutputSize(firstDim) {
        const valueShape = this.valuesShape;
        const defaultValueShape = this.defaultValueShape;
        (0, _tfjsCore.backend_util).validateDefaultValueShape(defaultValueShape, valueShape);
        const shape = this.tensorShapeFromTensor(this.shape, this.shapeShape);
        const outputShape = (0, _tfjsCore.backend_util).combineRaggedTensorToTensorShapes(this.raggedRank, shape, valueShape);
        const result = outputShape;
        if (result[0] < 0) result[0] = firstDim;
        for(let i = 1; i <= this.raggedRank; ++i)if (result[i] < 0) result[i] = this.getMaxWidth(i);
        return result;
    }
    /**
     * The outputIndex represents the index in the output tensor
     * where the first element of a particular dimension would be written.
     * If it is -1, it indicates that the index is out of scope.
     * Example, given firstDimension = 10, firstDimensionOutput = 6,
     * and outputIndexMultiplier = 100:
     * result = [0 100 200 300 400 500 -1 -1 -1 -1]
     * If firstDimensionOutput = 11 instead, then:
     * result = [0 100 200 300 400 500 600 700 800 900]
     */ calculateFirstParentOutputIndex(firstDimension, outputIndexMultiplier, firstDimensionOutput) {
        const minDimension = Math.min(firstDimension, firstDimensionOutput);
        const result = [];
        let currentOutputIndex = 0;
        for(let i = 0; i < minDimension; ++i, currentOutputIndex += outputIndexMultiplier)result.push(currentOutputIndex);
        for(let i = minDimension; i < firstDimension; ++i)result.push(-1);
        (0, _tfjsCore.util).assert(result.length === firstDimension, ()=>"Final length of result must be equal to firstDimension.");
        return result;
    }
    calculateOutputIndexRowSplit(rowSplit, parentOutputIndex, outputIndexMultiplier, outputSize) {
        const rowSplitSize = rowSplit.length;
        const result = [];
        for(let i = 0; i < rowSplitSize - 1; ++i){
            const rowLength = rowSplit[i + 1] - rowSplit[i];
            let realLength = Math.min(outputSize, rowLength);
            let parentOutputIndexCurrent = parentOutputIndex[i];
            if (parentOutputIndexCurrent === -1) realLength = 0;
            for(let j = 0; j < realLength; ++j){
                result.push(parentOutputIndexCurrent);
                parentOutputIndexCurrent += outputIndexMultiplier;
            }
            for(let j = 0; j < rowLength - realLength; ++j)result.push(-1);
        }
        if (rowSplitSize > 0 && result.length !== rowSplit[rowSplitSize - 1]) throw new Error("Invalid row split size.");
        return result;
    }
    // Calculate the output index of the first element of a list.
    // The parentOutputIndex is the same computation for the previous list.
    // -1 indicates an element or list that is out of range.
    // The outputIndexMultiplier is the number of output indices one moves
    // forward for each column.
    // E.g., given:
    // valueRowIds:[0 1 2 2 2 3 5 5 6]
    // parentOutputIndex:[1000 1100 2000 2100 -1 3000 4000]
    // outputIndexMultiplier: 10
    // outputSize: 2
    // You get:
    // result = [1000 1100 2000 2010 -1 2100 -1 -1 3000]
    // result[0] = parentOutputIndex[valueRowIds[0]]
    // result[1] = parentOutputIndex[valueRowIds[1]]
    // result[2] = parentOutputIndex[valueRowIds[2]]
    // result[3] = parentOutputIndex[valueRowIds[2] + 10]
    // result[4] = -1 because it is the third element the size is 2.
    // result[5] = parentOutputIndex[valueRowIds[3]]
    // result[6] = -1 because parentOutputIndex[valueRowIds[6]] == -1
    // result[7] = -1 because parentOutputIndex[valueRowIds[6]] == -1
    // result[8] = parentOutputIndex[valueRowIds[7]]
    calculateOutputIndexValueRowID(valueRowIds, parentOutputIndex, outputIndexMultiplier, outputSize) {
        const indexSize = valueRowIds.length;
        const result = [];
        if (indexSize === 0) return [];
        let currentOutputColumn = 0;
        let currentValueRowId = valueRowIds[0];
        if (currentValueRowId >= parentOutputIndex.length) throw new Error(`Got currentValueRowId=${currentValueRowId}, which is not less than ${parentOutputIndex.length}`);
        let currentOutputIndex = parentOutputIndex[currentValueRowId];
        result.push(currentOutputIndex);
        for(let i = 1; i < indexSize; ++i){
            const nextValueRowId = valueRowIds[i];
            if (nextValueRowId === currentValueRowId) {
                if (currentOutputIndex >= 0) {
                    ++currentOutputColumn;
                    if (currentOutputColumn < outputSize) currentOutputIndex += outputIndexMultiplier;
                    else currentOutputIndex = -1;
                }
            } else {
                currentOutputColumn = 0;
                currentValueRowId = nextValueRowId;
                if (nextValueRowId >= parentOutputIndex.length) throw new Error(`Got nextValueRowId=${nextValueRowId} which is not less than ${parentOutputIndex.length}`);
                currentOutputIndex = parentOutputIndex[nextValueRowId];
            }
            result.push(currentOutputIndex);
        }
        if (result.length !== valueRowIds.length) throw new Error("Invalid row ids.");
        return result;
    }
    calculateOutputIndex(dimension, parentOutputIndex, outputIndexMultiplier, outputSize) {
        const rowPartitionTensor = this.getRowPartitionTensor(dimension);
        const partitionType = this.getRowPartitionTypeByDimension(dimension);
        switch(partitionType){
            case RowPartitionType.VALUE_ROWIDS:
                return this.calculateOutputIndexValueRowID(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);
            case RowPartitionType.ROW_SPLITS:
                if (rowPartitionTensor.length - 1 > parentOutputIndex.length) throw new Error(`Row partition size is greater than output size: ${rowPartitionTensor.length - 1} > ${parentOutputIndex.length}`);
                return this.calculateOutputIndexRowSplit(rowPartitionTensor, parentOutputIndex, outputIndexMultiplier, outputSize);
            default:
                throw new Error(`Unsupported partition type: ${RowPartitionType[partitionType]}`);
        }
    }
    getFirstDimensionSize() {
        const firstPartitionTensor = this.rowPartitionValues[0];
        if (this.rowPartitionTypes.length === 0) throw new Error("No row_partition_types given.");
        const firstPartitionType = this.rowPartitionTypes[0];
        switch(firstPartitionType){
            case RowPartitionType.FIRST_DIM_SIZE:
                return firstPartitionTensor[0];
            case RowPartitionType.VALUE_ROWIDS:
                throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");
            case RowPartitionType.ROW_SPLITS:
                return this.rowPartitionValuesShapes[0][0] - 1;
            default:
                throw new Error(`Cannot handle type ${RowPartitionType[firstPartitionType]}`);
        }
    }
    compute() {
        const firstPartitionTensor = this.rowPartitionValues[0];
        if (firstPartitionTensor.length <= 0) throw new Error("Invalid first partition input. Tensor requires at least one element.");
        const firstDimension = this.getFirstDimensionSize();
        const outputSize = this.calculateOutputSize(firstDimension);
        const multiplier = new Array(this.raggedRank + 1);
        multiplier[multiplier.length - 1] = 1;
        for(let i = multiplier.length - 2; i >= 0; --i)multiplier[i] = multiplier[i + 1] * outputSize[i + 1];
        // Full size of the tensor.
        const outputShape = makeShape(outputSize, false);
        const outputTensor = (0, _tfjsCore.util).getArrayFromDType(this.valuesDType, (0, _tfjsCore.util).sizeFromShape(outputShape));
        const fullSize = multiplier[0] * outputSize[0];
        if (fullSize > 0) {
            let outputIndex = this.calculateFirstParentOutputIndex(firstDimension, multiplier[0], outputSize[0]);
            for(let i = 1; i <= this.raggedRank; ++i){
                const newOutputIndex = this.calculateOutputIndex(i - 1, outputIndex, multiplier[i], outputSize[i]);
                outputIndex = newOutputIndex;
            }
            this.setOutput(this.raggedRank, outputIndex, outputTensor, outputShape);
        }
        return [
            outputShape,
            outputTensor
        ];
    }
    setOutput(raggedRank, outputIndex, outputTensor, outputShape) {
        if (outputTensor.length === 0) return;
        const valuesBase = this.values;
        const outputBase = outputTensor;
        let elementShape = outputShape.slice();
        elementShape = elementShape.slice(raggedRank + 1);
        const valueElementSize = (0, _tfjsCore.util).sizeFromShape(elementShape);
        const outputIndexSize = outputIndex.length;
        // Broadcast the default value to value_element_size.  (We can skip this
        // if defaultValueTensor.size == 1, since we use fill when that's true.)
        let defaultValue = this.defaultValue;
        if (defaultValue.length !== valueElementSize && defaultValue.length !== 1) {
            const srcShape = this.defaultValueShape;
            (0, _tfjsCore.tidy)(()=>{
                const defaultValueTensor = (0, _tfjsCore.reshape)(defaultValue, srcShape);
                const bCastDefault = (0, _tfjsCore.broadcastTo)(defaultValueTensor, elementShape);
                defaultValue = bCastDefault.dataSync();
            });
        }
        // Loop through the outputIndex array, finding contiguous regions that
        // should be copied.  Once we find the end of a contiguous region, copy it
        // and add any necessary padding (with defaultValue).
        let srcStart = 0; // Start of contiguous region (in values)
        let dstStart = 0; // Destination for contiguous region (in output)
        let dstEnd = 0; // Destination for contiguous region (in output)
        for(let srcI = 0; srcI <= outputIndexSize; ++srcI){
            // dstI is the destination where the value at srcI should be copied.
            let dstI = srcI < outputIndexSize ? outputIndex[srcI] : -1;
            // If we're still in a contiguous region, then update dstEnd go to the
            // next srcI.
            if (dstI === dstEnd) {
                ++dstEnd;
                continue;
            }
            // We found the end of contiguous region.  This can be because we found
            // a gap (dstI > dstEnd), or a source value that shouldn't be copied
            // because it's out-of-bounds (dstI == -1), or the end of the tensor
            // (dstI === -1).
            if (dstStart < dstEnd) {
                // Copy the contiguous region.
                const src = valuesBase.subarray(srcStart * valueElementSize);
                const dst = outputBase.subarray(dstStart * valueElementSize);
                const nVals = (dstEnd - dstStart) * valueElementSize;
                copyArray(dst, src, nVals);
            }
            // Add any necessary padding (w/ defaultValue).
            if (srcI >= outputIndexSize) {
                // We reached the end of values: pad to the end of output.
                const outputSize = outputTensor.length;
                dstI = Math.floor(outputSize / valueElementSize);
            }
            if (dstI > dstEnd) {
                if (this.defaultValue.length === 1) {
                    outputBase.subarray(dstEnd * valueElementSize, dstI * valueElementSize).fill(this.defaultValue[0]);
                    dstEnd = dstI;
                } else while(dstI > dstEnd){
                    const dst = outputBase.slice(dstEnd * valueElementSize);
                    copyArray(dst, defaultValue, valueElementSize);
                    ++dstEnd;
                }
            }
            // Update indices.
            if (dstI < 0) {
                // srcI should be skipped -- leave it out of the contiguous region.
                srcStart = srcI + 1;
                dstStart = dstEnd;
            } else {
                // srcI should be copied -- include it in the contiguous region.
                srcStart = srcI;
                dstStart = dstEnd;
                dstEnd = dstStart + 1;
            }
        }
    }
}
function copyArray(dst, src, size) {
    for(let i = 0; i < size; i++)dst[i] = src[i];
}
function makeShape(shape, isPartial) {
    const out = [];
    for (let dim of shape){
        if (dim < 0) {
            if (!isPartial) throw new Error(`Dimension ${dim} must be >= 0`);
            if (dim < -1) throw new Error(`Dimension ${dim} must be >= -1`);
            dim = -1;
        }
        out.push(dim);
    }
    return out;
}
function raggedTensorToTensorImpl(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes) {
    return new RaggedTensorToTensorOp(shape, shapesShape, values, valuesShape, valuesDType, defaultValue, defaultValueShape, rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes).compute();
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gJNJp":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rangeImpl", ()=>rangeImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function rangeImpl(start, stop, step, dtype) {
    const sameStartStop = start === stop;
    const increasingRangeNegativeStep = start < stop && step < 0;
    const decreasingRangePositiveStep = stop < start && step > 1;
    if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) return (0, _tfjsCore.util).makeZerosTypedArray(0, dtype);
    const numElements = Math.abs(Math.ceil((stop - start) / step));
    const values = (0, _tfjsCore.util).makeZerosTypedArray(numElements, dtype);
    if (stop < start && step === 1) // Auto adjust the step's sign if it hasn't been set
    // (or was set to 1)
    step = -1;
    values[0] = start;
    for(let i = 1; i < values.length; i++)values[i] = values[i - 1] + step;
    return values;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gcVCI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rsqrtImpl", ()=>rsqrtImpl);
parcelHelpers.export(exports, "rsqrt", ()=>rsqrt);
parcelHelpers.export(exports, "rsqrtConfig", ()=>rsqrtConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const rsqrtImpl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>1 / Math.sqrt(xi));
const rsqrt = (0, _unaryUtils.unaryKernelFuncFromImpl)((0, _tfjsCore.Rsqrt), rsqrtImpl);
const rsqrtConfig = {
    kernelName: (0, _tfjsCore.Rsqrt),
    backendName: "cpu",
    kernelFunc: rsqrt
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kMt6f":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "scatterImpl", ()=>scatterImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
    const flattenShape = [
        outputSize / sliceSize,
        sliceSize
    ];
    const indicesData = indices.values;
    const updatesData = updates.values;
    if (outputSize === 0) return (0, _tfjsCore.buffer)(shape, updates.dtype);
    const outBuf = defaultValue instanceof (0, _tfjsCore.TensorBuffer) ? defaultValue : (0, _tfjsCore.buffer)(flattenShape, updates.dtype);
    if (typeof defaultValue === "string") outBuf.values.fill(defaultValue);
    else if (typeof defaultValue === "number") outBuf.values.fill(defaultValue);
    else if (typeof defaultValue === "boolean") outBuf.values.fill(+defaultValue);
    for(let i = 0; i < numUpdates; i++){
        const index = [];
        let flattenIndex = 0;
        for(let j = 0; j < sliceRank; j++){
            const dim = indicesData[i * sliceRank + j];
            index.push(dim);
            flattenIndex += dim * strides[j];
        }
        if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) throw new Error(`Invalid indices: ${index} does not index into ${shape}`);
        for(let k = 0; k < sliceSize; k++)if (sumDupeIndices) outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k];
        else outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k];
    }
    return outBuf;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1a9v3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sigmoidImpl", ()=>sigmoidImpl);
parcelHelpers.export(exports, "sigmoid", ()=>sigmoid);
parcelHelpers.export(exports, "sigmoidConfig", ()=>sigmoidConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const sigmoidImpl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>1 / (1 + Math.exp(-xi)));
const sigmoid = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Sigmoid), (xi)=>1 / (1 + Math.exp(-xi)));
const sigmoidConfig = {
    kernelName: (0, _tfjsCore.Sigmoid),
    backendName: "cpu",
    kernelFunc: sigmoid
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9L9GN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sliceImpl", ()=>sliceImpl);
parcelHelpers.export(exports, "slice", ()=>slice);
parcelHelpers.export(exports, "sliceConfig", ()=>sliceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function sliceImpl(vals, begin, size, shape, dtype) {
    const isContinous = (0, _tfjsCore.slice_util).isSliceContinous(shape, begin, size);
    const length = (0, _tfjsCore.util).sizeFromShape(size);
    const xStrides = (0, _tfjsCore.util).computeStrides(shape);
    if (isContinous) {
        const flatOffset = (0, _tfjsCore.slice_util).computeFlatOffset(begin, xStrides);
        if (dtype === "string") return vals.slice(flatOffset, flatOffset + length);
        return vals.subarray(flatOffset, flatOffset + length);
    }
    const decodedData = dtype === "string" ? (0, _tfjsCore.backend_util).fromUint8ToStringArray(vals) : vals;
    const inBuf = (0, _tfjsCore.buffer)(shape, dtype, decodedData);
    const outBuf = (0, _tfjsCore.buffer)(size, dtype);
    for(let i = 0; i < outBuf.size; ++i){
        const outLoc = outBuf.indexToLoc(i);
        const inLoc = outLoc.map((idx, j)=>idx + begin[j]);
        outBuf.set(inBuf.get(...inLoc), ...outLoc);
    }
    if (dtype === "string") return (0, _tfjsCore.backend_util).fromStringArrayToUint8(outBuf.values);
    return outBuf.values;
}
function slice(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { begin, size } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "slice");
    const [$begin, $size] = (0, _tfjsCore.slice_util).parseSliceParams(x, begin, size);
    (0, _tfjsCore.slice_util).assertParamsValid(x, $begin, $size);
    const vals = backend.data.get(x.dataId).values;
    const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);
    return backend.makeTensorInfo($size, x.dtype, outVals);
}
const sliceConfig = {
    kernelName: (0, _tfjsCore.Slice),
    backendName: "cpu",
    kernelFunc: slice
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"72f9e":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseFillEmptyRowsImpl", ()=>sparseFillEmptyRowsImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {
    const indicesCount = indicesShape[0];
    const denseRows = denseShape[0];
    const emptyRowIndicator = new Array(denseRows);
    const reverseIndexMap = new Array(indicesCount);
    const rank = indicesShape[1];
    if (denseRows === 0) {
        if (indicesCount !== 0) throw new Error((0, _tfjsCore.backend_util).getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));
        const outputIndices = (0, _tfjsCore.util).getArrayFromDType(indicesDType, 0);
        const outputValues = (0, _tfjsCore.util).getArrayFromDType(valuesDType, 0);
        return [
            outputIndices,
            [
                0,
                rank
            ],
            outputValues,
            emptyRowIndicator,
            reverseIndexMap
        ];
    }
    let rowsAreOrdered = true;
    let lastIndicesRow = 0;
    const csrOffset = new Array(denseRows).fill(0);
    for(let i = 0; i < indicesCount; ++i){
        // indices is a 2d tensor with shape of [N, rank]
        const row = indices[i * rank];
        if (row < 0) throw new Error((0, _tfjsCore.backend_util).getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));
        if (row >= denseRows) throw new Error((0, _tfjsCore.backend_util).getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));
        ++csrOffset[row];
        rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;
        lastIndicesRow = row;
    }
    let allRowsFull = true;
    for(let row = 0; row < denseRows; ++row){
        // csrOffset here describes the number of elements in this dense row
        const rowEmpty = csrOffset[row] === 0;
        emptyRowIndicator[row] = rowEmpty;
        allRowsFull = allRowsFull && !rowEmpty;
        // In filled version, each row has at least one element.
        csrOffset[row] = Math.max(csrOffset[row], 1);
        // Update csrOffset to represent the number of elements up to and
        // including denseRows + 1:
        //  csrOffset[0] == #{elements of row 0}
        //  csrOffset[1] == #{elements of row 1} + #{elements of row 0}
        //  ..
        //  csrOffset[i] == starting index for elements in row i + 1.
        if (row > 0) csrOffset[row] += csrOffset[row - 1];
    }
    if (allRowsFull && rowsAreOrdered) {
        const outputIndices = indices;
        const outputValues = values;
        for(let i = 0; i < indicesCount; ++i)reverseIndexMap[i] = i;
        return [
            outputIndices,
            [
                indicesCount,
                rank
            ],
            outputValues,
            emptyRowIndicator,
            reverseIndexMap
        ];
    } else {
        const fullIndicesCount = csrOffset[denseRows - 1];
        const outputIndices = (0, _tfjsCore.util).getArrayFromDType(indicesDType, fullIndicesCount * rank);
        const outputValues = (0, _tfjsCore.util).getArrayFromDType(valuesDType, fullIndicesCount);
        const filledCount = new Array(denseRows).fill(0);
        // Fill in values for rows that are not missing
        for(let i = 0; i < indicesCount; ++i){
            // indices is a 2d tensor with shape of [N, rank]
            const row = indices[i * rank];
            const offset = filledCount[row];
            const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;
            filledCount[row]++; // Increment the filled count for this row.
            for(let j = 0; j < rank; ++j)// indices and outputIndices are 2d tensors with shape of [N, rank]
            outputIndices[outputI * rank + j] = indices[i * rank + j];
            outputValues[outputI] = values[i];
            // We'll need this reverse index map to backprop correctly.
            reverseIndexMap[i] = outputI;
        }
        // Fill in values for rows that are missing
        for(let row = 0; row < denseRows; ++row){
            const rowCount = filledCount[row];
            if (rowCount === 0) {
                const startingIndex = row === 0 ? 0 : csrOffset[row - 1];
                // Remaining index values were set to zero already.
                // Just need to set the row index in the right location.
                // outputIndices is a 2d tensor with shape of [N, rank]
                outputIndices[startingIndex * rank + 0] = row;
                for(let col = 1; col < rank; ++col)outputIndices[startingIndex * rank + col] = 0;
                outputValues[startingIndex] = defaultValue;
            }
        }
        return [
            outputIndices,
            [
                fullIndicesCount,
                rank
            ],
            outputValues,
            emptyRowIndicator,
            reverseIndexMap
        ];
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ecdf6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseReshapeImpl", ()=>sparseReshapeImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {
    const denseSize = (0, _tfjsCore.util).sizeFromShape(inputShape);
    const nnz = inputIndicesShape[0];
    const outputRank = targetShape.length;
    // Compute the output shape. Determine product of specified dimensions, and
    // find the index of the unspecified one.
    const outputShape = [];
    let product = 1;
    let unknownIndex = -1;
    for(let d = 0; d < outputRank; ++d){
        const size = targetShape[d];
        if (size === -1) {
            if (unknownIndex !== -1) throw new Error((0, _tfjsCore.backend_util).getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));
            unknownIndex = d;
            outputShape.push(1);
        } else {
            if (size < 0) throw new Error((0, _tfjsCore.backend_util).getSparseReshapeNegativeOutputDimErrorMessage(d, size));
            product *= size;
            outputShape.push(size);
        }
    }
    if (unknownIndex !== -1) {
        if (product <= 0) throw new Error((0, _tfjsCore.backend_util).getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
        const missing = Math.trunc(denseSize / product);
        if (product * missing !== denseSize) throw new Error((0, _tfjsCore.backend_util).getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));
        outputShape[unknownIndex] = missing;
    }
    const outputSize = (0, _tfjsCore.util).sizeFromShape(outputShape);
    if (outputSize !== denseSize) throw new Error((0, _tfjsCore.backend_util).getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));
    const inputRank = inputShape.length;
    const inputStrides = [];
    if (inputRank > 0) {
        inputStrides[inputRank - 1] = 1;
        for(let d = inputRank - 2; d >= 0; --d)inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
    }
    const outputStrides = [];
    if (outputRank > 0) {
        outputStrides[outputRank - 1] = 1;
        for(let d = outputRank - 2; d >= 0; --d)outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
    }
    const newIndices = (0, _tfjsCore.util).getArrayFromDType(inputDType, nnz * outputRank);
    for(let i = 0; i < nnz; ++i){
        let id = 0;
        for(let j = 0; j < inputRank; ++j)// inputIndices is a 2d tensor with shape of [nnz, inputRank]
        id += inputIndices[i * inputRank + j] * inputStrides[j];
        for(let j = 0; j < outputRank; ++j){
            // newIndices is a 2d tensor with shape of [nnz, outputRank]
            newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
            id %= outputStrides[j];
        }
    }
    return [
        newIndices,
        [
            nnz,
            outputRank
        ],
        outputShape
    ];
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ie2pQ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseSegmentReductionImpl", ()=>sparseSegmentReductionImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function sparseSegmentReductionImpl(input, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {
    const numIndices = indices.length;
    // Flatten the array to two dimensions
    const inputFlat = [
        inputShape[0],
        input.length / inputShape[0]
    ];
    const numCol = inputFlat[1];
    // Note that the current implementation assumes that segmentIds values are
    // sorted.
    const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;
    const outputRows = lastSegmentIdPlusOne;
    if (outputRows < 0) throw new Error((0, _tfjsCore.backend_util).getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
    const outputShape = inputShape.slice();
    outputShape[0] = outputRows;
    const outputLength = outputShape.reduce((product, value)=>product * value, 1);
    // Output array is initialized with the value 0 by default.
    const output = (0, _tfjsCore.util).getArrayFromDType(inputDType, outputLength);
    // Note that we do not initialize the output buffer with a default value, so
    // we need to explicitly set missing indices to the default value.
    if (numIndices === 0) {
        if (outputRows > 0) output.fill(defaultValue);
        return [
            output,
            outputShape
        ];
    }
    if (outputRows <= 0) throw new Error((0, _tfjsCore.backend_util).getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
    let start = 0, end = 1;
    // Index from which the output is not initialized.
    let uninitializedIndex = 0;
    let outIndex = segmentIds[start];
    while(true){
        // We initialize nextIndex to 0 to avoid may be uninitialized warning
        let nextIndex = 0;
        if (end < numIndices) {
            nextIndex = segmentIds[end];
            if (outIndex === nextIndex) {
                ++end;
                continue;
            }
            // We have a new segment here.  Verify that the segment ids are growing.
            if (outIndex >= nextIndex) throw new Error((0, _tfjsCore.backend_util).getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
        }
        if (outIndex < 0 || outIndex >= outputRows) throw new Error((0, _tfjsCore.backend_util).getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));
        // If there is a gap between two indices, we need to set that gap to the
        // default value.
        if (outIndex > uninitializedIndex) output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
        for(let i = start; i < end; ++i){
            const index = indices[i];
            if (index < 0 || index >= inputFlat[0]) throw new Error((0, _tfjsCore.backend_util).getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));
            for(let j = 0; j < numCol; j++)output[outIndex * numCol + j] += input[index * numCol + j];
        }
        if (isMean) for(let j = 0; j < numCol; j++)output[outIndex * numCol + j] /= end - start;
        start = end;
        ++end;
        uninitializedIndex = outIndex + 1;
        outIndex = nextIndex;
        if (end > numIndices) break;
    }
    // Fill the gap at the end with the default value.
    if (uninitializedIndex < outputRows) output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);
    return [
        output,
        outputShape
    ];
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cnnoj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sqrtImpl", ()=>sqrtImpl);
parcelHelpers.export(exports, "sqrt", ()=>sqrt);
parcelHelpers.export(exports, "sqrtConfig", ()=>sqrtConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const sqrtImpl = (0, _unaryImpl.createSimpleUnaryImpl)((xi)=>Math.sqrt(xi));
const sqrt = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Sqrt), (xi)=>Math.sqrt(xi));
const sqrtConfig = {
    kernelName: (0, _tfjsCore.Sqrt),
    backendName: "cpu",
    kernelFunc: sqrt
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"77Ql7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "squaredDifferenceImpl", ()=>squaredDifferenceImpl);
parcelHelpers.export(exports, "squaredDifference", ()=>squaredDifference);
parcelHelpers.export(exports, "squaredDifferenceConfig", ()=>squaredDifferenceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const squaredDifferenceImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>{
    const diff = a - b;
    return diff * diff;
});
const squaredDifference = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.SquaredDifference), squaredDifferenceImpl);
const squaredDifferenceConfig = {
    kernelName: (0, _tfjsCore.SquaredDifference),
    backendName: "cpu",
    kernelFunc: squaredDifference
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9G2f2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "staticRegexReplaceImpl", ()=>staticRegexReplaceImpl);
parcelHelpers.export(exports, "staticRegexReplaceConfig", ()=>staticRegexReplaceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryImpl = require("../utils/unary_impl");
var _unaryUtils = require("../utils/unary_utils");
const staticRegexReplaceImpl = (0, _unaryImpl.createSimpleUnaryImpl)((x, attrs)=>{
    const { pattern, replaceGlobal, rewrite } = attrs;
    // TODO(mattSoulanille): Don't create a regex each time.
    return x.replace(new RegExp(pattern, replaceGlobal ? "g" : ""), rewrite);
});
const staticRegexReplace = (0, _unaryUtils.unaryKernelFuncFromImpl)((0, _tfjsCore.StaticRegexReplace), staticRegexReplaceImpl);
const staticRegexReplaceConfig = {
    kernelName: (0, _tfjsCore.StaticRegexReplace),
    backendName: "cpu",
    kernelFunc: staticRegexReplace
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_impl":"jdtAN","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kCJo9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stridedSliceImpl", ()=>stridedSliceImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function stridedSliceImpl(outShape, xBuf, strides, begin) {
    const outBuf = (0, _tfjsCore.buffer)(outShape, xBuf.dtype);
    for(let i = 0; i < outBuf.size; i++){
        const loc = outBuf.indexToLoc(i);
        const newLoc = new Array(loc.length);
        for(let j = 0; j < newLoc.length; j++)newLoc[j] = loc[j] * strides[j] + begin[j];
        outBuf.set(xBuf.get(...newLoc), ...loc);
    }
    return outBuf;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3rAXp":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringNGramsImpl", ()=>stringNGramsImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
/**
 * The StringNGramsOp class creates ngrams from ragged string data.
 * The constructor contains all attributes related to the operation such as
 * padding widths and strings, and the compute function can be used to
 * compute the ngrams for different ragged tensor inputs.
 */ class StringNGramsOp {
    constructor(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences){
        this.separator = (0, _tfjsCore.util).encodeString(separator);
        this.nGramWidths = nGramWidths;
        this.leftPad = (0, _tfjsCore.util).encodeString(leftPad);
        this.rightPad = (0, _tfjsCore.util).encodeString(rightPad);
        this.padWidth = padWidth;
        this.preserveShort = preserveShortSequences;
    }
    getPadWidth(nGramWidth) {
        // Ngrams can be padded with either a fixed pad width or a dynamic pad
        // width depending on the 'padWidth' arg, but in no case should the padding
        // ever be wider than 'nGramWidth' - 1.
        return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);
    }
    getNumNGrams(length, nGramWidth) {
        const padWidth = this.getPadWidth(nGramWidth);
        return Math.max(0, length + 2 * padWidth - nGramWidth + 1);
    }
    createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {
        for(let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex){
            const padWidth = this.getPadWidth(nGramWidth);
            const leftPadding = Math.max(0, padWidth - nGramIndex);
            const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));
            const numTokens = nGramWidth - (leftPadding + rightPadding);
            const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
            // Calculate the total expected size of the nGram so we can reserve the
            // correct amount of space in the string.
            let nGramSize = 0;
            // Size of the left padding.
            nGramSize += leftPadding * this.leftPad.length;
            // Size of the tokens.
            for(let n = 0; n < numTokens; ++n)nGramSize += data[dataStartIndex + n].length;
            // Size of the right padding.
            nGramSize += rightPadding * this.rightPad.length;
            // Size of the separators.
            const numSeparators = leftPadding + rightPadding + numTokens - 1;
            nGramSize += numSeparators * this.separator.length;
            // Build the nGram.
            output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);
            const nGram = output[outputStartIndex + nGramIndex];
            let nextNGramIndex = 0;
            const appendToNGram = (str)=>str.forEach((value)=>nGram[nextNGramIndex++] = value);
            for(let n = 0; n < leftPadding; ++n){
                appendToNGram(this.leftPad);
                appendToNGram(this.separator);
            }
            // Only output first numTokens - 1 pairs of data and separator
            for(let n = 0; n < numTokens - 1; ++n){
                appendToNGram(data[dataStartIndex + n]);
                appendToNGram(this.separator);
            }
            // Handle case when there are no tokens or no right padding as these
            // can result in consecutive separators.
            if (numTokens > 0) {
                // If we have tokens, then output last and then pair each separator
                // with the right padding that follows, to ensure nGram ends either with
                // the token or with the right pad.
                appendToNGram(data[dataStartIndex + numTokens - 1]);
                for(let n = 0; n < rightPadding; ++n){
                    appendToNGram(this.separator);
                    appendToNGram(this.rightPad);
                }
            } else {
                // If we don't have tokens, then the last item inserted into the nGram
                // has been the separator from the left padding loop above. Hence,
                // output right pad and separator and make sure to finish with a
                // padding, not a separator.
                for(let n = 0; n < rightPadding - 1; ++n){
                    appendToNGram(this.rightPad);
                    appendToNGram(this.separator);
                }
                appendToNGram(this.rightPad);
            }
        }
    }
    // Data and splits together form the definition of the ragged tensor,
    // where data is 1 dimensional and contains the values of the tensor
    // and splits denotes the indices at which each row starts.
    compute(data, splits) {
        // Validate that the splits are valid indices into data, only if there are
        // splits specified.
        const inputDataSize = data.length;
        const splitsSize = splits.length;
        if (splitsSize > 0) {
            let prevSplit = splits[0];
            if (prevSplit !== 0) throw new Error(`First split value must be 0, got ${prevSplit}`);
            for(let i = 1; i < splitsSize; ++i){
                let validSplits = splits[i] >= prevSplit;
                validSplits = validSplits && splits[i] <= inputDataSize;
                if (!validSplits) throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);
                prevSplit = splits[i];
            }
            if (prevSplit !== inputDataSize) throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);
        }
        const numBatchItems = splitsSize - 1;
        const nGramsSplits = (0, _tfjsCore.util).getArrayFromDType("int32", splitsSize);
        // If there is no data or size, return an empty ragged tensor.
        if (inputDataSize === 0 || splitsSize === 0) {
            const empty = new Array(inputDataSize);
            for(let i = 0; i <= numBatchItems; ++i)nGramsSplits[i] = 0;
            return [
                empty,
                nGramsSplits
            ];
        }
        nGramsSplits[0] = 0;
        for(let i = 1; i <= numBatchItems; ++i){
            const length = splits[i] - splits[i - 1];
            let numNGrams = 0;
            this.nGramWidths.forEach((nGramWidth)=>{
                numNGrams += this.getNumNGrams(length, nGramWidth);
            });
            if (this.preserveShort && length > 0 && numNGrams === 0) numNGrams = 1;
            nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;
        }
        const nGrams = new Array(nGramsSplits[numBatchItems]);
        for(let i = 0; i < numBatchItems; ++i){
            const splitIndex = splits[i];
            let outputStartIdx = nGramsSplits[i];
            this.nGramWidths.forEach((nGramWidth)=>{
                const length = splits[i + 1] - splits[i];
                const numNGrams = this.getNumNGrams(length, nGramWidth);
                this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
                outputStartIdx += numNGrams;
            });
            // If we're preserving short sequences, check to see if no sequence was
            // generated by comparing the current output start idx to the original
            // one (nGramSplitsdata). If no ngrams were generated, then they will
            // be equal (since we increment outputStartIdx by numNGrams every
            // time we create a set of ngrams.)
            if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {
                const dataLength = splits[i + 1] - splits[i];
                // One legitimate reason to not have any ngrams when this.preserveShort
                // is true is if the sequence itself is empty. In that case, move on.
                if (dataLength === 0) continue;
                // We don't have to worry about dynamic padding sizes here: if padding
                // was dynamic, every sequence would have had sufficient padding to
                // generate at least one nGram.
                const nGramWidth = dataLength + 2 * this.padWidth;
                const numNGrams = 1;
                this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
            }
        }
        return [
            nGrams,
            nGramsSplits
        ];
    }
}
function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) {
    return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences).compute(data, dataSplits);
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8xmpZ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringSplitImpl", ()=>stringSplitImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function split(str, delimiters, skipEmpty, result) {
    if (!str.length) return;
    // When the delimiter is empty, the input is split into individual characters.
    if (delimiters.length === 0) {
        for(let i = 0; i < str.length; ++i)result.push(str.subarray(i, i + 1));
        return;
    }
    // When there is one delimiter, the input is split only at that delimiter.
    if (delimiters.length === 1) {
        const delimiter = delimiters[0];
        let f = str.indexOf(delimiter);
        while(f !== -1){
            const token = str.subarray(0, f);
            if (!skipEmpty || token.length !== 0) result.push(token);
            str = str.subarray(f + 1);
            f = str.indexOf(delimiter);
        }
        if (!skipEmpty || str.length !== 0) result.push(str);
        return;
    }
    // When there are multiple delimiters, the input is split at every instance
    // one of the delimiters appears.
    let tokenStart = 0;
    for(let i = 0; i < str.length + 1; i++)if (i === str.length || delimiters.indexOf(str[i]) !== -1) {
        const token = str.subarray(tokenStart, i);
        if (!skipEmpty || token.length !== 0) result.push(token);
        tokenStart = i + 1;
    }
}
function stringSplitImpl(input, delimiter, skipEmpty) {
    const batchSize = input.length;
    // Empty delimiter means split the input character by character.
    const tokens = [];
    let outputSize = 0;
    let maxNumEntries = 0;
    const numIndices = new Array(batchSize);
    for(let i = 0; i < batchSize; ++i){
        const prevTokensLength = tokens.length;
        split(input[i], delimiter, skipEmpty, tokens);
        const nEntries = tokens.length - prevTokensLength;
        numIndices[i] = nEntries;
        outputSize += nEntries;
        maxNumEntries = Math.max(maxNumEntries, nEntries);
    }
    const indices = (0, _tfjsCore.util).getArrayFromDType("int32", outputSize * 2);
    const values = new Array(outputSize);
    const shape = [
        batchSize,
        maxNumEntries
    ];
    let c = 0;
    for(let i = 0; i < batchSize; ++i)for(let j = 0; j < numIndices[i]; ++j){
        // indices is a 2d tensor with shape of [outputSize, 2]
        indices[c * 2] = i;
        indices[c * 2 + 1] = j;
        values[c] = tokens[c];
        ++c;
    }
    return [
        indices,
        values,
        shape
    ];
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6jwLv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringToHashBucketFastImpl", ()=>stringToHashBucketFastImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function stringToHashBucketFastImpl(input, numBuckets) {
    const output = (0, _tfjsCore.util).getArrayFromDType("int32", input.length);
    for(let i = 0; i < input.length; ++i)output[i] = (0, _tfjsCore.util).fingerPrint64(input[i]).modulo(numBuckets).getLowBitsUnsigned();
    return output;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4DByI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "subImpl", ()=>subImpl);
parcelHelpers.export(exports, "subComplexImpl", ()=>subComplexImpl);
parcelHelpers.export(exports, "sub", ()=>sub);
parcelHelpers.export(exports, "subConfig", ()=>subConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const subImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((aValue, bValue)=>aValue - bValue);
const subComplexImpl = (0, _binaryUtils.createComplexBinaryKernelImpl)((aReal, aImag, bReal, bImag)=>{
    return {
        real: aReal - bReal,
        imag: aImag - bImag
    };
});
const sub = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Sub), subImpl, subComplexImpl);
const subConfig = {
    kernelName: (0, _tfjsCore.Sub),
    backendName: "cpu",
    kernelFunc: sub
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7g2re":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * An implementation of the tile kernel shared between webgl and cpu for string
 * tensors only.
 */ parcelHelpers.export(exports, "tileImpl", ()=>tileImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function tileImpl(xBuf, reps) {
    const newShape = new Array(xBuf.rank);
    for(let i = 0; i < newShape.length; i++)newShape[i] = xBuf.shape[i] * reps[i];
    const result = (0, _tfjsCore.buffer)(newShape, xBuf.dtype);
    for(let i = 0; i < result.values.length; ++i){
        const newLoc = result.indexToLoc(i);
        const originalLoc = new Array(xBuf.rank);
        for(let j = 0; j < originalLoc.length; j++)originalLoc[j] = newLoc[j] % xBuf.shape[j];
        const originalIndex = xBuf.locToIndex(originalLoc);
        result.values[i] = xBuf.values[originalIndex];
    }
    return result;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dl1sf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /** An implementation of the TopK kernel shared between webgl and cpu. */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "topKImpl", ()=>topKImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
const comparePair = (a, b)=>{
    const valueDiff = b.value - a.value;
    return valueDiff === 0 ? a.index - b.index : valueDiff;
};
/**
 * Partitions array where all elements smaller than the (k+1) smallest element
 * are found to the left of it, and all larger to the right of it.
 * Based on the Floyd-Rivest Algorithm, ref:
 * https://en.wikipedia.org/wiki/Floyd%E2%80%93Rivest_algorithm
 * @param array: Array to partition
 * @param left: Left index for the interval
 * @param right: Right index for the interval
 * @param k: Desired index value, where array[k] is the (k+1)th smallest element
 *           when left = 0
 */ function select(array, k, left = 0, right = array.length - 1) {
    while(right > left){
        // Use select recursively to sample a smaller set of size s
        // the arbitrary constants 600 and 0.5 are used in the original
        // version to minimize execution time.
        if (right - left > 600) {
            const n = right - left + 1;
            const i = k - left + 1;
            const z = Math.log(n);
            const s = 0.5 * Math.exp(2 * z / 3);
            const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i - n / 2);
            const newLeft = Math.max(left, Math.floor(k - i * s / n + sd));
            const newRight = Math.min(right, Math.floor(k + (n - i) * s / n + sd));
            select(array, k, newLeft, newRight);
        }
        // partition the elements between left and right around t
        const t = array[k];
        let i = left;
        let j = right;
        (0, _tfjsCore.util).swap(array, left, k);
        if (comparePair(array[right], t) > 0) (0, _tfjsCore.util).swap(array, left, right);
        while(i < j){
            (0, _tfjsCore.util).swap(array, i, j);
            i++;
            j--;
            while(comparePair(array[i], t) < 0)i = i + 1;
            while(comparePair(array[j], t) > 0)j = j - 1;
        }
        if (comparePair(array[left], t) === 0) (0, _tfjsCore.util).swap(array, left, j);
        else {
            j = j + 1;
            (0, _tfjsCore.util).swap(array, j, right);
        }
        // Adjust left and right towards the boundaries of the subset
        // containing the (k - left + 1)th smallest element.
        if (j <= k) left = j + 1;
        if (k <= j) right = j - 1;
    }
}
function topKImpl(x, xShape, xDtype, k, sorted) {
    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
    const lastDim = xShape[xShape.length - 1];
    const [batch, size] = [
        x.length / lastDim,
        lastDim
    ];
    const allTopKVals = (0, _tfjsCore.util).getTypedArrayFromDType(xDtype, batch * k);
    const allTopKIndices = (0, _tfjsCore.util).getTypedArrayFromDType("int32", batch * k);
    for(let b = 0; b < batch; b++){
        const offset = b * size;
        const vals = x.subarray(offset, offset + size);
        let valAndInd = new Array(vals.length);
        vals.forEach((value, index)=>valAndInd[index] = {
                value,
                index
            });
        if (k < valAndInd.length) {
            select(valAndInd, k);
            valAndInd = valAndInd.slice(0, k);
        }
        if (sorted) valAndInd.sort(comparePair);
        const outOffset = b * k;
        const topKVals = allTopKVals.subarray(outOffset, outOffset + k);
        const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
        for(let i = 0; i < k; i++){
            topKVals[i] = valAndInd[i].value;
            topKIndices[i] = valAndInd[i].index;
        }
    }
    // Reshape back to the original input shape, except that the last
    // dimension is k.
    const outputShape = xShape.slice();
    outputShape[outputShape.length - 1] = k;
    return [
        (0, _tfjsCore.buffer)(outputShape, xDtype, allTopKVals),
        (0, _tfjsCore.buffer)(outputShape, "int32", allTopKIndices)
    ];
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gLexL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "uniqueImpl", ()=>uniqueImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function uniqueImpl(values, axis, shape, dtype) {
    // Normalize and validate axis.
    const $axis = (0, _tfjsCore.util).parseAxisParam(axis, shape)[0];
    // Calculate the new shape that is suitable for extracting data along the
    // given axis.
    //
    // The rank is 3.
    // The size of the 1st dimension is the size of all the axes < the given axis.
    // The size of the 2nd dimension is the same as the size of the given axis.
    // The size of the 3rd dimension is the size of all the axes > the given axis.
    //
    // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the
    // newShape would be: [2*3, 5, 4].
    //
    // Note that this is not the final output shape. This will be the shape for an
    // intermediate TensorBuffer (see inputBuffer below) to allow us to extract
    // values along the given axis. To demonstrate how it works, consider the
    // following example:
    //
    // Input: a 3D tensor, with shape [1, 2, 3]
    // [
    //   [
    //      [1,2,3],
    //      [4,5,6]
    //   ]
    // ]
    // Axis: 2 (the last axis).
    // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6].
    //
    // For this example, newShape would be: [2, 3, 1], where 2 is calculated from
    // 1*2. The re-shaped data would look like:
    //
    // [
    //   [
    //     [1], [2], [3]
    //   ],
    //   [
    //     [4], [5], [6]
    //   ]
    // ]
    //
    // Then, we can construct a 3-level nested loop by the following dimension
    // order to extract the values along the axis (dimension1):
    // i: dimension1       // 0,1,2 (newShape[1])
    //   m: dimension0     // 0,1   (newShape[0])
    //     n: dimension2   // 0     (newShape[2])
    //
    //                       m, i, n
    //                      ---------
    // Iteration 0: data at [0, 0, 0] => "1"
    // Iteration 1: data at [1, 0, 0] => "4"
    // We got [1,4].
    // Iteration 2: data at [0, 1, 0] => "2"
    // Iteration 3: data at [1, 1, 0] => "5"
    // We got [2,5].
    // Iteration 4: data at [0, 2, 0] => "3"
    // Iteration 5: data at [1, 2, 0] => "6"
    // We got [3,6].
    const newShape = [
        1,
        shape[0],
        1
    ];
    for(let i = 0; i < $axis; i++)newShape[0] *= shape[i];
    newShape[1] = shape[$axis];
    for(let i = $axis + 1; i < shape.length; i++)newShape[2] *= shape[i];
    // A map from unique elements (their string representations) to their values
    // in "indices" (below).
    const uniqueElements = new Map();
    // The indices of each unique element in the original tensor along the given
    // axis. It is 1D and has the same size as the given axis.
    const indices = new Int32Array(shape[$axis]);
    // Create a buffer so we can easily extract value at a given location.
    const inputBuffer = new (0, _tfjsCore.TensorBuffer)(newShape, dtype, values);
    // The indices along the given axis that have unique elements. This is a
    // de-duped version of "indices" above.
    const uniqueIndices = [];
    const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
    for(let i = 0; i < shape[$axis]; i++){
        // Extract values along the axis.
        let element;
        if (is1DTensor) // Fast path for 1D tensor input.
        element = values[i].toString();
        else {
            const axisValues = [];
            for(let m = 0; m < newShape[0]; m++)for(let n = 0; n < newShape[2]; n++)axisValues.push(inputBuffer.get(m, i, n));
            element = axisValues.join(",");
        }
        // Dedup and update various indices.
        const existingIndex = uniqueElements.get(element);
        if (existingIndex != null) indices[i] = existingIndex;
        else {
            const uniqueIndex = uniqueElements.size;
            uniqueElements.set(element, uniqueIndex);
            indices[i] = uniqueIndex;
            uniqueIndices.push(i);
        }
    }
    // Now we know where each of the unique elements are located along the axis
    // (uniqueIndices). Extract them from input buffer and store them in the
    // output buffer.
    const outputTmpShape = newShape.slice();
    outputTmpShape[1] = uniqueElements.size;
    const outputBuffer = new (0, _tfjsCore.TensorBuffer)(outputTmpShape, dtype);
    uniqueIndices.forEach((uniqueElementIndex, i)=>{
        for(let m = 0; m < newShape[0]; m++)for(let n = 0; n < newShape[2]; n++)outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
    });
    // The output shape can be calculated from the input shape with the size of
    // the given axis replaced by the number of unique elements along that axis.
    const outputShape = shape.slice();
    outputShape[$axis] = outputTmpShape[1];
    return {
        outputValues: outputBuffer.values,
        outputShape,
        indices
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1FCRm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "PackProgram", ()=>PackProgram);
var _gpgpuMath = require("./gpgpu_math");
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class PackProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = false;
        this.packedOutput = true;
        // Only input / output 3D tensors.
        this.outputShape = outputShape;
        this.rank = outputShape.length;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        if (this.rank === 0) this.userCode = `
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;
        else {
            const channels = (0, _packingUtil.getChannels)("rc", this.rank);
            const dtype = (0, _shaderCompiler.getCoordsDataType)(this.rank);
            const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);
            const setup = this.getSetup(channels);
            const output = this.getOutput(channels);
            this.userCode = `
        void main() {
          ${dtype} rc = getOutputCoords();

          if(${outOfBoundsCondition}) {
            setOutput(vec4(0));
          } else {
            ${setup}

            setOutput(vec4(${output}));
          }
        }
      `;
        }
    }
    getSourceCoordsArr(dims) {
        const coords = [];
        for(let row = 0; row <= 1; row++)for(let col = 0; col <= 1; col++){
            let coord = `${row === 0 ? "r" : "rp1"}, ${col === 0 ? "c" : "cp1"}`;
            for(let d = 2; d < this.rank; d++)coord = `${dims[dims.length - 1 - d]},` + coord;
            coords.push(coord);
        }
        return coords;
    }
    getOutOfBoundsCondition(dims) {
        if (this.rank === 1) return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
        let cond = "";
        for(let i = this.rank - 2; i < this.rank; i++){
            cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`;
            if (i < this.rank - 1) cond += "||";
        }
        return cond;
    }
    getSetup(dims) {
        if (this.rank === 1) return "";
        const innerDims = dims.slice(-2);
        const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];
        const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];
        return `
      int r = ${innerDims[0]};
      int c = ${innerDims[1]};
      int rp1 = r + 1;
      int cp1 = c + 1;

      bool cEdge = cp1 >= ${col};
      bool rEdge = rp1 >= ${row};
    `;
    }
    getOutput(dims) {
        const sourceCoords = this.getSourceCoordsArr(dims);
        if (this.rank === 1) {
            const outShape = this.enableShapeUniforms ? "outShape" : this.outputShape[0];
            return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;
        }
        return `getA(${sourceCoords[0]}),
            cEdge ? 0. : getA(${sourceCoords[1]}),
            rEdge ? 0. : getA(${sourceCoords[2]}),
            rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;
    }
}

},{"./gpgpu_math":"btJo5","./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5ylpz":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getVecChannels", ()=>getVecChannels);
parcelHelpers.export(exports, "getChannels", ()=>getChannels);
parcelHelpers.export(exports, "getSourceCoords", ()=>getSourceCoords);
function getVecChannels(name, rank) {
    return [
        "x",
        "y",
        "z",
        "w",
        "u",
        "v"
    ].slice(0, rank).map((d)=>`${name}.${d}`);
}
function getChannels(name, rank) {
    if (rank === 1) return [
        name
    ];
    return getVecChannels(name, rank);
}
function getSourceCoords(rank, dims) {
    if (rank === 1) return "rc";
    let coords = "";
    for(let i = 0; i < rank; i++){
        coords += dims[i];
        if (i < rank - 1) coords += ",";
    }
    return coords;
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6lTtb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ReshapePackedProgram", ()=>ReshapePackedProgram);
var _gpgpuMath = require("./gpgpu_math");
var _shaderCompilerUtil = require("./shader_compiler_util");
class ReshapePackedProgram {
    constructor(outputShape, inputShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "inputShape",
                type: "ivec3"
            }
        ];
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        let mainLoop = ``;
        for(let i = 0; i < 4; i++){
            let thisRC = `thisRC = rc;`;
            if (i % 2 === 1) thisRC += `thisRC.z += 1;`;
            if (i > 1) thisRC += `thisRC.y += 1;`;
            mainLoop += `
        ${thisRC}
        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
          int flatIndex = getFlatIndex(thisRC);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${i}] =
            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
        ${i > 0 ? "}" : ""}
      `;
        }
        this.userCode = `
      ${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}
      ${this.enableShapeUniforms ? _shaderCompilerUtil.getFlatIndexFrom3DOutput() : _shaderCompilerUtil.getFlatIndexFrom3D(outputShape)}

      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0.);

        ivec3 thisRC;
        int rows = ${this.enableShapeUniforms ? "outShape[1]" : outputShape[1]};
        int cols = ${this.enableShapeUniforms ? "outShape[2]" : outputShape[2]};

        ${mainLoop}

        setOutput(result);
      }
    `;
    }
}
function getReshapedInputCoords(shape, enableShapeUniforms) {
    const coordsFromIndexSnippet = enableShapeUniforms ? _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndexByUniform([
        "r",
        "c",
        "d"
    ], "inputShape") : _shaderCompilerUtil.getLogicalCoordinatesFromFlatIndex([
        "r",
        "c",
        "d"
    ], shape);
    return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${coordsFromIndexSnippet}
      return ivec3(r, c, d);
    }
  `;
}

},{"./gpgpu_math":"btJo5","./shader_compiler_util":"4ROqF","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"72VRa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "TextureManager", ()=>TextureManager);
parcelHelpers.export(exports, "computeBytes", ()=>computeBytes);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gpgpuUtil = require("./gpgpu_util");
var _texUtil = require("./tex_util");
class TextureManager {
    constructor(gpgpu){
        this.gpgpu = gpgpu;
        this.numUsedTextures = 0;
        this.numFreeTextures = 0;
        this._numBytesAllocated = 0;
        // Number of bytes that have been allocated and available for reuse.
        this._numBytesFree = 0;
        this.freeTextures = {};
        this.usedTextures = {};
        this.logEnabled = false;
    }
    acquireTexture(shapeRC, usage, isPacked) {
        const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
        const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
        if (!(shapeKey in this.freeTextures)) this.freeTextures[shapeKey] = [];
        if (!(shapeKey in this.usedTextures)) this.usedTextures[shapeKey] = [];
        const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
        if (this.freeTextures[shapeKey].length > 0) {
            this.numFreeTextures--;
            this.numUsedTextures++;
            this._numBytesFree -= texBytes;
            this.log();
            const newTexture = this.freeTextures[shapeKey].pop();
            this.usedTextures[shapeKey].push(newTexture);
            return newTexture;
        }
        let newTexture;
        if (physicalTexType === (0, _texUtil.PhysicalTextureType).PACKED_2X2_FLOAT32) newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
        else if (physicalTexType === (0, _texUtil.PhysicalTextureType).PACKED_2X2_FLOAT16) newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
        else if (physicalTexType === (0, _texUtil.PhysicalTextureType).UNPACKED_FLOAT32) newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
        else if (physicalTexType === (0, _texUtil.PhysicalTextureType).UNPACKED_FLOAT16) newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
        else if (physicalTexType === (0, _texUtil.PhysicalTextureType).PACKED_4X1_UNSIGNED_BYTE) newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
        this.usedTextures[shapeKey].push(newTexture);
        this.numUsedTextures++;
        this._numBytesAllocated += texBytes;
        this.log();
        return newTexture;
    }
    releaseTexture(texture, shape, logicalTexType, isPacked) {
        if (this.freeTextures == null) // Already disposed.
        return;
        const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
        const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
        if (!(shapeKey in this.freeTextures)) this.freeTextures[shapeKey] = [];
        const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
        const deleteTexThreshold = (0, _tfjsCore.env)().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");
        if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {
            this.gpgpu.deleteMatrixTexture(texture.texture);
            this._numBytesAllocated -= texBytes;
        } else {
            this.freeTextures[shapeKey].push(texture);
            this.numFreeTextures++;
            this._numBytesFree += texBytes;
        }
        this.numUsedTextures--;
        const texList = this.usedTextures[shapeKey];
        const texIndex = texList && texList.indexOf(texture);
        if (texIndex == null || texIndex < 0) throw new Error("Cannot release a texture that was never provided by this texture manager");
        texList[texIndex] = texList[texList.length - 1];
        texList.pop();
        this.log();
    }
    log() {
        if (!this.logEnabled) return;
        const total = this.numFreeTextures + this.numUsedTextures;
        console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);
        const freeRatio = this._numBytesFree / this._numBytesAllocated;
        console.log(`Bytes allocated: ${this._numBytesAllocated}`);
        console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);
    }
    get numBytesAllocated() {
        return this._numBytesAllocated;
    }
    get numBytesFree() {
        return this._numBytesFree;
    }
    getNumUsedTextures() {
        return this.numUsedTextures;
    }
    getNumFreeTextures() {
        return this.numFreeTextures;
    }
    dispose() {
        if (this.freeTextures == null) // Already disposed.
        return;
        for(const texShape in this.freeTextures)this.freeTextures[texShape].forEach((tex)=>{
            this.gpgpu.deleteMatrixTexture(tex.texture);
        });
        for(const texShape in this.usedTextures)this.usedTextures[texShape].forEach((tex)=>{
            this.gpgpu.deleteMatrixTexture(tex.texture);
        });
        // TODO: Assign non-null value (empty object) to textures after disposed.
        this.freeTextures = null;
        this.usedTextures = null;
        this.numUsedTextures = 0;
        this.numFreeTextures = 0;
        this._numBytesAllocated = 0;
        this._numBytesFree = 0;
    }
}
function numBytesForInternalFormat(gl, internalFormat) {
    // tslint:disable-next-line:no-any
    const glany = gl;
    if (internalFormat === glany.R32F) return 4;
    else if (internalFormat === glany.R16F) return 2;
    else if (internalFormat === glany.RGBA32F) return 16;
    else if (internalFormat === gl.RGBA) return 16;
    else if (internalFormat === glany.RGBA16F) return 8;
    else if (internalFormat === glany.RGBA8) return 4;
    throw new Error(`Unknown internal format ${internalFormat}`);
}
function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {
    // It is not possible to infer packed status from the texture type because
    // depending on the textureConfig, different  texture types may resolve to the
    // same internal format (e.g. in WebGL1, the internal format for
    // UNPACKED_FLOAT16 textures is gl.RGBA). Therefore we pass in `isPacked`
    // explicitly.
    const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);
    let numElements;
    if (isPacked) {
        const [packedWidth, packedHeight] = (0, _texUtil.getPackedMatrixTextureShapeWidthHeight)(shape[0], shape[1]);
        numElements = packedWidth * packedHeight;
    } else {
        const [width, height] = (0, _texUtil.getUnpackedMatrixTextureShapeWidthHeight)(shape[0], shape[1]);
        numElements = width * height;
    }
    const bytesPerElement = numBytesForInternalFormat(gl, internalFormat);
    return numElements * bytesPerElement;
}
function internalFormatForPhysicalTexType(physicalTexType, textureConfig) {
    switch(physicalTexType){
        case (0, _texUtil.PhysicalTextureType).PACKED_2X2_FLOAT32:
            return (0, _gpgpuUtil.getInternalFormatForPackedMatrixTexture)(textureConfig);
        case (0, _texUtil.PhysicalTextureType).PACKED_2X2_FLOAT16:
            return (0, _gpgpuUtil.getInternalFormatForFloat16PackedMatrixTexture)(textureConfig);
        case (0, _texUtil.PhysicalTextureType).UNPACKED_FLOAT32:
            return (0, _gpgpuUtil.getInternalFormatForFloat32MatrixTexture)(textureConfig);
        case (0, _texUtil.PhysicalTextureType).UNPACKED_FLOAT16:
            return (0, _gpgpuUtil.getInternalFormatForFloat16MatrixTexture)(textureConfig);
        case (0, _texUtil.PhysicalTextureType).PACKED_4X1_UNSIGNED_BYTE:
            return (0, _gpgpuUtil.getInternalFormatForUnsignedBytesMatrixTexture)(textureConfig);
        default:
            throw new Error(`Unknown physical texture type ${physicalTexType}`);
    }
}
function getPhysicalTextureForRendering(isPacked) {
    if ((0, _tfjsCore.env)().getBool("WEBGL_RENDER_FLOAT32_ENABLED")) {
        if (isPacked) return (0, _texUtil.PhysicalTextureType).PACKED_2X2_FLOAT32;
        return (0, _texUtil.PhysicalTextureType).UNPACKED_FLOAT32;
    }
    if (isPacked) return (0, _texUtil.PhysicalTextureType).PACKED_2X2_FLOAT16;
    return (0, _texUtil.PhysicalTextureType).UNPACKED_FLOAT16;
}
function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
    if (logicalTexType === (0, _texUtil.TextureUsage).UPLOAD) return (0, _texUtil.PhysicalTextureType).PACKED_2X2_FLOAT32;
    else if (logicalTexType === (0, _texUtil.TextureUsage).RENDER || logicalTexType == null) return getPhysicalTextureForRendering(isPacked);
    else if (logicalTexType === (0, _texUtil.TextureUsage).DOWNLOAD || logicalTexType === (0, _texUtil.TextureUsage).PIXELS) return (0, _texUtil.PhysicalTextureType).PACKED_4X1_UNSIGNED_BYTE;
    throw new Error(`Unknown logical texture type ${logicalTexType}`);
}
function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
    return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;
}

},{"@tensorflow/tfjs-core":"21Ftl","./gpgpu_util":"ezSrp","./tex_util":"1oYRe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gIMrR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "UnaryOpProgram", ()=>UnaryOpProgram);
parcelHelpers.export(exports, "CHECK_NAN_SNIPPET", ()=>CHECK_NAN_SNIPPET);
parcelHelpers.export(exports, "LINEAR", ()=>LINEAR);
parcelHelpers.export(exports, "ABS", ()=>ABS);
parcelHelpers.export(exports, "STEP", ()=>STEP);
parcelHelpers.export(exports, "ELU", ()=>ELU);
parcelHelpers.export(exports, "RELU", ()=>RELU);
parcelHelpers.export(exports, "RELU6", ()=>RELU6);
parcelHelpers.export(exports, "CLONE", ()=>CLONE);
parcelHelpers.export(exports, "SIGMOID", ()=>SIGMOID);
var _gpgpuMath = require("./gpgpu_math");
class UnaryOpProgram {
    constructor(aShape, opSnippet){
        this.variableNames = [
            "A"
        ];
        this.outputShape = aShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        this.userCode = `
      float unaryOperation(float x) {
        ${opSnippet}
      }

      void main() {
        float x = getAAtOutCoords();
        float y = unaryOperation(x);

        setOutput(y);
      }
    `;
    }
}
const CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;
const LINEAR = `return x;`;
const ABS = `return abs(x);`;
function STEP(alpha = 0.0) {
    return CHECK_NAN_SNIPPET + `
    return x > 0.0 ? 1.0 : float(${alpha});
  `;
}
const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
const RELU = CHECK_NAN_SNIPPET + `
  return (x < 0.0) ? 0.0 : x;
`;
const RELU6 = CHECK_NAN_SNIPPET + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
const CLONE = "return x;";
const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;

},{"./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c4D5K":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "LINEAR", ()=>LINEAR);
parcelHelpers.export(exports, "ELU", ()=>ELU);
parcelHelpers.export(exports, "RELU", ()=>RELU);
parcelHelpers.export(exports, "RELU6", ()=>RELU6);
parcelHelpers.export(exports, "SIGMOID", ()=>SIGMOID);
parcelHelpers.export(exports, "UnaryOpPackedProgram", ()=>UnaryOpPackedProgram);
var _gpgpuMath = require("./gpgpu_math");
const LINEAR = `return x;`;
const ELU = `
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`;
const RELU = `
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
const RELU6 = `
  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;
class UnaryOpPackedProgram {
    constructor(aShape, opSnippet){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = aShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        this.userCode = `
      vec4 unaryOperation(vec4 x) {
        ${opSnippet}
      }

      void main() {
        vec4 x = getAAtOutCoords();
        vec4 y = unaryOperation(x);

        setOutput(y);
      }
    `;
    }
}

},{"./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6fxgJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "UnpackProgram", ()=>UnpackProgram);
var _gpgpuMath = require("./gpgpu_math");
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class UnpackProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = false;
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        const rank = outputShape.length;
        const channels = (0, _packingUtil.getChannels)("rc", rank);
        const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
        const sourceCoords = (0, _packingUtil.getSourceCoords)(rank, channels);
        const innerDims = channels.slice(-2);
        const coords = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`;
        this.userCode = `
      void main() {
        ${dtype} rc = getOutputCoords();
        vec4 packedInput = getA(${sourceCoords});

        setOutput(getChannel(packedInput, ${coords}));
      }
    `;
    }
}

},{"./gpgpu_math":"btJo5","./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8XYZf":[function(require,module,exports) {
/** @license See the LICENSE file. */ // This code is auto-generated, do not modify this file!
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "version", ()=>version);
const version = "4.21.0";

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l4jNp":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MathBackendWebGL", ()=>(0, _backendWebgl.MathBackendWebGL));
parcelHelpers.export(exports, "setWebGLContext", ()=>(0, _canvasUtil.setWebGLContext));
parcelHelpers.export(exports, "GPGPUContext", ()=>(0, _gpgpuContext.GPGPUContext));
// WebGL specific utils.
parcelHelpers.export(exports, "gpgpu_util", ()=>_gpgpuUtil);
parcelHelpers.export(exports, "webgl_util", ()=>_webglUtil);
/**
 * Enforce use of half precision textures if available on the platform.
 *
 * @doc {heading: 'Environment', namespace: 'webgl'}
 */ parcelHelpers.export(exports, "forceHalfFloat", ()=>forceHalfFloat);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gpgpuUtil = require("./gpgpu_util");
var _webglUtil = require("./webgl_util");
var _backendWebgl = require("./backend_webgl");
var _canvasUtil = require("./canvas_util");
var _gpgpuContext = require("./gpgpu_context");
function forceHalfFloat() {
    (0, _tfjsCore.env)().set("WEBGL_FORCE_F16_TEXTURES", true);
}

},{"@tensorflow/tfjs-core":"21Ftl","./gpgpu_util":"ezSrp","./webgl_util":"fjf7t","./backend_webgl":"9XxNW","./canvas_util":"1tNGf","./gpgpu_context":"9nvvF","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"es42p":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var _tfjsCore = require("@tensorflow/tfjs-core");
var _fusedMatMul = require("./kernels/_FusedMatMul");
var _abs = require("./kernels/Abs");
var _acos = require("./kernels/Acos");
var _acosh = require("./kernels/Acosh");
var _add = require("./kernels/Add");
var _addN = require("./kernels/AddN");
var _all = require("./kernels/All");
var _any = require("./kernels/Any");
var _argMax = require("./kernels/ArgMax");
var _argMin = require("./kernels/ArgMin");
var _asin = require("./kernels/Asin");
var _asinh = require("./kernels/Asinh");
var _atan = require("./kernels/Atan");
var _atan2 = require("./kernels/Atan2");
var _atanh = require("./kernels/Atanh");
var _avgPool = require("./kernels/AvgPool");
var _avgPool3D = require("./kernels/AvgPool3D");
var _avgPool3DGrad = require("./kernels/AvgPool3DGrad");
var _avgPoolGrad = require("./kernels/AvgPoolGrad");
var _batchMatMul = require("./kernels/BatchMatMul");
var _batchNorm = require("./kernels/BatchNorm");
var _batchToSpaceND = require("./kernels/BatchToSpaceND");
var _bincount = require("./kernels/Bincount");
var _bitwiseAnd = require("./kernels/BitwiseAnd");
var _broadcastArgs = require("./kernels/BroadcastArgs");
var _cast = require("./kernels/Cast");
var _ceil = require("./kernels/Ceil");
var _clipByValue = require("./kernels/ClipByValue");
var _complex = require("./kernels/Complex");
var _complexAbs = require("./kernels/ComplexAbs");
var _concat = require("./kernels/Concat");
var _conv2D = require("./kernels/Conv2D");
var _conv2DBackpropFilter = require("./kernels/Conv2DBackpropFilter");
var _conv2DBackpropInput = require("./kernels/Conv2DBackpropInput");
var _conv3D = require("./kernels/Conv3D");
var _conv3DBackpropFilterV2 = require("./kernels/Conv3DBackpropFilterV2");
var _conv3DBackpropInputV2 = require("./kernels/Conv3DBackpropInputV2");
var _cos = require("./kernels/Cos");
var _cosh = require("./kernels/Cosh");
var _cropAndResize = require("./kernels/CropAndResize");
var _cumprod = require("./kernels/Cumprod");
var _cumsum = require("./kernels/Cumsum");
var _denseBincount = require("./kernels/DenseBincount");
var _depthToSpace = require("./kernels/DepthToSpace");
var _depthwiseConv2DNative = require("./kernels/DepthwiseConv2dNative");
var _depthwiseConv2DNativeBackpropFilter = require("./kernels/DepthwiseConv2dNativeBackpropFilter");
var _depthwiseConv2DNativeBackpropInput = require("./kernels/DepthwiseConv2dNativeBackpropInput");
var _diag = require("./kernels/Diag");
var _dilation2D = require("./kernels/Dilation2D");
var _einsum = require("./kernels/Einsum");
var _elu = require("./kernels/Elu");
var _eluGrad = require("./kernels/EluGrad");
var _equal = require("./kernels/Equal");
var _erf = require("./kernels/Erf");
var _exp = require("./kernels/Exp");
var _expandDims = require("./kernels/ExpandDims");
var _expm1 = require("./kernels/Expm1");
var _fft = require("./kernels/FFT");
var _fill = require("./kernels/Fill");
var _flipLeftRight = require("./kernels/FlipLeftRight");
var _floor = require("./kernels/Floor");
var _floorDiv = require("./kernels/FloorDiv");
var _fromPixels = require("./kernels/FromPixels");
var _fusedConv2D = require("./kernels/FusedConv2D");
var _fusedDepthwiseConv2D = require("./kernels/FusedDepthwiseConv2D");
var _gatherNd = require("./kernels/GatherNd");
var _gatherV2 = require("./kernels/GatherV2");
var _greater = require("./kernels/Greater");
var _greaterEqual = require("./kernels/GreaterEqual");
var _identity = require("./kernels/Identity");
var _ifft = require("./kernels/IFFT");
var _imag = require("./kernels/Imag");
var _isFinite = require("./kernels/IsFinite");
var _isInf = require("./kernels/IsInf");
var _isNaN = require("./kernels/IsNaN");
var _leakyRelu = require("./kernels/LeakyRelu");
var _less = require("./kernels/Less");
var _lessEqual = require("./kernels/LessEqual");
var _linSpace = require("./kernels/LinSpace");
var _log = require("./kernels/Log");
var _log1P = require("./kernels/Log1p");
var _logicalAnd = require("./kernels/LogicalAnd");
var _logicalNot = require("./kernels/LogicalNot");
var _logicalOr = require("./kernels/LogicalOr");
var _lrn = require("./kernels/LRN");
var _lrngrad = require("./kernels/LRNGrad");
var _max = require("./kernels/Max");
var _maximum = require("./kernels/Maximum");
var _maxPool = require("./kernels/MaxPool");
var _maxPool3D = require("./kernels/MaxPool3D");
var _maxPool3DGrad = require("./kernels/MaxPool3DGrad");
var _maxPoolGrad = require("./kernels/MaxPoolGrad");
var _maxPoolWithArgmax = require("./kernels/MaxPoolWithArgmax");
var _mean = require("./kernels/Mean");
var _min = require("./kernels/Min");
var _minimum = require("./kernels/Minimum");
var _mirrorPad = require("./kernels/MirrorPad");
var _mod = require("./kernels/Mod");
var _multinomial = require("./kernels/Multinomial");
var _multiply = require("./kernels/Multiply");
var _neg = require("./kernels/Neg");
var _nonMaxSuppressionV3 = require("./kernels/NonMaxSuppressionV3");
var _nonMaxSuppressionV4 = require("./kernels/NonMaxSuppressionV4");
var _nonMaxSuppressionV5 = require("./kernels/NonMaxSuppressionV5");
var _notEqual = require("./kernels/NotEqual");
var _oneHot = require("./kernels/OneHot");
var _onesLike = require("./kernels/OnesLike");
var _pack = require("./kernels/Pack");
var _padV2 = require("./kernels/PadV2");
var _pow = require("./kernels/Pow");
var _prelu = require("./kernels/Prelu");
var _prod = require("./kernels/Prod");
var _raggedGather = require("./kernels/RaggedGather");
var _raggedRange = require("./kernels/RaggedRange");
var _raggedTensorToTensor = require("./kernels/RaggedTensorToTensor");
var _range = require("./kernels/Range");
var _real = require("./kernels/Real");
var _realDiv = require("./kernels/RealDiv");
var _reciprocal = require("./kernels/Reciprocal");
var _relu = require("./kernels/Relu");
var _relu6 = require("./kernels/Relu6");
var _reshape = require("./kernels/Reshape");
var _resizeBilinear = require("./kernels/ResizeBilinear");
var _resizeBilinearGrad = require("./kernels/ResizeBilinearGrad");
var _resizeNearestNeighbor = require("./kernels/ResizeNearestNeighbor");
var _resizeNearestNeighborGrad = require("./kernels/ResizeNearestNeighborGrad");
var _reverse = require("./kernels/Reverse");
var _rotateWithOffset = require("./kernels/RotateWithOffset");
var _round = require("./kernels/Round");
var _rsqrt = require("./kernels/Rsqrt");
var _scatterNd = require("./kernels/ScatterNd");
var _searchSorted = require("./kernels/SearchSorted");
var _select = require("./kernels/Select");
var _selu = require("./kernels/Selu");
var _sigmoid = require("./kernels/Sigmoid");
var _sign = require("./kernels/Sign");
var _sin = require("./kernels/Sin");
var _sinh = require("./kernels/Sinh");
var _slice = require("./kernels/Slice");
var _softmax = require("./kernels/Softmax");
var _softplus = require("./kernels/Softplus");
var _spaceToBatchND = require("./kernels/SpaceToBatchND");
var _sparseFillEmptyRows = require("./kernels/SparseFillEmptyRows");
var _sparseReshape = require("./kernels/SparseReshape");
var _sparseSegmentMean = require("./kernels/SparseSegmentMean");
var _sparseSegmentSum = require("./kernels/SparseSegmentSum");
var _sparseToDense = require("./kernels/SparseToDense");
var _splitV = require("./kernels/SplitV");
var _sqrt = require("./kernels/Sqrt");
var _square = require("./kernels/Square");
var _squaredDifference = require("./kernels/SquaredDifference");
var _staticRegexReplace = require("./kernels/StaticRegexReplace");
var _step = require("./kernels/Step");
var _stridedSlice = require("./kernels/StridedSlice");
var _stringNGrams = require("./kernels/StringNGrams");
var _stringSplit = require("./kernels/StringSplit");
var _stringToHashBucketFast = require("./kernels/StringToHashBucketFast");
var _sub = require("./kernels/Sub");
var _sum = require("./kernels/Sum");
var _tan = require("./kernels/Tan");
var _tanh = require("./kernels/Tanh");
var _tensorScatterUpdate = require("./kernels/TensorScatterUpdate");
var _tile = require("./kernels/Tile");
var _topK = require("./kernels/TopK");
var _transform = require("./kernels/Transform");
var _transpose = require("./kernels/Transpose");
var _unique = require("./kernels/Unique");
var _unpack = require("./kernels/Unpack");
var _unsortedSegmentSum = require("./kernels/UnsortedSegmentSum");
var _zerosLike = require("./kernels/ZerosLike");
// List all kernel configs here
const kernelConfigs = [
    (0, _fusedMatMul._fusedMatMulConfig),
    (0, _abs.absConfig),
    (0, _acos.acosConfig),
    (0, _acosh.acoshConfig),
    (0, _add.addConfig),
    (0, _addN.addNConfig),
    (0, _all.allConfig),
    (0, _any.anyConfig),
    (0, _argMax.argMaxConfig),
    (0, _argMin.argMinConfig),
    (0, _asin.asinConfig),
    (0, _asinh.asinhConfig),
    (0, _atan.atanConfig),
    (0, _atan2.atan2Config),
    (0, _atanh.atanhConfig),
    (0, _avgPool.avgPoolConfig),
    (0, _avgPool3D.avgPool3DConfig),
    (0, _avgPool3DGrad.avgPool3DGradConfig),
    (0, _avgPoolGrad.avgPoolGradConfig),
    (0, _batchMatMul.batchMatMulConfig),
    (0, _batchNorm.batchNormConfig),
    (0, _batchToSpaceND.batchToSpaceNDConfig),
    (0, _bincount.bincountConfig),
    (0, _bitwiseAnd.bitwiseAndConfig),
    (0, _broadcastArgs.broadcastArgsConfig),
    (0, _cast.castConfig),
    (0, _ceil.ceilConfig),
    (0, _clipByValue.clipByValueConfig),
    (0, _complex.complexConfig),
    (0, _complexAbs.complexAbsConfig),
    (0, _concat.concatConfig),
    (0, _conv2D.conv2DConfig),
    (0, _conv2DBackpropFilter.conv2DBackpropFilterConfig),
    (0, _conv2DBackpropInput.conv2DBackpropInputConfig),
    (0, _conv3D.conv3DConfig),
    (0, _conv3DBackpropFilterV2.conv3DBackpropFilterV2Config),
    (0, _conv3DBackpropInputV2.conv3DBackpropInputConfig),
    (0, _cos.cosConfig),
    (0, _cosh.coshConfig),
    (0, _cropAndResize.cropAndResizeConfig),
    (0, _cumprod.cumprodConfig),
    (0, _cumsum.cumsumConfig),
    (0, _denseBincount.denseBincountConfig),
    (0, _depthToSpace.depthToSpaceConfig),
    (0, _depthwiseConv2DNative.depthwiseConv2dNativeConfig),
    (0, _depthwiseConv2DNativeBackpropFilter.depthwiseConv2dNativeBackpropFilterConfig),
    (0, _depthwiseConv2DNativeBackpropInput.depthwiseConv2dNativeBackpropInputConfig),
    (0, _diag.diagConfig),
    (0, _dilation2D.dilation2DConfig),
    (0, _einsum.einsumConfig),
    (0, _elu.eluConfig),
    (0, _eluGrad.eluGradConfig),
    (0, _equal.equalConfig),
    (0, _erf.erfConfig),
    (0, _exp.expConfig),
    (0, _expandDims.expandDimsConfig),
    (0, _expm1.expm1Config),
    (0, _fft.fftConfig),
    (0, _fill.fillConfig),
    (0, _flipLeftRight.flipLeftRightConfig),
    (0, _floor.floorConfig),
    (0, _floorDiv.floorDivConfig),
    (0, _fromPixels.fromPixelsConfig),
    (0, _fusedConv2D.fusedConv2DConfig),
    (0, _fusedDepthwiseConv2D.fusedDepthwiseConv2DConfig),
    (0, _gatherNd.gatherNdConfig),
    (0, _gatherV2.gatherV2Config),
    (0, _greater.greaterConfig),
    (0, _greaterEqual.greaterEqualConfig),
    (0, _identity.identityConfig),
    (0, _ifft.ifftConfig),
    (0, _imag.imagConfig),
    (0, _isFinite.isFiniteConfig),
    (0, _isInf.isInfConfig),
    (0, _isNaN.isNaNConfig),
    (0, _leakyRelu.leakyReluConfig),
    (0, _less.lessConfig),
    (0, _lessEqual.lessEqualConfig),
    (0, _linSpace.linSpaceConfig),
    (0, _log.logConfig),
    (0, _log1P.log1pConfig),
    (0, _logicalAnd.logicalAndConfig),
    (0, _logicalNot.logicalNotConfig),
    (0, _logicalOr.logicalOrConfig),
    (0, _lrn.LRNConfig),
    (0, _lrngrad.LRNGradConfig),
    (0, _max.maxConfig),
    (0, _maximum.maximumConfig),
    (0, _maxPool.maxPoolConfig),
    (0, _maxPool3D.maxPool3DConfig),
    (0, _maxPool3DGrad.maxPool3DGradConfig),
    (0, _maxPoolGrad.maxPoolGradConfig),
    (0, _maxPoolWithArgmax.maxPoolWithArgmaxConfig),
    (0, _mean.meanConfig),
    (0, _min.minConfig),
    (0, _minimum.minimumConfig),
    (0, _mirrorPad.mirrorPadConfig),
    (0, _mod.modConfig),
    (0, _multinomial.multinomialConfig),
    (0, _multiply.multiplyConfig),
    (0, _neg.negConfig),
    (0, _nonMaxSuppressionV3.nonMaxSuppressionV3Config),
    (0, _nonMaxSuppressionV4.nonMaxSuppressionV4Config),
    (0, _nonMaxSuppressionV5.nonMaxSuppressionV5Config),
    (0, _notEqual.notEqualConfig),
    (0, _oneHot.oneHotConfig),
    (0, _onesLike.onesLikeConfig),
    (0, _pack.packConfig),
    (0, _padV2.padV2Config),
    (0, _pow.powConfig),
    (0, _prelu.preluConfig),
    (0, _prod.prodConfig),
    (0, _raggedGather.raggedGatherConfig),
    (0, _raggedRange.raggedRangeConfig),
    (0, _raggedTensorToTensor.raggedTensorToTensorConfig),
    (0, _range.rangeConfig),
    (0, _real.realConfig),
    (0, _realDiv.realDivConfig),
    (0, _reciprocal.reciprocalConfig),
    (0, _relu.reluConfig),
    (0, _relu6.relu6Config),
    (0, _reshape.reshapeConfig),
    (0, _resizeBilinear.resizeBilinearConfig),
    (0, _resizeBilinearGrad.resizeBilinearGradConfig),
    (0, _resizeNearestNeighbor.resizeNearestNeighborConfig),
    (0, _resizeNearestNeighborGrad.resizeNearestNeighborGradConfig),
    (0, _reverse.reverseConfig),
    (0, _rotateWithOffset.rotateWithOffsetConfig),
    (0, _round.roundConfig),
    (0, _rsqrt.rsqrtConfig),
    (0, _scatterNd.scatterNdConfig),
    (0, _searchSorted.searchSortedConfig),
    (0, _select.selectConfig),
    (0, _selu.seluConfig),
    (0, _sigmoid.sigmoidConfig),
    (0, _sign.signConfig),
    (0, _sin.sinConfig),
    (0, _sinh.sinhConfig),
    (0, _slice.sliceConfig),
    (0, _softmax.softmaxConfig),
    (0, _softplus.softplusConfig),
    (0, _spaceToBatchND.spaceToBatchNDConfig),
    (0, _sparseFillEmptyRows.sparseFillEmptyRowsConfig),
    (0, _sparseReshape.sparseReshapeConfig),
    (0, _sparseSegmentMean.sparseSegmentMeanConfig),
    (0, _sparseSegmentSum.sparseSegmentSumConfig),
    (0, _sparseToDense.sparseToDenseConfig),
    (0, _splitV.splitVConfig),
    (0, _sqrt.sqrtConfig),
    (0, _square.squareConfig),
    (0, _squaredDifference.squaredDifferenceConfig),
    (0, _staticRegexReplace.staticRegexReplaceConfig),
    (0, _step.stepConfig),
    (0, _stridedSlice.stridedSliceConfig),
    (0, _stringNGrams.stringNGramsConfig),
    (0, _stringSplit.stringSplitConfig),
    (0, _stringToHashBucketFast.stringToHashBucketFastConfig),
    (0, _sub.subConfig),
    (0, _sum.sumConfig),
    (0, _tan.tanConfig),
    (0, _tanh.tanhConfig),
    (0, _tensorScatterUpdate.tensorScatterUpdateConfig),
    (0, _tile.tileConfig),
    (0, _topK.topKConfig),
    (0, _transform.transformConfig),
    (0, _transpose.transposeConfig),
    (0, _unique.uniqueConfig),
    (0, _unpack.unpackConfig),
    (0, _unsortedSegmentSum.unsortedSegmentSumConfig),
    (0, _zerosLike.zerosLikeConfig)
];
for (const kernelConfig of kernelConfigs)(0, _tfjsCore.registerKernel)(kernelConfig);

},{"@tensorflow/tfjs-core":"21Ftl","./kernels/_FusedMatMul":"f5H8C","./kernels/Abs":"5sWXh","./kernels/Acos":"88j8R","./kernels/Acosh":"3MqlI","./kernels/Add":"iCeMO","./kernels/AddN":"inami","./kernels/All":"8QrzY","./kernels/Any":"cNRuv","./kernels/ArgMax":"cxDNq","./kernels/ArgMin":"7we0s","./kernels/Asin":"2y04O","./kernels/Asinh":"8uUU0","./kernels/Atan":"hh4aJ","./kernels/Atan2":"k9THK","./kernels/Atanh":"3axXM","./kernels/AvgPool":"iU9nc","./kernels/AvgPool3D":"dFK3l","./kernels/AvgPool3DGrad":"kSFm4","./kernels/AvgPoolGrad":"dwrYJ","./kernels/BatchMatMul":"9tzUH","./kernels/BatchNorm":"2u2bT","./kernels/BatchToSpaceND":"bsQ5u","./kernels/Bincount":"7xVX3","./kernels/BitwiseAnd":"87LwA","./kernels/BroadcastArgs":"9Zlag","./kernels/Cast":"c2JG9","./kernels/Ceil":"iSroj","./kernels/ClipByValue":"gt2mD","./kernels/Complex":"55NfL","./kernels/ComplexAbs":"fwiH6","./kernels/Concat":"2gCZH","./kernels/Conv2D":"425yr","./kernels/Conv2DBackpropFilter":"iNkBM","./kernels/Conv2DBackpropInput":"jm349","./kernels/Conv3D":"c8Gvg","./kernels/Conv3DBackpropFilterV2":"lV1Z3","./kernels/Conv3DBackpropInputV2":"fnJFd","./kernels/Cos":"2H7EX","./kernels/Cosh":"jBLJs","./kernels/CropAndResize":"aCuUK","./kernels/Cumprod":"fIbv6","./kernels/Cumsum":"l2jIP","./kernels/DenseBincount":"7qaEa","./kernels/DepthToSpace":"926cY","./kernels/DepthwiseConv2dNative":"9xbgW","./kernels/DepthwiseConv2dNativeBackpropFilter":"aWI5l","./kernels/DepthwiseConv2dNativeBackpropInput":"6HZVm","./kernels/Diag":"7TZuU","./kernels/Dilation2D":"lLrqs","./kernels/Einsum":"hmWYw","./kernels/Elu":"fJBTw","./kernels/EluGrad":"hjBqq","./kernels/Equal":"dNiJE","./kernels/Erf":"ahvKl","./kernels/Exp":"i4xew","./kernels/ExpandDims":"5OvP3","./kernels/Expm1":"baClA","./kernels/FFT":"zq0LC","./kernels/Fill":"6Rawr","./kernels/FlipLeftRight":"8QDUY","./kernels/Floor":"hB9uc","./kernels/FloorDiv":"g0Hz7","./kernels/FromPixels":"bNE2l","./kernels/FusedConv2D":"5vkH0","./kernels/FusedDepthwiseConv2D":"5SfAK","./kernels/GatherNd":"4xUDy","./kernels/GatherV2":"cBFXe","./kernels/Greater":"eDhqn","./kernels/GreaterEqual":"1w6uF","./kernels/Identity":"hY9aT","./kernels/IFFT":"1mdy7","./kernels/Imag":"3PMfw","./kernels/IsFinite":"aWWtq","./kernels/IsInf":"bxXPH","./kernels/IsNaN":"jUwW0","./kernels/LeakyRelu":"hocMJ","./kernels/Less":"htToM","./kernels/LessEqual":"eXbD5","./kernels/LinSpace":"kM2RJ","./kernels/Log":"fVZGS","./kernels/Log1p":"czYhe","./kernels/LogicalAnd":"37RLf","./kernels/LogicalNot":"b9oed","./kernels/LogicalOr":"4Kv3V","./kernels/LRN":"6ZjnF","./kernels/LRNGrad":"BMOEB","./kernels/Max":"11YMi","./kernels/Maximum":"9B0Ki","./kernels/MaxPool":"74RsU","./kernels/MaxPool3D":"iXVcU","./kernels/MaxPool3DGrad":"eejL1","./kernels/MaxPoolGrad":"eaA7G","./kernels/MaxPoolWithArgmax":"jaMAy","./kernels/Mean":"4HPXU","./kernels/Min":"pHfvF","./kernels/Minimum":"5hTEN","./kernels/MirrorPad":"fGu7V","./kernels/Mod":"f0WUU","./kernels/Multinomial":"wTvWa","./kernels/Multiply":"5ePR8","./kernels/Neg":"6P8i7","./kernels/NonMaxSuppressionV3":"fEqP1","./kernels/NonMaxSuppressionV4":"5Qv9T","./kernels/NonMaxSuppressionV5":"T9Z8C","./kernels/NotEqual":"hOvsV","./kernels/OneHot":"6SVa6","./kernels/OnesLike":"cH3Uy","./kernels/Pack":"73QIj","./kernels/PadV2":"bOlXa","./kernels/Pow":"5GYaP","./kernels/Prelu":"a9omL","./kernels/Prod":"1BmsY","./kernels/RaggedGather":"dhNCK","./kernels/RaggedRange":"e99uc","./kernels/RaggedTensorToTensor":"c17RR","./kernels/Range":"4ulNZ","./kernels/Real":"4HOZy","./kernels/RealDiv":"cbtMz","./kernels/Reciprocal":"5wKjZ","./kernels/Relu":"fTpFl","./kernels/Relu6":"inRmD","./kernels/Reshape":"fgZen","./kernels/ResizeBilinear":"iNia7","./kernels/ResizeBilinearGrad":"gEi6O","./kernels/ResizeNearestNeighbor":"eTSe5","./kernels/ResizeNearestNeighborGrad":"fZoE2","./kernels/Reverse":"81ghw","./kernels/RotateWithOffset":"7nWSD","./kernels/Round":"gGWXk","./kernels/Rsqrt":"2xH8K","./kernels/ScatterNd":"cYHjA","./kernels/SearchSorted":"kyGeI","./kernels/Select":"b6rGf","./kernels/Selu":"1HlBg","./kernels/Sigmoid":"c3gWE","./kernels/Sign":"83hlB","./kernels/Sin":"6Fn44","./kernels/Sinh":"9tvOm","./kernels/Slice":"6jQ29","./kernels/Softmax":"cHx7b","./kernels/Softplus":"1YmQs","./kernels/SpaceToBatchND":"58w9T","./kernels/SparseFillEmptyRows":"iVlEr","./kernels/SparseReshape":"arBQ7","./kernels/SparseSegmentMean":"gQmRe","./kernels/SparseSegmentSum":"2zeh8","./kernels/SparseToDense":"2SjVb","./kernels/SplitV":"6cXfl","./kernels/Sqrt":"4bT4M","./kernels/Square":"jCuUm","./kernels/SquaredDifference":"fSbIq","./kernels/StaticRegexReplace":"8HWNl","./kernels/Step":"d3n9V","./kernels/StridedSlice":"47wD9","./kernels/StringNGrams":"aUZOK","./kernels/StringSplit":"ezwh5","./kernels/StringToHashBucketFast":"b6pUo","./kernels/Sub":"gV1EU","./kernels/Sum":"c4eiD","./kernels/Tan":"2vzWF","./kernels/Tanh":"7BFBf","./kernels/TensorScatterUpdate":"2uPVJ","./kernels/Tile":"dbDLN","./kernels/TopK":"gvorh","./kernels/Transform":"4cCvF","./kernels/Transpose":"cKJAC","./kernels/Unique":"e7lqL","./kernels/Unpack":"5jnF3","./kernels/UnsortedSegmentSum":"vnA1C","./kernels/ZerosLike":"28sQG"}],"f5H8C":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "_fusedMatMul", ()=>_fusedMatMul);
parcelHelpers.export(exports, "_fusedMatMulConfig", ()=>_fusedMatMulConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _batchMatMulImpl = require("./BatchMatMul_impl");
function _fusedMatMul(args) {
    const { inputs, backend, attrs } = args;
    const { a, b, bias, preluActivationWeights } = inputs;
    const { transposeA, transposeB, activation, leakyreluAlpha } = attrs;
    return (0, _batchMatMulImpl.batchMatMulImpl)({
        a,
        b,
        transposeA,
        transposeB,
        backend,
        bias,
        preluActivationWeights,
        leakyreluAlpha,
        activation
    });
}
const _fusedMatMulConfig = {
    kernelName: (0, _tfjsCore._FusedMatMul),
    backendName: "webgl",
    kernelFunc: _fusedMatMul
};

},{"@tensorflow/tfjs-core":"21Ftl","./BatchMatMul_impl":"kyEQG","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kyEQG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MATMUL_SHARED_DIM_THRESHOLD", ()=>MATMUL_SHARED_DIM_THRESHOLD);
parcelHelpers.export(exports, "batchMatMulImpl", ()=>batchMatMulImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _mulmatPackedGpu = require("../mulmat_packed_gpu");
var _multiply = require("./Multiply");
var _reshape = require("./Reshape");
var _sum = require("./Sum");
var _transpose = require("./Transpose");
const MATMUL_SHARED_DIM_THRESHOLD = 1000;
function batchMatMulImpl({ a, b, transposeA, transposeB, backend, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
    const aRank = a.shape.length;
    const bRank = b.shape.length;
    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
    const outerDimsA = a.shape.slice(0, -2);
    const outerDimsB = b.shape.slice(0, -2);
    const batchDimA = (0, _tfjsCore.util).sizeFromShape(outerDimsA);
    const batchDimB = (0, _tfjsCore.util).sizeFromShape(outerDimsB);
    const outShapeOuterDims = (0, _tfjsCore.broadcast_util).assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));
    const outShape = outShapeOuterDims.concat([
        outerShapeA,
        outerShapeB
    ]);
    (0, _tfjsCore.util).assert(innerShapeA === innerShapeB, ()=>`Error in matMul: inner shapes (${innerShapeA}) and (` + `${innerShapeB}) of Tensors with shapes ${a.shape} and ` + `${b.shape} and transposeA=${transposeA}` + ` and transposeB=${transposeB} must match.`);
    const a3dShape = transposeA ? [
        batchDimA,
        innerShapeA,
        outerShapeA
    ] : [
        batchDimA,
        outerShapeA,
        innerShapeA
    ];
    const b3dShape = transposeB ? [
        batchDimB,
        outerShapeB,
        innerShapeB
    ] : [
        batchDimB,
        innerShapeB,
        outerShapeB
    ];
    // The rest of the implementation is designed to operate on rank-3 tensors
    const a3d = (0, _reshape.reshape)({
        inputs: {
            x: a
        },
        backend,
        attrs: {
            shape: a3dShape
        }
    });
    const b3d = (0, _reshape.reshape)({
        inputs: {
            x: b
        },
        backend,
        attrs: {
            shape: b3dShape
        }
    });
    const intermediates = [
        a3d,
        b3d
    ];
    const batchDim = Math.max(batchDimA, batchDimB);
    const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
    const hasBias = bias != null;
    const hasPreluActivationWeights = preluActivationWeights != null;
    const hasLeakyreluAlpha = activation === "leakyrelu";
    const fusedActivation = activation != null ? (0, _kernelFuncsUtils.mapActivationToShaderProgram)(activation, true) : null;
    const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;
    let out;
    // Since the matrices are vectors, it is faster to call mul().sum()
    // because sum() is O(sqrt(N)) due to divide-and-conquer.
    if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {
        let aVec = a3d;
        let bVec = b3d;
        if (transposeA) {
            aVec = (0, _transpose.transpose)({
                inputs: {
                    x: a3d
                },
                backend,
                attrs: {
                    perm: [
                        0,
                        2,
                        1
                    ]
                }
            });
            intermediates.push(aVec);
        }
        if (transposeB) {
            bVec = (0, _transpose.transpose)({
                inputs: {
                    x: b3d
                },
                backend,
                attrs: {
                    perm: [
                        0,
                        2,
                        1
                    ]
                }
            });
            intermediates.push(bVec);
        }
        const shouldReshapeA = outerShapeB !== 1;
        const shouldReshapeB = outerShapeB === 1;
        let aVec3d = aVec;
        if (shouldReshapeA) {
            aVec3d = (0, _reshape.reshape)({
                inputs: {
                    x: aVec
                },
                backend,
                attrs: {
                    shape: [
                        batchDim,
                        sharedDim,
                        1
                    ]
                }
            });
            intermediates.push(aVec3d);
        }
        const axis = outerShapeB === 1 ? 2 : 1;
        let bVec3d = bVec;
        if (shouldReshapeB) {
            bVec3d = (0, _reshape.reshape)({
                inputs: {
                    x: bVec
                },
                backend,
                attrs: {
                    shape: [
                        batchDim,
                        1,
                        sharedDim
                    ]
                }
            });
            intermediates.push(bVec3d);
        }
        const product = (0, _multiply.multiply)({
            inputs: {
                a: aVec3d,
                b: bVec3d
            },
            backend
        });
        out = (0, _sum.sum)({
            inputs: {
                x: product
            },
            backend,
            attrs: {
                axis,
                keepDims: true
            }
        });
        intermediates.push(product);
    } else {
        const dtype = (0, _tfjsCore.upcastType)(a.dtype, b.dtype);
        const program = new (0, _mulmatPackedGpu.MatMulPackedProgram)(a3dShape, b3dShape, [
            batchDim,
            outerShapeA,
            outerShapeB
        ], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
        const inputs = [
            a3d,
            b3d
        ];
        if (bias != null) inputs.push(bias);
        if (hasPreluActivationWeights) inputs.push(preluActivationWeights);
        if (hasLeakyreluAlpha) {
            const $leakyreluAlpha = backend.makeTensorInfo([], "float32", (0, _tfjsCore.util).createScalarValue(leakyreluAlpha, "float32"));
            inputs.push($leakyreluAlpha);
            intermediates.push($leakyreluAlpha);
        }
        out = backend.runWebGLProgram(program, inputs, dtype);
    }
    const outReshaped = (0, _reshape.reshape)({
        inputs: {
            x: out
        },
        backend,
        attrs: {
            shape: outShape
        }
    });
    intermediates.push(out);
    for (const i of intermediates)backend.disposeIntermediateTensorInfo(i);
    return outReshaped;
}

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../mulmat_packed_gpu":"cdOmp","./Multiply":"5ePR8","./Reshape":"fgZen","./Sum":"c4eiD","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"grPtN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "CHECK_NAN_SNIPPET_UNARY", ()=>CHECK_NAN_SNIPPET_UNARY);
/**
 * Template that creates a `KernelFunc` for unary ops.
 * @param opSnippet Op snippet to create `UnaryOpProgram`.
 * @param packedOpSnippet Op snippet to create `UnaryOpPackedProgram`.
 * @param dtype Optional. If set, the result has this dtype. Otherwise, the
 *     result has the same dtype as the first input. This is mainly used in
 *     comparison kernels, such as Equal, Less, Greater, etc.
 */ parcelHelpers.export(exports, "unaryKernelFunc", ()=>unaryKernelFunc);
/**
 * Template that creates a `KernelFunc` for binary ops.
 * @param opSnippet Op snippet to create `BinaryOpProgram`.
 * @param packedOpSnippet Op snippet to create `BinaryOpPackedProgram`.
 * @param checkOutOfBoundsForPackedProgram Whether to set checkOutOfBounds=true
 *     when creating BinaryOpPackedProgram.
 * @param dtype Optional. If set, the result has this dtype. Otherwise, the
 *     result has the same dtype as the first input. This is mainly used in
 *     comparison kernels, such as Equal, Less, Greater, etc.
 */ parcelHelpers.export(exports, "binaryKernelFunc", ()=>binaryKernelFunc);
parcelHelpers.export(exports, "mapActivationToShaderProgram", ()=>mapActivationToShaderProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _complex = require("../kernels/Complex");
var _leakyRelu = require("../kernels/LeakyRelu");
var _prelu = require("../kernels/Prelu");
var _unaryopGpu = require("../unaryop_gpu");
var _unaryopPackedGpu = require("../unaryop_packed_gpu");
const CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;
function unaryKernelFunc({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {
    return ({ inputs, backend })=>{
        const { x } = inputs;
        const webglBackend = backend;
        const $dtype = dtype || x.dtype;
        if (webglBackend.shouldExecuteOnCPU([
            x
        ]) && cpuKernelImpl != null) {
            const xData = webglBackend.texData.get(x.dataId);
            const outValues = cpuKernelImpl(xData.values, $dtype);
            return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);
        }
        const shouldUsePackedProgram = (0, _tfjsCore.env)().getBool("WEBGL_PACK_UNARY_OPERATIONS") && packedOpSnippet != null;
        let program;
        if (shouldUsePackedProgram) program = new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(x.shape, packedOpSnippet);
        else program = new (0, _unaryopGpu.UnaryOpProgram)(x.shape, opSnippet);
        return webglBackend.runWebGLProgram(program, [
            x
        ], $dtype);
    };
}
function binaryKernelFunc({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {
    return ({ inputs, backend })=>{
        const { a, b } = inputs;
        const webglBackend = backend;
        if (supportsComplex && a.dtype === "complex64") {
            const aData = webglBackend.texData.get(a.dataId);
            const bData = webglBackend.texData.get(b.dataId);
            const [real, imag] = [
                [
                    aData.complexTensorInfos.real,
                    bData.complexTensorInfos.real
                ],
                [
                    aData.complexTensorInfos.imag,
                    bData.complexTensorInfos.imag
                ]
            ].map((complexParts)=>{
                const [aPart, bPart] = complexParts;
                const aHandle = {
                    dataId: aPart.dataId,
                    dtype: aPart.dtype,
                    shape: a.shape
                };
                const bHandle = {
                    dataId: bPart.dataId,
                    dtype: bPart.dtype,
                    shape: b.shape
                };
                const program = new (0, _binaryopGpu.BinaryOpProgram)(opSnippet, a.shape, b.shape);
                return webglBackend.runWebGLProgram(program, [
                    aHandle,
                    bHandle
                ], (0, _tfjsCore.upcastType)(aPart.dtype, bPart.dtype));
            });
            const complexOutput = (0, _complex.complex)({
                inputs: {
                    real,
                    imag
                },
                backend: webglBackend
            });
            webglBackend.disposeIntermediateTensorInfo(real);
            webglBackend.disposeIntermediateTensorInfo(imag);
            // TODO(annxingyuan): Implement CPU forwarding for complex inputs.
            return complexOutput;
        }
        const $dtype = dtype || (0, _tfjsCore.upcastType)(a.dtype, b.dtype);
        if ((a.dtype === "string" || b.dtype === "string" || webglBackend.shouldExecuteOnCPU([
            a,
            b
        ])) && cpuKernelImpl != null) {
            const aVals = webglBackend.texData.get(a.dataId).values;
            const bVals = webglBackend.texData.get(b.dataId).values;
            const decodedAVals = a.dtype === "string" ? // tslint:disable-next-line: no-any
            (0, _tfjsCore.backend_util).fromUint8ToStringArray(aVals) : aVals;
            const decodedBVals = a.dtype === "string" ? // tslint:disable-next-line: no-any
            (0, _tfjsCore.backend_util).fromUint8ToStringArray(bVals) : bVals;
            const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);
            const out = webglBackend.makeTensorInfo(outShape, $dtype);
            const outData = webglBackend.texData.get(out.dataId);
            outData.values = outValues;
            return out;
        }
        const shouldUsePackedProgram = (0, _tfjsCore.env)().getBool("WEBGL_PACK_BINARY_OPERATIONS") && packedOpSnippet != null;
        let program;
        if (shouldUsePackedProgram) program = new (0, _binaryopPackedGpu.BinaryOpPackedProgram)(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);
        else program = new (0, _binaryopGpu.BinaryOpProgram)(opSnippet, a.shape, b.shape);
        return webglBackend.runWebGLProgram(program, [
            a,
            b
        ], $dtype);
    };
}
function mapActivationToShaderProgram(activation, packed = false) {
    if (activation === "linear") {
        if (packed) return _unaryopPackedGpu.LINEAR;
        return _unaryopGpu.LINEAR;
    } else if (activation === "relu") {
        if (packed) return _unaryopPackedGpu.RELU;
        return _unaryopGpu.RELU;
    } else if (activation === "elu") {
        if (packed) return _unaryopPackedGpu.ELU;
        return _unaryopGpu.ELU;
    } else if (activation === "relu6") {
        if (packed) return _unaryopPackedGpu.RELU6;
        return _unaryopGpu.RELU6;
    } else if (activation === "prelu") {
        if (packed) return 0, _prelu.PRELU_PACKED;
        return 0, _prelu.PRELU;
    } else if (activation === "leakyrelu") {
        if (packed) return 0, _leakyRelu.LEAKYRELU_PACKED;
        return 0, _leakyRelu.LEAKYRELU;
    } else if (activation === "sigmoid") {
        if (packed) return _unaryopPackedGpu.SIGMOID;
        return _unaryopGpu.SIGMOID;
    }
    throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);
}

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","../kernels/Complex":"55NfL","../kernels/LeakyRelu":"hocMJ","../kernels/Prelu":"a9omL","../unaryop_gpu":"gIMrR","../unaryop_packed_gpu":"c4D5K","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d0A5z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "CHECK_NAN_SNIPPET", ()=>CHECK_NAN_SNIPPET);
parcelHelpers.export(exports, "SQUARED_DIFFERENCE", ()=>SQUARED_DIFFERENCE);
parcelHelpers.export(exports, "BinaryOpProgram", ()=>BinaryOpProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gpgpuMath = require("./gpgpu_math");
const CHECK_NAN_SNIPPET = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
const SQUARED_DIFFERENCE = "return (a - b) * (a - b);";
class BinaryOpProgram {
    constructor(op, aShape, bShape){
        this.variableNames = [
            "A",
            "B"
        ];
        this.outputShape = (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(aShape, bShape);
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        this.userCode = `
      float binaryOperation(float a, float b) {
        ${op}
      }

      void main() {
        float a = getAAtOutCoords();
        float b = getBAtOutCoords();
        setOutput(binaryOperation(a, b));
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hkzp7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "CHECK_NAN_SNIPPET_PACKED", ()=>CHECK_NAN_SNIPPET_PACKED);
parcelHelpers.export(exports, "ELU_DER", ()=>ELU_DER);
parcelHelpers.export(exports, "NOT_EQUAL", ()=>NOT_EQUAL);
parcelHelpers.export(exports, "BinaryOpPackedProgram", ()=>BinaryOpPackedProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gpgpuMath = require("./gpgpu_math");
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
const CHECK_NAN_SNIPPET_PACKED = `
  result.r = isNaN.r ? NAN : result.r;
  result.g = isNaN.g ? NAN : result.g;
  result.b = isNaN.b ? NAN : result.b;
  result.a = isNaN.a ? NAN : result.a;
`;
const ELU_DER = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
const NOT_EQUAL = `
  return vec4(notEqual(a, b));
`;
class BinaryOpPackedProgram {
    constructor(op, aShape, bShape, checkOutOfBounds = false){
        this.variableNames = [
            "A",
            "B"
        ];
        this.supportsBroadcasting = true;
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(aShape, bShape);
        const rank = this.outputShape.length;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(rank);
        let checkOutOfBoundsString = "";
        if (checkOutOfBounds) {
            if (rank === 0 || (0, _tfjsCore.util).sizeFromShape(this.outputShape) === 1) checkOutOfBoundsString = `
          result.y = 0.;
          result.z = 0.;
          result.w = 0.;
        `;
            else {
                const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
                checkOutOfBoundsString = `
          ${dtype} coords = getOutputCoords();
        `;
                if (rank === 1) {
                    if (this.enableShapeUniforms) checkOutOfBoundsString += `
            result.y = (coords + 1) >= outShape ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;
                    else checkOutOfBoundsString += `
            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;
                } else {
                    const channels = (0, _packingUtil.getChannels)("coords", rank);
                    if (this.enableShapeUniforms) checkOutOfBoundsString += `
            bool nextRowOutOfBounds =
              (${channels[rank - 2]} + 1) >= outShape[${rank} - 2];
            bool nextColOutOfBounds =
              (${channels[rank - 1]} + 1) >= outShape[${rank} - 1];
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `;
                    else checkOutOfBoundsString += `
            bool nextRowOutOfBounds =
              (${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};
            bool nextColOutOfBounds =
              (${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};
            result.y = nextColOutOfBounds ? 0. : result.y;
            result.z = nextRowOutOfBounds ? 0. : result.z;
            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
          `;
                }
            }
        }
        this.userCode = `
      vec4 binaryOperation(vec4 a, vec4 b) {
        ${op}
      }

      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();

        vec4 result = binaryOperation(a, b);
        ${checkOutOfBoundsString}

        setOutput(result);
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","./gpgpu_math":"btJo5","./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"55NfL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * In WebGL data is stored in GPU textures which can't be efficiently copied, so
 * complex tensors share data with their real and imaginary components. Complex
 * tensors' reference to the components is tracked by refCount on the individual
 * component. The refCounts are increased by the identity call.
 *
 * When a complex tensor is disposed, it will reduce the refCount on the
 * components by calling disposeData on each.
 */ parcelHelpers.export(exports, "complex", ()=>complex);
parcelHelpers.export(exports, "complexConfig", ()=>complexConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _identity = require("./Identity");
function complex(args) {
    const { inputs, backend } = args;
    const { real, imag } = inputs;
    const complexInfo = backend.makeTensorInfo(real.shape, "complex64");
    const complex = backend.texData.get(complexInfo.dataId);
    const realTensorInfo = (0, _identity.identity)({
        inputs: {
            x: real
        },
        backend
    });
    const imagTensorInfo = (0, _identity.identity)({
        inputs: {
            x: imag
        },
        backend
    });
    complex.complexTensorInfos = {
        real: realTensorInfo,
        imag: imagTensorInfo
    };
    return complexInfo;
}
const complexConfig = {
    kernelName: (0, _tfjsCore.Complex),
    backendName: "webgl",
    kernelFunc: complex
};

},{"@tensorflow/tfjs-core":"21Ftl","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hY9aT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "identity", ()=>identity);
parcelHelpers.export(exports, "identityConfig", ()=>identityConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function identity(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    backend.incRef(x.dataId);
    return {
        dataId: x.dataId,
        shape: x.shape,
        dtype: x.dtype
    };
}
const identityConfig = {
    kernelName: (0, _tfjsCore.Identity),
    backendName: "webgl",
    kernelFunc: identity
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hocMJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "LEAKYRELU", ()=>LEAKYRELU);
parcelHelpers.export(exports, "LEAKYRELU_PACKED", ()=>LEAKYRELU_PACKED);
parcelHelpers.export(exports, "leakyRelu", ()=>leakyRelu);
parcelHelpers.export(exports, "leakyReluConfig", ()=>leakyReluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
const LEAKYRELU = `return (a < 0.) ? b * a : a;`;
const LEAKYRELU_PACKED = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function leakyRelu(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { alpha } = attrs;
    const $alpha = backend.makeTensorInfo([], "float32", (0, _tfjsCore.util).createScalarValue(alpha, "float32"));
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new (0, _binaryopPackedGpu.BinaryOpPackedProgram)(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new (0, _binaryopGpu.BinaryOpProgram)(LEAKYRELU, x.shape, $alpha.shape);
    const result = backend.runWebGLProgram(program, [
        x,
        $alpha
    ], "float32");
    backend.disposeIntermediateTensorInfo($alpha);
    return result;
}
const leakyReluConfig = {
    kernelName: (0, _tfjsCore.LeakyRelu),
    backendName: "webgl",
    kernelFunc: leakyRelu
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a9omL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "PRELU", ()=>PRELU);
parcelHelpers.export(exports, "PRELU_PACKED", ()=>PRELU_PACKED);
parcelHelpers.export(exports, "prelu", ()=>prelu);
parcelHelpers.export(exports, "preluConfig", ()=>preluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
const PRELU = `return (a < 0.) ? b * a : a;`;
const PRELU_PACKED = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function prelu(args) {
    const { inputs, backend } = args;
    const { x, alpha } = inputs;
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new (0, _binaryopPackedGpu.BinaryOpPackedProgram)(PRELU_PACKED, x.shape, alpha.shape) : new (0, _binaryopGpu.BinaryOpProgram)(PRELU, x.shape, alpha.shape);
    return backend.runWebGLProgram(program, [
        x,
        alpha
    ], "float32");
}
const preluConfig = {
    kernelName: (0, _tfjsCore.Prelu),
    backendName: "webgl",
    kernelFunc: prelu
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cdOmp":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MatMulPackedProgram", ()=>MatMulPackedProgram);
var _gpgpuMath = require("./gpgpu_math");
class MatMulPackedProgram {
    constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false, hasLeakyreluActivation = false){
        this.variableNames = [
            "matrixA",
            "matrixB"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        const sharedDim = transposeA ? aShape[1] : aShape[2];
        const sharedDimensionPacked = Math.ceil(sharedDim / 2);
        const aSample = transposeA ? "i * 2, rc.y" : "rc.y, i * 2";
        const bSample = transposeB ? "rc.z, i * 2" : "i * 2, rc.z";
        const aSwizzle = transposeA ? [
            "a.xxyy",
            "a.zzww"
        ] : [
            "a.xxzz",
            "a.yyww"
        ];
        const bSwizzle = transposeB ? [
            "b.xzxz",
            "b.ywyw"
        ] : [
            "b.xyxy",
            "b.zwzw"
        ];
        let activationSnippet = "", applyActivationSnippet = "";
        if (activation) {
            if (hasPreluActivation) activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${activation}
        }`;
            else if (hasLeakyreluActivation) activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${activation}
        }`;
            else activationSnippet = `vec4 activation(vec4 x) {
          ${activation}
        }`;
            applyActivationSnippet = `result = activation(result);`;
        }
        const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
        if (addBias) this.variableNames.push("bias");
        if (hasPreluActivation) this.variableNames.push("preluActivationWeights");
        if (hasLeakyreluActivation) this.variableNames.push("leakyreluAlpha");
        let batchASnippet = "rc.x";
        let batchBSnippet = "rc.x";
        if (aShape[0] < bShape[0]) batchASnippet = `imod(rc.x, ${aShape[0]})`;
        else if (bShape[0] < aShape[0]) batchBSnippet = `imod(rc.x, ${bShape[0]})`;
        this.userCode = `
      ${activationSnippet}
      // Don't use uniform for sharedDimensionPacked for performance.
      const float sharedDimension = ${sharedDimensionPacked}.0;

      vec4 dot2x2ARowBCol(ivec3 rc) {
        vec4 result = vec4(0);
        int batchA = ${batchASnippet};
        int batchB = ${batchBSnippet};
        for (int i = 0; i < ${sharedDimensionPacked}; i++) {
          vec4 a = getMatrixA(batchA, ${aSample});
          vec4 b = getMatrixB(batchB, ${bSample});

          // These swizzled products need to be separately added.
          // See: https://github.com/tensorflow/tfjs/issues/1735
          result += (${aSwizzle[0]} * ${bSwizzle[0]});
          result += (${aSwizzle[1]} * ${bSwizzle[1]});
        }
        return result;
      }

      void main() {
        ivec3 rc = getOutputCoords();
        vec4 result = dot2x2ARowBCol(rc);

        ${addBiasSnippet}

        ${applyActivationSnippet}

        setOutput(result);
      }
    `;
    }
}

},{"./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5ePR8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "multiply", ()=>multiply);
parcelHelpers.export(exports, "multiplyConfig", ()=>multiplyConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopComplexGpu = require("../binaryop_complex_gpu");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _shared = require("../kernel_utils/shared");
var _complex = require("./Complex");
const MUL = "return a * b;";
function multiply(args) {
    const { inputs, backend } = args;
    const { a, b } = inputs;
    const dtype = (0, _tfjsCore.backend_util).upcastType(a.dtype, b.dtype);
    if (a.dtype === "complex64") {
        const aData = backend.texData.get(a.dataId);
        const bData = backend.texData.get(b.dataId);
        const realProgram = new (0, _binaryopComplexGpu.BinaryOpComplexProgram)(_binaryopComplexGpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
        const imagProgram = new (0, _binaryopComplexGpu.BinaryOpComplexProgram)(_binaryopComplexGpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
        const inputs = [
            {
                dataId: aData.complexTensorInfos.real.dataId,
                dtype: aData.complexTensorInfos.real.dtype,
                shape: a.shape
            },
            {
                dataId: aData.complexTensorInfos.imag.dataId,
                dtype: aData.complexTensorInfos.imag.dtype,
                shape: a.shape
            },
            {
                dataId: bData.complexTensorInfos.real.dataId,
                dtype: bData.complexTensorInfos.real.dtype,
                shape: b.shape
            },
            {
                dataId: bData.complexTensorInfos.imag.dataId,
                dtype: bData.complexTensorInfos.imag.dtype,
                shape: b.shape
            }
        ];
        const realPart = backend.runWebGLProgram(realProgram, inputs, "float32");
        const imagPart = backend.runWebGLProgram(imagProgram, inputs, "float32");
        const complexOutput = (0, _complex.complex)({
            inputs: {
                real: realPart,
                imag: imagPart
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(realPart);
        backend.disposeIntermediateTensorInfo(imagPart);
        // TODO(annxingyuan): CPU forwarding for complex inputs.
        return complexOutput;
    }
    if (backend.shouldExecuteOnCPU([
        a,
        b
    ])) {
        const aData = backend.texData.get(a.dataId);
        const bData = backend.texData.get(b.dataId);
        const [outValues, outShape] = (0, _shared.multiplyImplCPU)(a.shape, b.shape, aData.values, bData.values, dtype);
        const out = backend.makeTensorInfo(outShape, dtype);
        const outData = backend.texData.get(out.dataId);
        outData.values = outValues;
        return out;
    }
    let program;
    if ((0, _tfjsCore.env)().getBool("WEBGL_PACK_BINARY_OPERATIONS")) program = new (0, _binaryopPackedGpu.BinaryOpPackedProgram)(MUL, a.shape, b.shape);
    else program = new (0, _binaryopGpu.BinaryOpProgram)(MUL, a.shape, b.shape);
    return backend.runWebGLProgram(program, [
        a,
        b
    ], dtype);
}
const multiplyConfig = {
    kernelName: (0, _tfjsCore.Multiply),
    backendName: "webgl",
    kernelFunc: multiply
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_complex_gpu":"3sjSC","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","../kernel_utils/shared":"e4M7v","./Complex":"55NfL","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3sjSC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "COMPLEX_MULTIPLY", ()=>COMPLEX_MULTIPLY);
parcelHelpers.export(exports, "BinaryOpComplexProgram", ()=>BinaryOpComplexProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
const COMPLEX_MULTIPLY = {
    REAL: "return areal * breal - aimag * bimag;",
    IMAG: "return areal * bimag + aimag * breal;"
};
class BinaryOpComplexProgram {
    constructor(op, aShape, bShape){
        this.variableNames = [
            "AReal",
            "AImag",
            "BReal",
            "BImag"
        ];
        this.outputShape = (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(aShape, bShape);
        this.userCode = `
      float binaryOpComplex(
          float areal, float aimag, float breal, float bimag) {
        ${op}
      }

      void main() {
        float areal = getARealAtOutCoords();
        float aimag = getAImagAtOutCoords();
        float breal = getBRealAtOutCoords();
        float bimag = getBImagAtOutCoords();
        setOutput(binaryOpComplex(areal, aimag, breal, bimag));
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fgZen":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reshape", ()=>reshape);
parcelHelpers.export(exports, "reshapeConfig", ()=>reshapeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reshape = require("../kernel_utils/reshape");
var _webglUtil = require("../webgl_util");
function reshape(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { shape } = attrs;
    const webglBackend = backend;
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const $shape = (0, _tfjsCore.util).inferFromImplicitShape(shape, xSize);
    const $xSize = (0, _tfjsCore.util).sizeFromShape($shape);
    (0, _tfjsCore.util).assert(xSize === $xSize, ()=>`The new shape (${$shape}) has ${$xSize} elements and the old ` + `shape (${x.shape}) has ${xSize} elements. The new shape and old ` + `shape must have the same number of elements.`);
    const xTexData = webglBackend.texData.get(x.dataId);
    if (xTexData.isPacked && !(0, _webglUtil.isReshapeFree)(x.shape, $shape) && !(xTexData.texture !== null && (0, _webglUtil.isReshapeFree)(xTexData.shape, $shape))) return (0, _reshape.packedReshape)(x, $shape, webglBackend);
    webglBackend.incRef(x.dataId);
    return {
        dataId: x.dataId,
        shape: $shape,
        dtype: x.dtype
    };
}
const reshapeConfig = {
    kernelName: (0, _tfjsCore.Reshape),
    backendName: "webgl",
    kernelFunc: reshape
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reshape":"jLTQF","../webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jLTQF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "packedReshape", ()=>packedReshape);
var _reshapePackedGpu = require("../reshape_packed_gpu");
var _webglUtil = require("../webgl_util");
function packedReshape(input, afterShape, backend) {
    const input3DShape = [
        (0, _webglUtil.getBatchDim)(input.shape),
        ...(0, _webglUtil.getRowsCols)(input.shape)
    ];
    const input3D = {
        dtype: input.dtype,
        shape: input3DShape,
        dataId: input.dataId
    };
    const afterShapeAs3D = [
        (0, _webglUtil.getBatchDim)(afterShape),
        ...(0, _webglUtil.getRowsCols)(afterShape)
    ];
    const program = new (0, _reshapePackedGpu.ReshapePackedProgram)(afterShapeAs3D, input3DShape);
    const preventEagerUnpackingOfOutput = true;
    const customValues = [
        input3DShape
    ];
    const output = backend.runWebGLProgram(program, [
        input3D
    ], input.dtype, customValues, preventEagerUnpackingOfOutput);
    return {
        dataId: output.dataId,
        shape: afterShape,
        dtype: output.dtype
    };
}

},{"../reshape_packed_gpu":"6lTtb","../webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c4eiD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sum", ()=>sum);
parcelHelpers.export(exports, "sumConfig", ()=>sumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _sumImpl = require("./Sum_impl");
function sum(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    return (0, _sumImpl.sumImpl)(x, axis, keepDims, backend);
}
const sumConfig = {
    kernelName: (0, _tfjsCore.Sum),
    backendName: "webgl",
    kernelFunc: sum
};

},{"@tensorflow/tfjs-core":"21Ftl","./Sum_impl":"jJiik","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jJiik":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sumImpl", ()=>sumImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reduce = require("../kernel_utils/reduce");
var _reshape = require("./Reshape");
var _transposeImpl = require("./Transpose_impl");
function sumImpl(x, axis, keepDims, backend) {
    const reductionIndices = axis;
    const xRank = x.shape.length;
    const origAxes = (0, _tfjsCore.util).parseAxisParam(reductionIndices, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    const sumInputIsTransposed = permutedAxes != null;
    let sumInput = x;
    if (sumInputIsTransposed) {
        sumInput = (0, _transposeImpl.transposeImpl)(x, permutedAxes, backend);
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, xRank);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("sum", axes, xRank);
    const [sumOutShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(sumInput.shape, axes);
    let outShape = sumOutShape;
    if (keepDims) // rather than reshape at the end, set the target shape here.
    outShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(sumOutShape, origAxes);
    const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const batchSize = xSize / inSize;
    const reshapedInput = (0, _reshape.reshape)({
        inputs: {
            x: sumInput
        },
        attrs: {
            shape: [
                batchSize,
                inSize
            ]
        },
        backend
    });
    const outType = (0, _tfjsCore.sumOutType)(x.dtype);
    const reduced = (0, _reduce.reduce)(reshapedInput, outType, "sum", backend);
    const out = (0, _reshape.reshape)({
        inputs: {
            x: reduced
        },
        attrs: {
            shape: outShape
        },
        backend
    });
    backend.disposeIntermediateTensorInfo(reshapedInput);
    backend.disposeIntermediateTensorInfo(reduced);
    if (sumInputIsTransposed) backend.disposeIntermediateTensorInfo(sumInput);
    return out;
}

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reduce":"dr0Xl","./Reshape":"fgZen","./Transpose_impl":"arYj9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dr0Xl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reduce", ()=>reduce);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _meanGpu = require("../mean_gpu");
var _reduceGpu = require("../reduce_gpu");
// Returns an array of configuration objects that describe each stage of the
// reduction.
function getReductionStages(inShape) {
    const stages = [];
    while(stages.length === 0 || stages[stages.length - 1].outSize !== 1){
        const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];
        const windowSize = (0, _tfjsCore.backend_util).computeOptimalWindowSize(outSize);
        stages.push({
            inSize: outSize,
            windowSize,
            outSize: Math.ceil(outSize / windowSize)
        });
    }
    return stages;
}
function reduce(x, dtype, reductionType, backend) {
    const reductionStages = getReductionStages(x.shape);
    let result = x;
    for(let i = 0; i < reductionStages.length; i++){
        const { inSize, windowSize, outSize } = reductionStages[i];
        let program;
        let previousResult;
        if (reductionType === "mean") program = i === 0 ? new (0, _meanGpu.MeanProgram)({
            windowSize,
            inSize,
            batchSize: x.shape[0],
            outSize
        }, inSize) : new (0, _meanGpu.MeanProgram)({
            windowSize,
            inSize,
            batchSize: x.shape[0],
            outSize
        });
        else program = new (0, _reduceGpu.ReduceProgram)({
            windowSize,
            inSize,
            batchSize: x.shape[0],
            outSize
        }, reductionType);
        previousResult = result;
        result = backend.runWebGLProgram(program, [
            result
        ], dtype);
        if (previousResult.dataId !== x.dataId) backend.disposeIntermediateTensorInfo(previousResult);
    }
    return result;
}

},{"@tensorflow/tfjs-core":"21Ftl","../mean_gpu":"gMZwh","../reduce_gpu":"kqmxI","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gMZwh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MeanProgram", ()=>MeanProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
class MeanProgram {
    constructor(reduceInfo, divisor){
        this.variableNames = [
            "x"
        ];
        const { windowSize, batchSize, inSize, outSize } = reduceInfo;
        this.outputShape = [
            batchSize,
            outSize
        ];
        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
        const windowSizeVec4Remainder = windowSize % 4;
        let updateSnippet = `sumValue += dot(values, ones);`;
        if (divisor != null) {
            const denominator = 1 / divisor;
            updateSnippet = `sumValue += dot(values * ${(0, _tfjsCore.util).isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;
        }
        let checkOutOfBounds = "";
        if (inSize % windowSize > 0) checkOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return 0.0;
        }
      `;
        this.userCode = `
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${checkOutOfBounds}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        float sumValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder === 1}) {
          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1), 0.0, 0.0);

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2), 0.0);

          ${updateSnippet}
        }
        setOutput(sumValue);
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kqmxI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ReduceProgram", ()=>ReduceProgram);
class ReduceProgram {
    constructor(reduceInfo, reduceType){
        this.variableNames = [
            "x"
        ];
        const { windowSize, batchSize, inSize, outSize } = reduceInfo;
        this.outputShape = [
            batchSize,
            outSize
        ];
        let initializationValue = "0.0";
        let compareOp = ``;
        if (reduceType === "prod") initializationValue = "1.0";
        else if (reduceType === "min") {
            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
            initializationValue = "1.0 / 1e-20";
            compareOp = `min`;
        } else if (reduceType === "max") {
            // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
            initializationValue = "-1.0 / 1e-20";
            compareOp = `max`;
        }
        let returnValue = `${reduceType}(${reduceType}(${reduceType}(` + "minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";
        if (reduceType === "sum") returnValue = `sumValue`;
        else if (reduceType === "prod") returnValue = `prodValue`;
        else if (reduceType === "all") returnValue = `allValue`;
        else if (reduceType === "any") returnValue = `anyValue`;
        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
        const windowSizeVec4Remainder = windowSize % 4;
        let updateSnippet = `
      if (${reduceType === "sum"}) {
        sumValue += dot(values, ones);
      } else if (${reduceType === "prod"}) {
        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
        prodValue *= tmp[0] * tmp[1];
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
        if (${reduceType === "min"} || ${reduceType === "max"}) {
          minMaxValue = ${compareOp}(values, minMaxValue);
          bvec4 isNaN = isnan(values);
          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
            minMaxValue = vec4(NAN);
          }
        }
      }
    `;
        let vecType = `vec4`;
        if (reduceType === "all") {
            initializationValue = "1.0";
            updateSnippet = `
        bool reducedAllValue = all(values);
        float floatedReducedAllValue = float(reducedAllValue);
        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
      `;
            vecType = `bvec4`;
        } else if (reduceType === "any") {
            initializationValue = "0.0";
            updateSnippet = `
        bool reducedAnyValue = any(values);
        float floatedReducedAnyValue = float(reducedAnyValue);
        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
      `;
            vecType = `bvec4`;
        }
        let checkOutOfBounds = "";
        if (inSize % windowSize > 0) checkOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return initializationValue;
        }
      `;
        this.userCode = `
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float getValue(int batch, int inIdx) {
        ${checkOutOfBounds}
        return getX(batch, inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        vec4 minMaxValue = vec4(${initializationValue});
        float prodValue = 1.0;
        float sumValue = 0.0;
        float allValue = 1.0;
        float anyValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder === 1}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 2}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 3}) {
          ${vecType} values = ${vecType}(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          ${updateSnippet}
        }
        setOutput(${returnValue});
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"arYj9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transposeImpl", ()=>transposeImpl);
parcelHelpers.export(exports, "transposeImplCPU", ()=>(0, _shared.transposeImplCPU));
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _transposeGpu = require("../transpose_gpu");
var _transposePackedGpu = require("../transpose_packed_gpu");
function transposeImpl(x, perm, backend) {
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new (0, _transposePackedGpu.TransposePackedProgram)(x.shape, perm) : new (0, _transposeGpu.TransposeProgram)(x.shape, perm);
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype);
}

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../transpose_gpu":"ejy1h","../transpose_packed_gpu":"bN4zV","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ejy1h":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "TransposeProgram", ()=>TransposeProgram);
var _shaderCompiler = require("./shader_compiler");
class TransposeProgram {
    constructor(aShape, newDim){
        this.variableNames = [
            "A"
        ];
        const outputShape = new Array(aShape.length);
        for(let i = 0; i < outputShape.length; i++)outputShape[i] = aShape[newDim[i]];
        this.outputShape = outputShape;
        this.rank = outputShape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(this.rank);
        const switched = getSwitchedCoords(newDim);
        this.userCode = `
    void main() {
      ${dtype} resRC = getOutputCoords();
      setOutput(getA(${switched}));
    }
    `;
    }
}
function getSwitchedCoords(newDim) {
    const rank = newDim.length;
    if (rank > 6) throw Error(`Transpose for rank ${rank} is not yet supported`);
    const originalOrder = [
        "resRC.x",
        "resRC.y",
        "resRC.z",
        "resRC.w",
        "resRC.u",
        "resRC.v"
    ];
    const switchedCoords = new Array(rank);
    for(let i = 0; i < newDim.length; i++)switchedCoords[newDim[i]] = originalOrder[i];
    return switchedCoords.join();
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bN4zV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "TransposePackedProgram", ()=>TransposePackedProgram);
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class TransposePackedProgram {
    constructor(aShape, newDim){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        const outputShape = new Array(aShape.length);
        for(let i = 0; i < outputShape.length; i++)outputShape[i] = aShape[newDim[i]];
        this.outputShape = outputShape;
        this.rank = outputShape.length;
        if (this.rank > 6) throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
        const dtype = (0, _shaderCompiler.getCoordsDataType)(this.rank);
        const outputOrder = (0, _packingUtil.getVecChannels)("rc", this.rank);
        const switchedOrder = new Array(this.rank);
        for(let i = 0; i < newDim.length; i++)switchedOrder[newDim[i]] = outputOrder[i];
        const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
        const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
        const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;
        this.userCode = `
    void main() {
      ${dtype} rc = getOutputCoords();
      vec4 result = vec4(0.);
      result[0] = ${getc};
      if(${nextColumn}) {
        result[1] = ${getc};
      }
      --${outputOrder[this.rank - 1]};
      if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {
        result[2] = ${getc};
        if(${nextColumn}) {
          result[3] = ${getc};
        }
      }
      setOutput(result);
    }
    `;
    }
}

},{"./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cKJAC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transpose", ()=>transpose);
parcelHelpers.export(exports, "transposeConfig", ()=>transposeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _transposeImpl = require("./Transpose_impl");
function transpose(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { perm } = attrs;
    const webglBackend = backend;
    const xRank = x.shape.length;
    const newShape = new Array(xRank);
    for(let i = 0; i < newShape.length; i++)newShape[i] = x.shape[perm[i]];
    let out;
    if (webglBackend.shouldExecuteOnCPU([
        x
    ])) {
        const xTexData = webglBackend.texData.get(x.dataId);
        const values = xTexData.values;
        const outValues = (0, _transposeImpl.transposeImplCPU)(values, x.shape, x.dtype, perm, newShape);
        out = webglBackend.makeTensorInfo(newShape, x.dtype);
        const outData = webglBackend.texData.get(out.dataId);
        outData.values = outValues;
    } else out = (0, _transposeImpl.transposeImpl)(x, perm, webglBackend);
    return out;
}
const transposeConfig = {
    kernelName: (0, _tfjsCore.Transpose),
    backendName: "webgl",
    kernelFunc: transpose
};

},{"@tensorflow/tfjs-core":"21Ftl","./Transpose_impl":"arYj9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5sWXh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "abs", ()=>abs);
parcelHelpers.export(exports, "absConfig", ()=>absConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _unaryopGpu = require("../unaryop_gpu");
var _unaryopPackedGpu = require("../unaryop_packed_gpu");
const ABS = `return abs(x);`;
function abs(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    // TODO: handle cases when x is complex. Once the cpu implementation
    // can handle complex values, refactor to use unaryKernelFunc.
    if (backend.shouldExecuteOnCPU([
        x
    ]) && x.dtype !== "complex64") {
        const xData = backend.texData.get(x.dataId);
        const outValues = (0, _shared.simpleAbsImplCPU)(xData.values);
        return backend.makeTensorInfo(x.shape, x.dtype, outValues);
    }
    let program;
    if ((0, _tfjsCore.env)().getBool("WEBGL_PACK_UNARY_OPERATIONS")) program = new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(x.shape, ABS);
    else program = new (0, _unaryopGpu.UnaryOpProgram)(x.shape, ABS);
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype);
}
const absConfig = {
    kernelName: (0, _tfjsCore.Abs),
    backendName: "webgl",
    kernelFunc: abs
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../unaryop_gpu":"gIMrR","../unaryop_packed_gpu":"c4D5K","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"88j8R":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "acos", ()=>acos);
parcelHelpers.export(exports, "acosConfig", ()=>acosConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const ACOS = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`;
const acos = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ACOS
});
const acosConfig = {
    kernelName: (0, _tfjsCore.Acos),
    backendName: "webgl",
    kernelFunc: acos
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3MqlI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "acosh", ()=>acosh);
parcelHelpers.export(exports, "acoshConfig", ()=>acoshConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const ACOSH = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
const acosh = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ACOSH
});
const acoshConfig = {
    kernelName: (0, _tfjsCore.Acosh),
    backendName: "webgl",
    kernelFunc: acosh
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iCeMO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "addKernelFunc", ()=>addKernelFunc);
parcelHelpers.export(exports, "addConfig", ()=>addConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const ADD = "return a + b;";
const addKernelFunc = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: ADD,
    packedOpSnippet: ADD,
    supportsComplex: true,
    cpuKernelImpl: (0, _shared.addImplCPU)
});
const addConfig = {
    kernelName: (0, _tfjsCore.Add),
    backendName: "webgl",
    kernelFunc: addKernelFunc
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"inami":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "addN", ()=>addN);
parcelHelpers.export(exports, "addNConfig", ()=>addNConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _addnGpu = require("../addn_gpu");
var _addnPackedGpu = require("../addn_packed_gpu");
var _identity = require("./Identity");
function addN(args) {
    const { inputs, backend } = args;
    const tensors = inputs;
    if (tensors.length === 1) return (0, _identity.identity)({
        inputs: {
            x: tensors[0]
        },
        backend
    });
    // Limit the number of uploaded textures for optimization.
    if (tensors.length > (0, _tfjsCore.env)().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
        const midIndex = Math.floor(tensors.length / 2);
        const leftSide = addN({
            inputs: tensors.slice(0, midIndex),
            backend
        });
        const rightSide = addN({
            inputs: tensors.slice(midIndex),
            backend
        });
        return addN({
            inputs: [
                leftSide,
                rightSide
            ],
            backend
        });
    }
    const dtype = tensors.map((t)=>t.dtype).reduce((d1, d2)=>(0, _tfjsCore.upcastType)(d1, d2));
    const shapes = tensors.map((t)=>t.shape);
    // We can make sure shapes are identical in op level.
    const usePackedOp = (0, _tfjsCore.env)().getBool("WEBGL_PACK");
    const program = usePackedOp ? new (0, _addnPackedGpu.AddNPackedProgram)(tensors[0].shape, shapes) : new (0, _addnGpu.AddNProgram)(tensors[0].shape, shapes);
    return backend.runWebGLProgram(program, tensors, dtype);
}
const addNConfig = {
    kernelName: (0, _tfjsCore.AddN),
    backendName: "webgl",
    kernelFunc: addN
};

},{"@tensorflow/tfjs-core":"21Ftl","../addn_gpu":"jJYUj","../addn_packed_gpu":"96PNN","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jJYUj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "AddNProgram", ()=>AddNProgram);
class AddNProgram {
    constructor(outputShape, shapes){
        this.outputShape = [];
        this.outputShape = outputShape;
        this.variableNames = shapes.map((_, i)=>`T${i}`);
        const snippets = [];
        // Get target elements from every input tensor.
        this.variableNames.forEach((variable)=>{
            snippets.push(`float v${variable} = get${variable}AtOutCoords();`);
        });
        // Calculate the sum of all elements.
        const operation = this.variableNames.map((variable)=>{
            return `v${variable}`;
        }).join(" + ");
        this.userCode = `
      void main() {
        ${snippets.join("\n        ")}

        float result = ${operation};
        setOutput(result);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"96PNN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "AddNPackedProgram", ()=>AddNPackedProgram);
class AddNPackedProgram {
    constructor(outputShape, shapes){
        this.outputShape = [];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = outputShape;
        this.variableNames = shapes.map((_, i)=>`T${i}`);
        const snippets = [];
        // Get target elements from every input tensor.
        this.variableNames.forEach((variable)=>{
            snippets.push(`vec4 v${variable} = get${variable}AtOutCoords();`);
        });
        // Calculate the sum of all elements.
        const operation = this.variableNames.map((variable)=>{
            return `v${variable}`;
        }).join(" + ");
        this.userCode = `
      void main() {
        ${snippets.join("\n        ")}

        vec4 result = ${operation};
        setOutput(result);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8QrzY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "all", ()=>all);
parcelHelpers.export(exports, "allConfig", ()=>allConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reduce = require("../kernel_utils/reduce");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function all(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    const xRank = x.shape.length;
    const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
        permutedX = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, xRank);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("all", axes, xRank);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(permutedX.shape, axes);
    const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const a2D = (0, _reshape.reshape)({
        inputs: {
            x: permutedX
        },
        backend,
        attrs: {
            shape: [
                -1,
                inSize
            ]
        }
    });
    const reduced = (0, _reduce.reduce)(a2D, a2D.dtype, "all", backend);
    let res;
    if (keepDims) {
        const newShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(outShape, origAxes);
        res = (0, _reshape.reshape)({
            inputs: {
                x: reduced
            },
            backend,
            attrs: {
                shape: newShape
            }
        });
    } else res = (0, _reshape.reshape)({
        inputs: {
            x: reduced
        },
        backend,
        attrs: {
            shape: outShape
        }
    });
    backend.disposeIntermediateTensorInfo(a2D);
    backend.disposeIntermediateTensorInfo(reduced);
    if (permutedAxes != null) backend.disposeIntermediateTensorInfo(permutedX);
    return res;
}
const allConfig = {
    kernelName: (0, _tfjsCore.All),
    backendName: "webgl",
    kernelFunc: all
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reduce":"dr0Xl","./Reshape":"fgZen","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cNRuv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "any", ()=>any);
parcelHelpers.export(exports, "anyConfig", ()=>anyConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reduce = require("../kernel_utils/reduce");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function any(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    const xRank = x.shape.length;
    const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
        permutedX = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, xRank);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("any", axes, xRank);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(permutedX.shape, axes);
    const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const a2D = (0, _reshape.reshape)({
        inputs: {
            x: permutedX
        },
        backend,
        attrs: {
            shape: [
                -1,
                inSize
            ]
        }
    });
    const reduced = (0, _reduce.reduce)(a2D, a2D.dtype, "any", backend);
    let res;
    if (keepDims) {
        const newShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(outShape, origAxes);
        res = (0, _reshape.reshape)({
            inputs: {
                x: reduced
            },
            backend,
            attrs: {
                shape: newShape
            }
        });
    } else res = (0, _reshape.reshape)({
        inputs: {
            x: reduced
        },
        backend,
        attrs: {
            shape: outShape
        }
    });
    backend.disposeIntermediateTensorInfo(a2D);
    backend.disposeIntermediateTensorInfo(reduced);
    if (permutedAxes != null) backend.disposeIntermediateTensorInfo(permutedX);
    return res;
}
const anyConfig = {
    kernelName: (0, _tfjsCore.Any),
    backendName: "webgl",
    kernelFunc: any
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reduce":"dr0Xl","./Reshape":"fgZen","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cxDNq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "argMax", ()=>argMax);
parcelHelpers.export(exports, "argMaxConfig", ()=>argMaxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _argMinMax = require("../kernel_utils/arg_min_max");
var _transpose = require("./Transpose");
function argMax(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis } = attrs;
    let axes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
        $x = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        intermediateTensorInfos.push($x);
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, $x.shape.length);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("argMax", [
        axes[0]
    ], $x.shape.length);
    const out = (0, _argMinMax.argMinMaxReduce)(backend, $x, axes[0], "max");
    intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return out;
}
const argMaxConfig = {
    kernelName: (0, _tfjsCore.ArgMax),
    backendName: "webgl",
    kernelFunc: argMax
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/arg_min_max":"1rsTF","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1rsTF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "argMinMaxReduce", ()=>argMinMaxReduce);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _argminmaxGpu = require("../argminmax_gpu");
var _argminmaxPackedGpu = require("../argminmax_packed_gpu");
var _reshape = require("../kernels/Reshape");
function argReduce(backend, x, reduceType, bestIndicesA = null) {
    let batchSize = x.shape[0];
    let inSize = x.shape[1];
    if (bestIndicesA != null) {
        batchSize = bestIndicesA.shape[0];
        inSize = bestIndicesA.shape[1];
    }
    const windowSize = (0, _tfjsCore.backend_util).computeOptimalWindowSize(inSize);
    const reduceInfo = {
        windowSize,
        inSize,
        batchSize,
        outSize: Math.ceil(inSize / windowSize)
    };
    const program = new (0, _argminmaxGpu.ArgMinMaxProgram)(reduceInfo, reduceType, bestIndicesA == null);
    const inputs = [
        x
    ];
    if (bestIndicesA != null) inputs.push(bestIndicesA);
    const output = backend.runWebGLProgram(program, inputs, "int32");
    // No need to run another GPGPU program.
    if (output.shape[1] === 1) return output;
    const result = argReduce(backend, x, reduceType, output);
    backend.disposeIntermediateTensorInfo(output);
    return result;
}
function argReducePacked(backend, x, reduceType, bestIndicesA = null) {
    const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
    const inSize = inShape[inShape.length - 1];
    const windowSize = (0, _tfjsCore.backend_util).computeOptimalWindowSize(inSize);
    const program = new (0, _argminmaxPackedGpu.ArgMinMaxPackedProgram)(inShape, windowSize, reduceType, bestIndicesA == null);
    const inputs = bestIndicesA == null ? [
        x
    ] : [
        x,
        bestIndicesA
    ];
    const output = backend.runWebGLProgram(program, inputs, "int32");
    if (output.shape.length === x.shape.length) {
        const result = argReducePacked(backend, x, reduceType, output);
        backend.disposeIntermediateTensorInfo(output);
        return result;
    }
    return output;
}
function argMinMaxReduce(backend, x, axis, reduceType) {
    const axes = [
        axis
    ];
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);
    if (!(0, _tfjsCore.env)().getBool("WEBGL_PACK_REDUCE") || x.shape.length <= 2) {
        const intermediateTensorInfos = [];
        // Eagerly unpack x input since it is passed in to all the shaders which
        // require unpacked inputs.
        const xtexData = backend.texData.get(x.dataId);
        const xIsPacked = xtexData !== null && xtexData.isPacked;
        let xUnPacked = x;
        if (xIsPacked) {
            xUnPacked = backend.unpackTensor(x);
            intermediateTensorInfos.push(xUnPacked);
        }
        const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(xUnPacked.shape, axes);
        const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
        const a2D = (0, _reshape.reshape)({
            inputs: {
                x: xUnPacked
            },
            backend,
            attrs: {
                shape: [
                    -1,
                    inSize
                ]
            }
        });
        intermediateTensorInfos.push(a2D);
        const reduced = argReduce(backend, a2D, reduceType);
        intermediateTensorInfos.push(reduced);
        const reshaped = (0, _reshape.reshape)({
            inputs: {
                x: reduced
            },
            backend,
            attrs: {
                shape: outShape
            }
        });
        intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
        return reshaped;
    }
    return argReducePacked(backend, x, reduceType);
}

},{"@tensorflow/tfjs-core":"21Ftl","../argminmax_gpu":"7Vig3","../argminmax_packed_gpu":"1fu9z","../kernels/Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7Vig3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ArgMinMaxProgram", ()=>ArgMinMaxProgram);
class ArgMinMaxProgram {
    constructor(reduceInfo, op, firstPass){
        this.variableNames = [
            "A"
        ];
        const { windowSize, batchSize, outSize } = reduceInfo;
        if (!firstPass) this.variableNames.push("bestIndicesA");
        this.outputShape = [
            batchSize,
            outSize
        ];
        const compOp = op === "max" ? ">" : "<";
        const indexSnippet = firstPass ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
        this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${windowSize};

        int bestIndex = inOffset;
        float bestValue = getA(batch, bestIndex);

        for (int i = 0; i < ${windowSize}; i++) {
          int inIdx = ${indexSnippet};
          float candidate = getA(batch, inIdx);
          if (candidate ${compOp} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1fu9z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ArgMinMaxPackedProgram", ()=>ArgMinMaxPackedProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class ArgMinMaxPackedProgram {
    constructor(shape, windowSize, op, firstPass){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        (0, _tfjsCore.util).assert(shape.length > 2, ()=>`Packed arg${op.charAt(0).toUpperCase() + op.slice(1)} supports only inputs with rank above 2.`);
        const inSize = shape[shape.length - 1];
        const outSize = Math.ceil(inSize / windowSize);
        this.outputShape = shape.slice(0, -1);
        if (outSize > 1) this.outputShape.push(outSize);
        if (!firstPass) this.variableNames.push("bestIndicesA");
        const outShape = this.outputShape;
        const rank = outShape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
        const coords = (0, _packingUtil.getChannels)("coords", rank);
        let sourceLocSetup;
        let sourceRank;
        if (outSize === 1) {
            sourceRank = rank + 1;
            const sourceLocDType = (0, _shaderCompiler.getCoordsDataType)(sourceRank);
            sourceLocSetup = `
        ${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords.join()}, 0);
        ++${coords[rank - 1]};
        ${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords.join()}, 0);
        ++${coords[rank - 2]};
        ${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords.join()}, 0);
        --${coords[rank - 1]};
        ${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords.join()}, 0);
        --${coords[rank - 2]};`;
        } else {
            sourceRank = rank;
            sourceLocSetup = `
        ${dtype} sourceLocR = coords;
        ++${coords[rank - 1]};
        ${dtype} sourceLocG = coords;
        ++${coords[rank - 2]};
        ${dtype} sourceLocA = coords;
        --${coords[rank - 1]};
        ${dtype} sourceLocB = coords;
        --${coords[rank - 2]};`;
        }
        const channels = [
            "x",
            "y",
            "z",
            "w",
            "u",
            "v"
        ].slice(0, sourceRank);
        const inChannel = "." + channels[sourceRank - 1]; // e.g. ".b" for rank 3.
        const intChannels = channels.map((x)=>"int " + x);
        const srcRCoords = (0, _packingUtil.getChannels)("sourceLocR", sourceRank - 1).concat("inIdx.r");
        const srcGCoords = (0, _packingUtil.getChannels)("sourceLocG", sourceRank - 1).concat("inIdx.g");
        const srcBCoords = (0, _packingUtil.getChannels)("sourceLocB", sourceRank - 1).concat("inIdx.b");
        const srcACoords = (0, _packingUtil.getChannels)("sourceLocA", sourceRank - 1).concat("inIdx.a");
        const compOp = op === "max" ? "greaterThan" : "lessThan";
        const fetchCandidateIdx = firstPass ? "" : `
          inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
                             getBestIndicesAChannel(${srcGCoords.join()}),
                             getBestIndicesAChannel(${srcBCoords.join()}),
                             getBestIndicesAChannel(${srcACoords.join()})));`;
        const fetchValue = `vec4(
            getAChannel(${srcRCoords.join()}),
            hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
            hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
            hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;
        const getBestIndicesAChannelSnippet = firstPass ? "" : `
      float getBestIndicesAChannel(${intChannels.join()}) {
        return getChannel(getBestIndicesA(${channels.join()}),
                                          vec2(${channels.slice(-2).join()}));
      }`;
        this.userCode = `
      float getAChannel(${intChannels.join()}) {
        return getChannel(getA(${channels.join()}),
                               vec2(${channels.slice(-2).join()}));
      }
      ${getBestIndicesAChannelSnippet}
      void main() {
        ${dtype} coords = getOutputCoords();
        bool hasNextCol = ${coords[rank - 1]} < ${outShape[rank - 1] - 1};
        bool hasNextRow = ${coords[rank - 2]} < ${outShape[rank - 2] - 1};
        ${sourceLocSetup}
        ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
          sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
        ivec4 inIdx = srcIdx;
        vec4 bestIndex = vec4(inIdx);
        vec4 bestValue = ${fetchValue};

        for (int i = 0; i < ${windowSize}; i++) {
          inIdx = srcIdx;
          ${fetchCandidateIdx}
          vec4 candidate = ${fetchValue};
          bvec4 nan = isnan(candidate);
          bvec4 replace = bvec4(
            vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));

          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,
                           replace.y  ? candidate.y : bestValue.y,
                           replace.z  ? candidate.z : bestValue.z,
                           replace.w  ? candidate.w : bestValue.w);
          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
          srcIdx++;
        }
        setOutput(bestIndex);
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7we0s":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "argMin", ()=>argMin);
parcelHelpers.export(exports, "argMinConfig", ()=>argMinConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _argMinMax = require("../kernel_utils/arg_min_max");
var _transpose = require("./Transpose");
function argMin(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis } = attrs;
    let axes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
        $x = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        intermediateTensorInfos.push($x);
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, $x.shape.length);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("argMin", [
        axes[0]
    ], $x.shape.length);
    const out = (0, _argMinMax.argMinMaxReduce)(backend, $x, axes[0], "min");
    intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return out;
}
const argMinConfig = {
    kernelName: (0, _tfjsCore.ArgMin),
    backendName: "webgl",
    kernelFunc: argMin
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/arg_min_max":"1rsTF","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2y04O":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "asin", ()=>asin);
parcelHelpers.export(exports, "asinConfig", ()=>asinConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const ASIN = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`;
const asin = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ASIN
});
const asinConfig = {
    kernelName: (0, _tfjsCore.Asin),
    backendName: "webgl",
    kernelFunc: asin
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8uUU0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "asinh", ()=>asinh);
parcelHelpers.export(exports, "asinhConfig", ()=>asinhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const ASINH = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `return log(x + sqrt(x * x + 1.0));`;
const asinh = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ASINH
});
const asinhConfig = {
    kernelName: (0, _tfjsCore.Asinh),
    backendName: "webgl",
    kernelFunc: asinh
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hh4aJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atan", ()=>atan);
parcelHelpers.export(exports, "atanConfig", ()=>atanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const ATAN = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  return atan(x);
`;
const atan = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ATAN
});
const atanConfig = {
    kernelName: (0, _tfjsCore.Atan),
    backendName: "webgl",
    kernelFunc: atan
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"k9THK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atan2", ()=>atan2);
parcelHelpers.export(exports, "atan2Config", ()=>atan2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const ATAN2 = (0, _binaryopGpu.CHECK_NAN_SNIPPET) + `
  return atan(a, b);
`;
const ATAN2_PACKED = `
  vec4 result = atan(a, b);
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  ` + (0, _binaryopPackedGpu.CHECK_NAN_SNIPPET_PACKED) + `
  return result;
`;
const atan2 = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: ATAN2,
    packedOpSnippet: ATAN2_PACKED
});
const atan2Config = {
    kernelName: (0, _tfjsCore.Atan2),
    backendName: "webgl",
    kernelFunc: atan2
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3axXM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atanh", ()=>atanh);
parcelHelpers.export(exports, "atanhConfig", ()=>atanhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const ATANH = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
const atanh = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ATANH
});
const atanhConfig = {
    kernelName: (0, _tfjsCore.Atanh),
    backendName: "webgl",
    kernelFunc: atanh
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iU9nc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool", ()=>avgPool);
parcelHelpers.export(exports, "avgPoolConfig", ()=>avgPoolConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _poolGpu = require("../pool_gpu");
var _webglUtil = require("../webgl_util");
var _identity = require("./Identity");
function avgPool(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    (0, _webglUtil.assertNotComplex)(x, "avgPool");
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const dilations = 1;
    (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in avgPool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && (0, _tfjsCore.util).arraysEqual(convInfo.inShape, convInfo.outShape)) return (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    const avgPoolProgram = new (0, _poolGpu.Pool2DProgram)(convInfo, "avg", false);
    return backend.runWebGLProgram(avgPoolProgram, [
        x
    ], "float32");
}
const avgPoolConfig = {
    kernelName: (0, _tfjsCore.AvgPool),
    backendName: "webgl",
    kernelFunc: avgPool
};

},{"@tensorflow/tfjs-core":"21Ftl","../pool_gpu":"b1t1V","../webgl_util":"fjf7t","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b1t1V":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Pool2DProgram", ()=>Pool2DProgram);
parcelHelpers.export(exports, "Pool3DProgram", ()=>Pool3DProgram);
class Pool2DProgram {
    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false){
        this.variableNames = [
            "x"
        ];
        if (poolType === "avg" && computePositions) throw new Error("Cannot compute positions for average pool.");
        const filterWidth = convInfo.filterWidth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationHeight = convInfo.dilationHeight;
        const dilationWidth = convInfo.dilationWidth;
        const effectiveFilterHeight = convInfo.effectiveFilterHeight;
        const effectiveFilterWidth = convInfo.effectiveFilterWidth;
        const padTop = convInfo.padInfo.top;
        const padLeft = convInfo.padInfo.left;
        this.outputShape = convInfo.outShape;
        const isAvgPool = poolType === "avg";
        const batchFlattenPositionStr = `((batch  * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
        const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
        let initializationValue = "0.0";
        if (!isAvgPool) // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
        initializationValue = "-1.0 / 1e-20";
        if (computePositions) {
            const compareOp = ">=";
            this.userCode = `
        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
        const ivec2 pads = ivec2(${padTop}, ${padLeft});

        void main() {
          ivec4 coords = getOutputCoords();
          int batch = coords[0];
          int d = coords[3];

          ivec2 xRCCorner = coords.yz * strides - pads;
          int xRCorner = xRCCorner.x;
          int xCCorner = xRCCorner.y;

          // max/min x(?, ?, d) to get y(yR, yC, d).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;
          float avgValue = 0.0;

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              int xC = xCCorner + wC;

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              float value = getX(batch, xR, xC, d);

              // If a min / max value has already been found, use it. If not,
              // use the current value.
              float currMinMaxValue = mix(
                  value, minMaxValue, minMaxValueFound);
              if (value ${compareOp} currMinMaxValue) {
                minMaxValue = value;
                minMaxValueFound = 1.0;
                minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
            return;
        }
        const compareOp = "max";
        let returnValue = `${poolType}(${poolType}(${poolType}(` + "minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";
        if (poolType === "avg") returnValue = `avgValue / max(count, 1.0)`;
        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
        const filterWidthVec4Remainder = filterWidth % 4;
        const updateSnippet = `
      if (${isAvgPool}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `;
        this.userCode = `
      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xR, int xC, int d) {
        if (xC < 0 || xC >= ${convInfo.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xR, xC, d);
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d = coords[3];

        ivec2 xRCCorner = coords.yz * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // max/min x(?, ?, d) to get y(yR, yC, d).
        // ? = to be determined
        vec4 minMaxValue = vec4(${initializationValue});
        float avgValue = 0.0;
        count = 0.0;

        for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
          int xR = xRCorner + wR;

          if (xR < 0 || xR >= ${convInfo.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
            int xC = xCCorner + wC * ${dilationWidth};

            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
            );

            ${updateSnippet}
          }

          int xC = xCCorner + ${filterWidthNearestVec4};
          if (${filterWidthVec4Remainder === 1}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              initializationValue,
              initializationValue,
              initializationValue
            );

            ${updateSnippet}
          } else if (${filterWidthVec4Remainder === 2}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              initializationValue,
              initializationValue
            );

            ${updateSnippet}
          } else if (${filterWidthVec4Remainder === 3}) {
            vec4 values = vec4(
              getValue(batch, xR, xC, d),
              getValue(batch, xR, xC + ${dilationWidth}, d),
              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
              initializationValue
            );

            ${updateSnippet}
          }
        }
        setOutput(${returnValue});
      }
    `;
    }
}
class Pool3DProgram {
    constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false){
        this.variableNames = [
            "x"
        ];
        if (poolType === "avg" && computePositions) throw new Error("Cannot compute positions for average pool.");
        const filterWidth = convInfo.filterWidth;
        const strideDepth = convInfo.strideDepth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationDepth = convInfo.dilationDepth;
        const dilationHeight = convInfo.dilationHeight;
        const dilationWidth = convInfo.dilationWidth;
        const effectiveFilterDepth = convInfo.effectiveFilterDepth;
        const effectiveFilterHeight = convInfo.effectiveFilterHeight;
        const effectiveFilterWidth = convInfo.effectiveFilterWidth;
        const padFront = convInfo.padInfo.front;
        const padTop = convInfo.padInfo.top;
        const padLeft = convInfo.padInfo.left;
        this.outputShape = convInfo.outShape;
        const isAvgPool = poolType === "avg";
        let initializationValue = "0.0";
        if (!isAvgPool) // WebGL on Firefox Linux can't compile 1/0 so we do 1/eps.
        initializationValue = "-1.0 / 1e-20";
        if (computePositions) {
            const compareOp = ">=";
            this.userCode = `
        const ivec3 strides =
            ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
        const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

        void main() {
          ivec5 coords = getOutputCoords();
          int batch = coords.x;
          int ch = coords.u;

          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
          int xDCorner = xCorner.x;
          int xRCorner = xCorner.y;
          int xCCorner = xCorner.z;

          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
          // ? = to be determined
          float minMaxValue = 0.0;
          float minMaxValueFound = 0.0;
          int minMaxPosition = 0;

          for (int wD = 0; wD < ${effectiveFilterDepth};
              wD += ${dilationDepth}) {
            int xD = xDCorner + wD;

            if (xD < 0 || xD >= ${convInfo.inDepth}) {
              continue;
            }

            for (int wR = 0; wR < ${effectiveFilterHeight};
                wR += ${dilationHeight}) {
              int xR = xRCorner + wR;

              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                continue;
              }

              for (int wC = 0; wC < ${effectiveFilterWidth};
                  wC += ${dilationWidth}) {
                int xC = xCCorner + wC;

                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                  continue;
                }

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${compareOp} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
                      wR * ${effectiveFilterWidth} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
            return;
        }
        const compareOp = "max";
        let returnValue = `${poolType}(${poolType}(${poolType}(` + "minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";
        if (poolType === "avg") // Use `max(count, 1.0)` instead of `count` in case count === 0.0.
        // If count === 0.0, `avgValue` is always 0.0 and we change `count`'s
        // value to avoid dividing zero.
        returnValue = `avgValue / max(count, 1.0)`;
        const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
        const filterWidthVec4Remainder = filterWidth % 4;
        const updateSnippet = `
      if (${isAvgPool}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${compareOp}(values, minMaxValue);
      }
    `;
        this.userCode = `
      const ivec3 strides =
        ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
      const float initializationValue = ${initializationValue};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

      float getValue(int batch, int xD, int xR, int xC, int ch) {
        if (xC < 0 || xC >= ${convInfo.inWidth}) {
          return initializationValue;
        }
        count += 1.0;
        return getX(batch, xD, xR, xC, ch);
      }

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xDCorner = xCorner.x;
        int xRCorner = xCorner.y;
        int xCCorner = xCorner.z;

        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
        // ? = to be determined
        vec4 minMaxValue = vec4(${initializationValue});
        float avgValue = 0.0;
        count = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
            wD += ${dilationDepth}) {
          int xD = xDCorner + wD;

          if (xD < 0 || xD >= ${convInfo.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
            int xR = xRCorner + wR;

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
              int xC = xCCorner + wC * ${dilationWidth};

              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
              );

              ${updateSnippet}
            }

            int xC = xCCorner + ${filterWidthNearestVec4};
            if (${filterWidthVec4Remainder === 1}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                initializationValue,
                initializationValue,
                initializationValue
              );

              ${updateSnippet}
            } else if (${filterWidthVec4Remainder === 2}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                initializationValue,
                initializationValue
              );

              ${updateSnippet}
            } else if (${filterWidthVec4Remainder === 3}) {
              vec4 values = vec4(
                getValue(batch, xD, xR, xC, ch),
                getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
                getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
                initializationValue
              );

              ${updateSnippet}
            }
          }
        }
        setOutput(${returnValue});
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dFK3l":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool3D", ()=>avgPool3D);
parcelHelpers.export(exports, "avgPool3DConfig", ()=>avgPool3DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _poolGpu = require("../pool_gpu");
function avgPool3D(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { filterSize, strides, pad, dimRoundingMode, dataFormat } = attrs;
    const dilations = [
        1,
        1,
        1
    ];
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);
    const avgPoolProgram = new (0, _poolGpu.Pool3DProgram)(convInfo, "avg", false);
    return backend.runWebGLProgram(avgPoolProgram, [
        x
    ], "float32");
}
const avgPool3DConfig = {
    kernelName: (0, _tfjsCore.AvgPool3D),
    backendName: "webgl",
    kernelFunc: avgPool3D
};

},{"@tensorflow/tfjs-core":"21Ftl","../pool_gpu":"b1t1V","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kSFm4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool3DGrad", ()=>avgPool3DGrad);
parcelHelpers.export(exports, "avgPool3DGradConfig", ()=>avgPool3DGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _avgPoolBackpropGpu = require("../avg_pool_backprop_gpu");
function avgPool3DGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input } = inputs;
    const x = input;
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const dilations = [
        1,
        1,
        1
    ];
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
    const avgPoolBackpropProgram = new (0, _avgPoolBackpropGpu.AvgPool3DBackpropProgram)(convInfo);
    return backend.runWebGLProgram(avgPoolBackpropProgram, [
        dy
    ], x.dtype);
}
const avgPool3DGradConfig = {
    kernelName: (0, _tfjsCore.AvgPool3DGrad),
    backendName: "webgl",
    kernelFunc: avgPool3DGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../avg_pool_backprop_gpu":"6KYh8","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6KYh8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "AvgPool2DBackpropProgram", ()=>AvgPool2DBackpropProgram);
parcelHelpers.export(exports, "AvgPool3DBackpropProgram", ()=>AvgPool3DBackpropProgram);
class AvgPool2DBackpropProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy"
        ];
        this.outputShape = convInfo.inShape;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationHeight = convInfo.dilationHeight;
        const dilationWidth = convInfo.dilationWidth;
        const effectiveFilterHeight = convInfo.effectiveFilterHeight;
        const effectiveFilterWidth = convInfo.effectiveFilterWidth;
        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
        const avgMultiplier = 1 / (filterHeight * filterWidth);
        this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float avgMultiplier = float(${avgMultiplier});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${effectiveFilterHeight};
            wR += ${dilationHeight}) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${effectiveFilterWidth};
            wC+= ${dilationWidth}) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);

            dotProd += dyValue * avgMultiplier;
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}
class AvgPool3DBackpropProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy"
        ];
        this.outputShape = convInfo.inShape;
        const filterDepth = convInfo.filterDepth;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const strideDepth = convInfo.strideDepth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationDepth = convInfo.dilationDepth;
        const dilationHeight = convInfo.dilationHeight;
        const dilationWidth = convInfo.dilationWidth;
        const effectiveFilterDepth = convInfo.effectiveFilterDepth;
        const effectiveFilterHeight = convInfo.effectiveFilterHeight;
        const effectiveFilterWidth = convInfo.effectiveFilterWidth;
        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
        const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
        this.userCode = `
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
      const float avgMultiplier = float(${avgMultiplier});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
            wD += ${dilationDepth}) {
          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;

          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);

              dotProd += dyValue * avgMultiplier;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dwrYJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPoolGrad", ()=>avgPoolGrad);
parcelHelpers.export(exports, "avgPoolGradConfig", ()=>avgPoolGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _avgPoolBackpropGpu = require("../avg_pool_backprop_gpu");
var _webglUtil = require("../webgl_util");
function avgPoolGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input } = inputs;
    const x = input;
    (0, _webglUtil.assertNotComplex)([
        dy,
        input
    ], "avgPoolGrad");
    const { filterSize, strides, pad } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */ , pad);
    const avgPoolBackpropProgram = new (0, _avgPoolBackpropGpu.AvgPool2DBackpropProgram)(convInfo);
    return backend.runWebGLProgram(avgPoolBackpropProgram, [
        dy
    ], x.dtype);
}
const avgPoolGradConfig = {
    kernelName: (0, _tfjsCore.AvgPoolGrad),
    backendName: "webgl",
    kernelFunc: avgPoolGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../avg_pool_backprop_gpu":"6KYh8","../webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9tzUH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchMatMul", ()=>batchMatMul);
parcelHelpers.export(exports, "batchMatMulConfig", ()=>batchMatMulConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _batchMatMulImpl = require("./BatchMatMul_impl");
function batchMatMul(args) {
    const { inputs, backend, attrs } = args;
    const { a, b } = inputs;
    const { transposeA, transposeB } = attrs;
    return (0, _batchMatMulImpl.batchMatMulImpl)({
        a,
        b,
        transposeA,
        transposeB,
        backend
    });
}
const batchMatMulConfig = {
    kernelName: (0, _tfjsCore.BatchMatMul),
    backendName: "webgl",
    kernelFunc: batchMatMul
};

},{"@tensorflow/tfjs-core":"21Ftl","./BatchMatMul_impl":"kyEQG","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2u2bT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchNorm", ()=>batchNorm);
parcelHelpers.export(exports, "batchNormConfig", ()=>batchNormConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _batchnormGpu = require("../batchnorm_gpu");
var _batchnormPackedGpu = require("../batchnorm_packed_gpu");
const batchNorm = ({ inputs, backend, attrs })=>{
    const { x, mean, variance, offset, scale } = inputs;
    (0, _tfjsCore.util).assert(mean.shape.length === variance.shape.length, ()=>"Batch normalization gradient requires mean and variance to have equal ranks.");
    (0, _tfjsCore.util).assert(offset == null || mean.shape.length === offset.shape.length, ()=>"Batch normalization gradient requires mean and offset to have equal ranks.");
    (0, _tfjsCore.util).assert(scale == null || mean.shape.length === scale.shape.length, ()=>"Batch normalization gradient requires mean and scale to have equal ranks.");
    let { varianceEpsilon } = attrs;
    if (varianceEpsilon == null) varianceEpsilon = 0.001;
    const finalInputs = [
        x,
        mean,
        variance
    ];
    let offsetShape = null;
    if (offset != null) {
        offsetShape = offset.shape;
        finalInputs.push(offset);
    }
    let scaleShape = null;
    if (scale != null) {
        scaleShape = scale.shape;
        finalInputs.push(scale);
    }
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_NORMALIZATION") ? new (0, _batchnormPackedGpu.BatchNormPackedProgram)(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new (0, _batchnormGpu.BatchNormProgram)(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
    const output = backend.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);
    return output;
};
const batchNormConfig = {
    kernelName: (0, _tfjsCore.FusedBatchNorm),
    backendName: "webgl",
    kernelFunc: batchNorm
};

},{"@tensorflow/tfjs-core":"21Ftl","../batchnorm_gpu":"bs6T4","../batchnorm_packed_gpu":"eFA7W","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bs6T4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "BatchNormProgram", ()=>BatchNormProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
class BatchNormProgram {
    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon){
        this.outputShape = [];
        this.variableNames = [
            "x",
            "mean",
            "variance"
        ];
        (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, meanShape);
        (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, varianceShape);
        let offsetSnippet = "0.0";
        if (offsetShape != null) {
            (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, offsetShape);
            this.variableNames.push("offset");
            offsetSnippet = "getOffsetAtOutCoords()";
        }
        let scaleSnippet = "1.0";
        if (scaleShape != null) {
            (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, scaleShape);
            this.variableNames.push("scale");
            scaleSnippet = "getScaleAtOutCoords()";
        }
        this.outputShape = xShape;
        this.userCode = `
      void main() {
        float x = getXAtOutCoords();
        float mean = getMeanAtOutCoords();
        float variance = getVarianceAtOutCoords();
        float offset = ${offsetSnippet};
        float scale = ${scaleSnippet};
        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eFA7W":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "BatchNormPackedProgram", ()=>BatchNormPackedProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
class BatchNormPackedProgram {
    constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon){
        this.packedInputs = true;
        this.packedOutput = true;
        this.variableNames = [
            "x",
            "mean",
            "variance"
        ];
        (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, meanShape);
        (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, varianceShape);
        let offsetSnippet = "vec4(0.0)";
        if (offsetShape != null) {
            (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, offsetShape);
            this.variableNames.push("offset");
            offsetSnippet = "getOffsetAtOutCoords()";
        }
        let scaleSnippet = "vec4(1.0)";
        if (scaleShape != null) {
            (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(xShape, scaleShape);
            this.variableNames.push("scale");
            scaleSnippet = "getScaleAtOutCoords()";
        }
        this.outputShape = xShape;
        this.userCode = `
      void main() {
        vec4 offset = ${offsetSnippet};
        vec4 scale = ${scaleSnippet};

        vec4 x = getXAtOutCoords();
        vec4 mean = getMeanAtOutCoords();
        vec4 variance = getVarianceAtOutCoords();

        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));

        setOutput((x - mean) * inv + offset);
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bsQ5u":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchToSpaceND", ()=>batchToSpaceND);
parcelHelpers.export(exports, "batchToSpaceNDConfig", ()=>batchToSpaceNDConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reshape = require("./Reshape");
var _slice = require("./Slice");
var _transpose = require("./Transpose");
const batchToSpaceND = (args)=>{
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { blockShape, crops } = attrs;
    (0, _tfjsCore.util).assert(x.shape.length <= 4, ()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
    const prod = blockShape.reduce((a, b)=>a * b);
    const reshaped = (0, _tfjsCore.backend_util).getReshaped(x.shape, blockShape, prod);
    const permuted = (0, _tfjsCore.backend_util).getPermuted(reshaped.length, blockShape.length);
    const reshapedPermuted = (0, _tfjsCore.backend_util).getReshapedPermuted(x.shape, blockShape, prod);
    const sliceBeginCoords = (0, _tfjsCore.backend_util).getSliceBeginCoords(crops, blockShape.length);
    const sliceSize = (0, _tfjsCore.backend_util).getSliceSize(reshapedPermuted, crops, blockShape.length);
    const toDispose = [];
    const reshapedIntermediate = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: reshaped
        }
    });
    const transposedIntermediate = (0, _transpose.transpose)({
        inputs: {
            x: reshapedIntermediate
        },
        backend,
        attrs: {
            perm: permuted
        }
    });
    const reshapedIntermediate2 = (0, _reshape.reshape)({
        inputs: {
            x: transposedIntermediate
        },
        backend,
        attrs: {
            shape: reshapedPermuted
        }
    });
    const sliced = (0, _slice.slice)({
        inputs: {
            x: reshapedIntermediate2
        },
        backend,
        attrs: {
            begin: sliceBeginCoords,
            size: sliceSize
        }
    });
    toDispose.push(reshapedIntermediate);
    toDispose.push(transposedIntermediate);
    toDispose.push(reshapedIntermediate2);
    toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return sliced;
};
const batchToSpaceNDConfig = {
    kernelName: (0, _tfjsCore.BatchToSpaceND),
    backendName: "webgl",
    kernelFunc: batchToSpaceND
};

},{"@tensorflow/tfjs-core":"21Ftl","./Reshape":"fgZen","./Slice":"6jQ29","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6jQ29":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "slice", ()=>slice);
parcelHelpers.export(exports, "sliceConfig", ()=>sliceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _sliceGpu = require("../slice_gpu");
var _slicePackedGpu = require("../slice_packed_gpu");
function shallowSlice(x, begin, size, backend) {
    const xTexData = backend.texData.get(x.dataId);
    const t = backend.makeTensorInfo(size, x.dtype);
    const newTexData = backend.texData.get(t.dataId);
    // Copy texture data from the original tensor.
    Object.assign(newTexData, xTexData);
    newTexData.refCount = 1;
    newTexData.shape = size;
    newTexData.dtype = x.dtype;
    let flatOffset = (0, _tfjsCore.slice_util).computeFlatOffset(begin, (0, _tfjsCore.util).computeStrides(x.shape));
    if (xTexData.slice) // We are slicing an already sliced tensor, so we have to accumulate
    // the offset.
    flatOffset += xTexData.slice.flatOffset;
    newTexData.slice = {
        flatOffset,
        // Point to the original dataId, which is used to do ref counting.
        origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
    };
    // Increase the ref count for that data bucket.
    const refCount = backend.dataRefCount.get(newTexData.slice.origDataId) || 1;
    backend.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
    return t;
}
function slice(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { begin, size } = attrs;
    const [$begin, $size] = (0, _tfjsCore.slice_util).parseSliceParams(x, begin, size);
    (0, _tfjsCore.slice_util).assertParamsValid(x, $begin, $size);
    if ((0, _tfjsCore.util).sizeFromShape($size) === 0) return backend.makeTensorInfo($size, x.dtype, []);
    // Run on cpu if dtype is string. For string, the backend represents it
    // as Uint8Array[], where each Uint8Array is a character. Given that the
    // computation is only on the outer array, uploading the whole data onto
    // gpu is wasteful. Also, currently webgl doesn't have a design to
    // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we
    // just run the kernel on cpu if dtype is string.
    if (backend.shouldExecuteOnCPU([
        x
    ]) || x.dtype === "string") {
        const xTexData = backend.texData.get(x.dataId);
        const outValues = (0, _shared.sliceImplCPU)(xTexData.values, $begin, $size, x.shape, x.dtype);
        return backend.makeTensorInfo($size, x.dtype, outValues);
    }
    const { isPacked } = backend.texData.get(x.dataId);
    const isContinous = (0, _tfjsCore.slice_util).isSliceContinous(x.shape, $begin, $size);
    if (isPacked || !isContinous) {
        const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new (0, _slicePackedGpu.SlicePackedProgram)($size) : new (0, _sliceGpu.SliceProgram)($size);
        const customValues = [
            $begin
        ];
        return backend.runWebGLProgram(program, [
            x
        ], x.dtype, customValues);
    }
    backend.uploadToGPU(x.dataId);
    return shallowSlice(x, $begin, $size, backend);
}
const sliceConfig = {
    kernelName: (0, _tfjsCore.Slice),
    backendName: "webgl",
    kernelFunc: slice
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../slice_gpu":"5kdG6","../slice_packed_gpu":"grMHc","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5kdG6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SliceProgram", ()=>SliceProgram);
var _shaderCompiler = require("./shader_compiler");
class SliceProgram {
    constructor(destSize){
        this.variableNames = [
            "source"
        ];
        this.outputShape = destSize;
        this.rank = destSize.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(this.rank);
        this.customUniforms = [
            {
                name: "start",
                arrayIndex: this.rank,
                type: "int"
            }
        ];
        const sourceCoords = getCoords(this.rank);
        let body;
        const coordSum = destSize.map((_, i)=>{
            return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;
        });
        body = `
        ${dtype} sourceLoc;
        ${dtype} coords = getOutputCoords();
        ${coordSum.join("\n")}
      `;
        this.userCode = `
      void main() {
        ${body}
        setOutput(getSource(${sourceCoords}));
      }
    `;
    }
}
const coords = [
    "x",
    "y",
    "z",
    "w",
    "u",
    "v"
];
function getCoords(rank) {
    if (rank === 1) return "sourceLoc";
    else if (rank <= 6) return coords.slice(0, rank).map((x)=>"sourceLoc." + x).join(",");
    else throw Error(`Slicing for rank ${rank} is not yet supported`);
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"grMHc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SlicePackedProgram", ()=>SlicePackedProgram);
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class SlicePackedProgram {
    constructor(destSize){
        this.variableNames = [
            "source"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = destSize;
        this.rank = destSize.length;
        this.customUniforms = [
            {
                name: "start",
                arrayIndex: this.rank,
                type: "int"
            }
        ];
        const dtype = (0, _shaderCompiler.getCoordsDataType)(this.rank);
        const coords = (0, _packingUtil.getChannels)("coords", this.rank);
        const sourceLoc = (0, _packingUtil.getChannels)("sourceLoc", this.rank);
        const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`;
        const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;
        const upperRow = `
      result.x = ${getChannel};
      if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {
        ++${sourceLoc[this.rank - 1]};
        result.y = ${getChannel};
        --${sourceLoc[this.rank - 1]};
      }
    `;
        const lowerRow = this.rank === 1 ? "" : `
      --${coords[this.rank - 1]};
      if (++${coords[this.rank - 2]} < ${destSize[this.rank - 2]}) {
        ++${sourceLoc[this.rank - 2]};
        result.z = ${getChannel};
        if (++${coords[this.rank - 1]} < ${destSize[this.rank - 1]}) {
          ++${sourceLoc[this.rank - 1]};
          result.w = ${getChannel};
        }
      }
    `;
        const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +
            ${dtype}(${destSize.map((_, i)=>`start[${i}]`).join()});` : destSize.map((_, i)=>`${sourceLoc[i]} = ${coords[i]} + start[${i}];`).join("\n");
        this.userCode = `
      void main() {
        ${dtype} coords = getOutputCoords();
        ${dtype} sourceLoc;
        ${sourceLocSetup}
        vec4 result = vec4(0.);
        ${upperRow}
        ${lowerRow}
        setOutput(result);
      }
    `;
    }
}

},{"./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7xVX3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "bincount", ()=>bincount);
parcelHelpers.export(exports, "bincountConfig", ()=>bincountConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function bincount(args) {
    const { inputs, backend, attrs } = args;
    const { x, weights } = inputs;
    const { size } = attrs;
    const xVals = backend.readSync(x.dataId);
    const weightsVals = backend.readSync(weights.dataId);
    const outVals = (0, _shared.bincountImplCPU)(xVals, weightsVals, weights.dtype, weights.shape, size);
    return backend.makeTensorInfo([
        size
    ], weights.dtype, outVals);
}
const bincountConfig = {
    kernelName: (0, _tfjsCore.Bincount),
    backendName: "webgl",
    kernelFunc: bincount
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"87LwA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "BITWISEAND", ()=>BITWISEAND);
parcelHelpers.export(exports, "BITWISEAND_UNPACKED", ()=>BITWISEAND_UNPACKED);
parcelHelpers.export(exports, "bitwiseAnd", ()=>bitwiseAnd);
parcelHelpers.export(exports, "bitwiseAndConfig", ()=>bitwiseAndConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _shared = require("../kernel_utils/shared");
const BITWISEAND = `
  int r = int(a.r) & int(b.r);
  int g = int(a.g) & int(b.g);
  int rb = int(a.b) & int(b.b);
  int ra = int(a.a) & int(b.a);
  return vec4(r, g, rb, ra);
`;
const BITWISEAND_UNPACKED = `
  return float(int(a.r) & int(b.r));
`;
function bitwiseAnd(args) {
    const { inputs, backend } = args;
    const { a, b } = inputs;
    const shouldUsePackedProgram = (0, _tfjsCore.env)().getBool("WEBGL_PACK_BINARY_OPERATIONS");
    const versionNumber = (0, _tfjsCore.env)().getNumber("WEBGL_VERSION");
    // The type of a and b are ensured to be `int32` in core, therefore no need to
    // consider other type situations.
    if (backend.shouldExecuteOnCPU([
        a,
        b
    ]) || versionNumber === 1) {
        const aVals = backend.texData.get(a.dataId).values;
        const bVals = backend.texData.get(b.dataId).values;
        const [outValues, outShape] = (0, _shared.bitwiseAndImplCPU)(a.shape, b.shape, aVals, bVals, a.dtype);
        const out = backend.makeTensorInfo(outShape, a.dtype);
        const outData = backend.texData.get(out.dataId);
        outData.values = outValues;
        return out;
    }
    let program;
    if (shouldUsePackedProgram) program = new (0, _binaryopPackedGpu.BinaryOpPackedProgram)(BITWISEAND, a.shape, b.shape, false);
    else program = new (0, _binaryopGpu.BinaryOpProgram)(BITWISEAND_UNPACKED, a.shape, b.shape);
    return backend.runWebGLProgram(program, [
        a,
        b
    ], a.dtype);
}
const bitwiseAndConfig = {
    kernelName: (0, _tfjsCore.BitwiseAnd),
    backendName: "webgl",
    kernelFunc: bitwiseAnd
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9Zlag":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "broadcastArgs", ()=>broadcastArgs);
parcelHelpers.export(exports, "broadcastArgsConfig", ()=>broadcastArgsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function broadcastArgs(args) {
    const { inputs, backend } = args;
    const { s0, s1 } = inputs;
    const s0Vals = backend.readSync(s0.dataId);
    const s1Vals = backend.readSync(s1.dataId);
    const broadcastShape = (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
    return backend.makeTensorInfo([
        broadcastShape.length
    ], "int32", Int32Array.from(broadcastShape));
}
const broadcastArgsConfig = {
    kernelName: (0, _tfjsCore.BroadcastArgs),
    backendName: "webgl",
    kernelFunc: broadcastArgs
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c2JG9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cast", ()=>cast);
parcelHelpers.export(exports, "castConfig", ()=>castConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _complex = require("./Complex");
var _identity = require("./Identity");
var _notEqual = require("./NotEqual");
var _real = require("./Real");
var _int = require("../kernel_utils/int");
function cast(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { dtype } = attrs;
    // Casting to complex64.
    if (dtype === "complex64") {
        if (x.dtype === "complex64") return (0, _identity.identity)({
            inputs: {
                x
            },
            backend
        });
        // TODO(annxingyuan): Import kernel function once zeros is modularized.
        const zerosTensor = _tfjsCore.zeros(x.shape);
        const floatX = cast({
            inputs: {
                x
            },
            backend,
            attrs: {
                dtype: "float32"
            }
        });
        const result = (0, _complex.complex)({
            inputs: {
                real: floatX,
                imag: zerosTensor
            },
            backend
        });
        zerosTensor.dispose();
        backend.disposeIntermediateTensorInfo(floatX);
        return result;
    }
    // Casting from complex64
    if (x.dtype === "complex64") {
        const realPart = (0, _real.real)({
            inputs: {
                input: x
            },
            backend
        });
        const result = cast({
            inputs: {
                x: realPart
            },
            backend,
            attrs: {
                dtype
            }
        });
        backend.disposeIntermediateTensorInfo(realPart);
        return result;
    }
    if (!(0, _tfjsCore.util).hasEncodingLoss(x.dtype, dtype)) {
        // We don't change the underlying data, since we cast to higher
        // precision.
        const result = (0, _identity.identity)({
            inputs: {
                x
            },
            backend
        });
        return {
            dataId: result.dataId,
            shape: result.shape,
            dtype
        };
    }
    if (backend.shouldExecuteOnCPU([
        x
    ])) {
        const values = backend.texData.get(x.dataId).values;
        const [resultShape, resultType, resultData] = (0, _shared.castImplCPU)(values, x.shape, x.dtype, dtype);
        return backend.makeTensorInfo(resultShape, resultType, resultData);
    }
    if (dtype === "int32") return (0, _int.int)(x, backend);
    if (dtype === "bool") {
        const zerosTensorInfo = backend.makeTensorInfo([], "bool", (0, _tfjsCore.util).getTypedArrayFromDType("bool", 1));
        const binaryInputs = {
            a: x,
            b: zerosTensorInfo
        };
        const result = (0, _notEqual.notEqual)({
            inputs: binaryInputs,
            backend
        });
        backend.disposeIntermediateTensorInfo(zerosTensorInfo);
        return result;
    }
    throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
}
const castConfig = {
    kernelName: (0, _tfjsCore.Cast),
    backendName: "webgl",
    kernelFunc: cast
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","./Complex":"55NfL","./Identity":"hY9aT","./NotEqual":"hOvsV","./Real":"4HOZy","../kernel_utils/int":"aZKXt","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hOvsV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "notEqual", ()=>notEqual);
parcelHelpers.export(exports, "notEqualConfig", ()=>notEqualConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const NOT_EQUAL = `return float(a != b);`;
const notEqual = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: NOT_EQUAL,
    cpuKernelImpl: (0, _shared.notEqualImplCPU),
    dtype: "bool"
});
const notEqualConfig = {
    kernelName: (0, _tfjsCore.NotEqual),
    backendName: "webgl",
    kernelFunc: notEqual
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4HOZy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "real", ()=>real);
parcelHelpers.export(exports, "realConfig", ()=>realConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _identity = require("./Identity");
function real(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    const inputData = backend.texData.get(input.dataId);
    return (0, _identity.identity)({
        inputs: {
            x: inputData.complexTensorInfos.real
        },
        backend
    });
}
const realConfig = {
    kernelName: (0, _tfjsCore.Real),
    backendName: "webgl",
    kernelFunc: real
};

},{"@tensorflow/tfjs-core":"21Ftl","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aZKXt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "int", ()=>int);
var _unaryopGpu = require("../unaryop_gpu");
const TO_INT = `return float(int(x));`;
function int(input, backend) {
    const program = new (0, _unaryopGpu.UnaryOpProgram)(input.shape, TO_INT);
    const output = backend.runWebGLProgram(program, [
        input
    ], "int32");
    return {
        dataId: output.dataId,
        shape: output.shape,
        dtype: output.dtype
    };
}

},{"../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iSroj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ceil", ()=>ceil);
parcelHelpers.export(exports, "ceilConfig", ()=>ceilConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const CEIL = `return ceil(x);`;
const ceil = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: CEIL,
    packedOpSnippet: CEIL,
    cpuKernelImpl: (0, _shared.ceilImplCPU)
});
const ceilConfig = {
    kernelName: (0, _tfjsCore.Ceil),
    backendName: "webgl",
    kernelFunc: ceil
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gt2mD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "clipByValue", ()=>clipByValue);
parcelHelpers.export(exports, "clipByValueConfig", ()=>clipByValueConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _clipGpu = require("../clip_gpu");
var _clipPackedGpu = require("../clip_packed_gpu");
function clipByValue(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { clipValueMin, clipValueMax } = attrs;
    let program;
    if ((0, _tfjsCore.env)().getBool("WEBGL_PACK_CLIP")) program = new (0, _clipPackedGpu.ClipPackedProgram)(x.shape);
    else program = new (0, _clipGpu.ClipProgram)(x.shape);
    const customValues = [
        [
            clipValueMin
        ],
        [
            clipValueMax
        ]
    ];
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype, customValues);
}
const clipByValueConfig = {
    kernelName: (0, _tfjsCore.ClipByValue),
    backendName: "webgl",
    kernelFunc: clipByValue
};

},{"@tensorflow/tfjs-core":"21Ftl","../clip_gpu":"5PxVu","../clip_packed_gpu":"au6Kr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5PxVu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ClipProgram", ()=>ClipProgram);
class ClipProgram {
    constructor(aShape){
        this.variableNames = [
            "A"
        ];
        this.customUniforms = [
            {
                name: "minVal",
                type: "float"
            },
            {
                name: "maxVal",
                type: "float"
            }
        ];
        this.outputShape = aShape;
        this.userCode = `

      void main() {
        float value = getAAtOutCoords();
        if (isnan(value)) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, minVal, maxVal));
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"au6Kr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ClipPackedProgram", ()=>ClipPackedProgram);
class ClipPackedProgram {
    constructor(aShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "minVal",
                type: "float"
            },
            {
                name: "maxVal",
                type: "float"
            }
        ];
        this.outputShape = aShape;
        this.userCode = `
      void main() {
        vec4 value = getAAtOutCoords();

        if (any(isnan(value))) {
          setOutput(value);
          return;
        }

        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fwiH6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "complexAbs", ()=>complexAbs);
parcelHelpers.export(exports, "complexAbsConfig", ()=>complexAbsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _complexAbsGpu = require("../complex_abs_gpu");
// Returns a TensorInfo with the complex shape and the dataId of the
// underlying part. We need to do this because a reshaped complex tensor is
// not reflected in its parts.
function makeComplexComponentTensorInfo(complexTensor, complexPart) {
    return {
        dataId: complexPart.dataId,
        dtype: complexPart.dtype,
        shape: complexTensor.shape
    };
}
function complexAbs(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    const xData = backend.texData.get(x.dataId);
    const program = new (0, _complexAbsGpu.ComplexAbsProgram)(x.shape);
    const programInputs = [
        makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
        makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)
    ];
    return backend.runWebGLProgram(program, programInputs, programInputs[0].dtype);
}
const complexAbsConfig = {
    kernelName: (0, _tfjsCore.ComplexAbs),
    backendName: "webgl",
    kernelFunc: complexAbs
};

},{"@tensorflow/tfjs-core":"21Ftl","../complex_abs_gpu":"iVDTX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iVDTX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ComplexAbsProgram", ()=>ComplexAbsProgram);
class ComplexAbsProgram {
    constructor(shape){
        this.variableNames = [
            "real",
            "imag"
        ];
        this.outputShape = shape;
        this.userCode = `
      void main() {
        float re = abs(getRealAtOutCoords());
        float im = abs(getImagAtOutCoords());
        float mx = max(re, im);

        // sadly the length function in glsl is not underflow-safe
        // (at least not on Intel GPUs). So the safe solution is
        // to ensure underflow-safety in all cases.
        setOutput(
          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
        );
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2gCZH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concat", ()=>concat);
parcelHelpers.export(exports, "concatConfig", ()=>concatConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _concatImpl = require("./Concat_impl");
var _identity = require("./Identity");
function concat(args) {
    const { inputs, backend, attrs } = args;
    const { axis } = attrs;
    const $axis = (0, _tfjsCore.util).parseAxisParam(axis, inputs[0].shape)[0];
    const shapes = inputs.map((t)=>t.shape);
    (0, _tfjsCore.backend_util).assertParamsConsistent(shapes, $axis);
    const outShape = (0, _tfjsCore.backend_util).computeOutShape(inputs.map((t)=>t.shape), $axis);
    if ((0, _tfjsCore.util).sizeFromShape(outShape) === 0) return backend.makeTensorInfo(outShape, inputs[0].dtype, []);
    // Keep only non-empty tensors (ignore tensors with 0 in their shape).
    const $inputs = inputs.filter((t)=>(0, _tfjsCore.util).sizeFromShape(t.shape) > 0);
    if ($inputs.length === 1) return (0, _identity.identity)({
        inputs: {
            x: $inputs[0]
        },
        backend
    });
    return (0, _concatImpl.concatImpl)($inputs, $axis, backend);
}
const concatConfig = {
    kernelName: (0, _tfjsCore.Concat),
    backendName: "webgl",
    kernelFunc: concat
};

},{"@tensorflow/tfjs-core":"21Ftl","./Concat_impl":"2G3Uy","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2G3Uy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concatImpl", ()=>concatImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _concatGpu = require("../concat_gpu");
var _concatPackedGpu = require("../concat_packed_gpu");
var _shared = require("../kernel_utils/shared");
var _unaryopGpu = require("../unaryop_gpu");
var _unaryopPackedGpu = require("../unaryop_packed_gpu");
var _complex = require("./Complex");
var _imag = require("./Imag");
var _real = require("./Real");
var _reshape = require("./Reshape");
function concatImpl(inputs, axis, backend) {
    const dtype = inputs[0].dtype;
    if (dtype === "complex64") {
        const reals = inputs.map((t)=>(0, _real.real)({
                inputs: {
                    input: t
                },
                backend
            }));
        const imags = inputs.map((t)=>(0, _imag.imag)({
                inputs: {
                    input: t
                },
                backend
            }));
        const realConcated = concatImpl(reals, axis, backend);
        const imagConcated = concatImpl(imags, axis, backend);
        const result = (0, _complex.complex)({
            inputs: {
                real: realConcated,
                imag: imagConcated
            },
            backend
        });
        reals.forEach((r)=>backend.disposeIntermediateTensorInfo(r));
        imags.forEach((i)=>backend.disposeIntermediateTensorInfo(i));
        backend.disposeIntermediateTensorInfo(realConcated);
        backend.disposeIntermediateTensorInfo(imagConcated);
        return result;
    }
    let runOnCpu = backend.shouldExecuteOnCPU(inputs);
    // Run on cpu if dtype is string. For string, the backend represents it
    // as Uint8Array[], where each Uint8Array is a character. Given that the
    // computation is only on the outer array, uploading the whole data onto
    // gpu is wasteful. Also, currently webgl doesn't have a design to
    // upload and retrieve Uint8Array[] between cpu and gpu. Therefore, we
    // just run the kernel on cpu if dtype is string.
    if (dtype === "string") runOnCpu = true;
    if (runOnCpu) {
        // Any concat of n-dimensional tensors across any axis can be reduced to
        // a concatenation of two-dimensional tensors across the axis 1 by first
        // partitioning the axes of the original tensors into those less than the
        // axis to be concatenated and the rest. Then reshape the tensors
        // into a two-dimensional tensor by collapsing these two sets of axes and
        // concatenate the resulting matrices across the axis 1, finally reshaping
        // the result to have the proper shape.
        const tensors2D = inputs.map((t)=>{
            const innerSize = (0, _tfjsCore.util).sizeFromShape(t.shape.slice(axis));
            const shape = [
                -1,
                innerSize
            ];
            return (0, _reshape.reshape)({
                inputs: {
                    x: t
                },
                backend,
                attrs: {
                    shape
                }
            });
        });
        const inputsValShapes = tensors2D.map((t)=>{
            return {
                vals: backend.readSync(t.dataId),
                shape: t.shape
            };
        });
        // Concats 2d tensors along axis=1.
        const outShape = (0, _tfjsCore.backend_util).computeOutShape(tensors2D.map((t)=>t.shape), 1 /* axis */ );
        const simplyConcat = tensors2D[0].shape[0] === 1;
        const outVals = (0, _shared.concatImplCPU)(inputsValShapes, outShape, dtype, simplyConcat);
        const finalOutShape = (0, _tfjsCore.backend_util).computeOutShape(inputs.map((t)=>t.shape), axis);
        const outInfo = backend.makeTensorInfo(finalOutShape, dtype, outVals);
        tensors2D.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
        return outInfo;
    }
    // Keep only non-empty tensors (ignore tensors with 0 in their shape).
    const $inputs = inputs.filter((t)=>(0, _tfjsCore.util).sizeFromShape(t.shape) > 0);
    const shouldPack = (0, _tfjsCore.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && $inputs[0].shape.length > 1;
    if ($inputs.length === 1) {
        // Clone tensor.
        const program = shouldPack ? new (0, _unaryopGpu.UnaryOpProgram)(inputs[0].shape, (0, _unaryopGpu.CLONE)) : new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(inputs[0].shape, (0, _unaryopGpu.CLONE));
        return backend.runWebGLProgram(program, inputs, dtype);
    }
    const maxTexturesInShader = (0, _tfjsCore.env)().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");
    if ($inputs.length > maxTexturesInShader) {
        const reducedInputs = [];
        for(let i = 0; i < $inputs.length; i += maxTexturesInShader){
            const subArray = $inputs.slice(i, i + maxTexturesInShader);
            reducedInputs.push(concatImpl(subArray, axis, backend));
        }
        const result = concatImpl(reducedInputs, axis, backend);
        for (const i of reducedInputs)backend.disposeIntermediateTensorInfo(i);
        return result;
    }
    if (shouldPack) {
        const program = new (0, _concatPackedGpu.ConcatPackedProgram)($inputs.map((t)=>t.shape), axis);
        return backend.runWebGLProgram(program, $inputs, dtype);
    }
    const { tensors2D, outShape } = computeTensors2D($inputs, axis, backend);
    const program = new (0, _concatGpu.ConcatProgram)(tensors2D.map((t)=>t.shape));
    const result = backend.runWebGLProgram(program, tensors2D, dtype);
    tensors2D.forEach((r)=>backend.disposeIntermediateTensorInfo(r));
    const reshapedResult = (0, _reshape.reshape)({
        inputs: {
            x: result
        },
        attrs: {
            shape: outShape
        },
        backend
    });
    backend.disposeIntermediateTensorInfo(result);
    return reshapedResult;
}
function computeTensors2D(inputs, axis, backend) {
    // Any concat of n-dimensional tensors across any axis can be reduced to
    // a concatenation of two-dimensional tensors across the axis 1 by first
    // partitioning the axes of the original tensors into those less than the
    // axis to be concatenated and the rest. Then reshape the tensors
    // into a two-dimensional tensor by collapsing these two sets of axes and
    // concatenate the resulting matrices across the axis 1, finally reshaping
    // the result to have the proper shape.
    const outShape = (0, _tfjsCore.backend_util).computeOutShape(inputs.map((t)=>t.shape), axis);
    const tensors2D = inputs.map((x)=>(0, _reshape.reshape)({
            inputs: {
                x
            },
            attrs: {
                shape: [
                    -1,
                    (0, _tfjsCore.util).sizeFromShape(x.shape.slice(axis))
                ]
            },
            backend
        }));
    return {
        tensors2D,
        outShape
    };
}

},{"@tensorflow/tfjs-core":"21Ftl","../concat_gpu":"5FWMv","../concat_packed_gpu":"6p42F","../kernel_utils/shared":"e4M7v","../unaryop_gpu":"gIMrR","../unaryop_packed_gpu":"c4D5K","./Complex":"55NfL","./Imag":"3PMfw","./Real":"4HOZy","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5FWMv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ConcatProgram", ()=>ConcatProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
class ConcatProgram {
    // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().
    constructor(shapes){
        this.outputShape = [];
        this.outputShape = (0, _tfjsCore.backend_util).computeOutShape(shapes, 1 /* axis */ );
        this.variableNames = shapes.map((_, i)=>`T${i}`);
        const offsets = new Array(shapes.length - 1);
        offsets[0] = shapes[0][1];
        for(let i = 1; i < offsets.length; i++)offsets[i] = offsets[i - 1] + shapes[i][1];
        const snippets = [
            `if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`
        ];
        for(let i = 1; i < offsets.length; i++){
            const shift = offsets[i - 1];
            snippets.push(`else if (yC < ${offsets[i]}) ` + `setOutput(getT${i}(yR, yC-${shift}));`);
        }
        const lastIndex = offsets.length;
        const lastShift = offsets[offsets.length - 1];
        snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);
        this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int yR = coords.x;
        int yC = coords.y;

        ${snippets.join("\n        ")}
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6p42F":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ConcatPackedProgram", ()=>ConcatPackedProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class ConcatPackedProgram {
    constructor(shapes, axis){
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = [];
        this.outputShape = (0, _tfjsCore.backend_util).computeOutShape(shapes, axis);
        const shape = this.outputShape;
        const rank = shape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
        const coords = (0, _packingUtil.getChannels)("coords", rank);
        const channels = [
            "x",
            "y",
            "z",
            "w",
            "u",
            "v"
        ].slice(0, rank);
        this.variableNames = shapes.map((_, i)=>`T${i}`);
        const offsets = new Array(shapes.length - 1);
        offsets[0] = shapes[0][axis];
        for(let i = 1; i < offsets.length; i++)offsets[i] = offsets[i - 1] + shapes[i][axis];
        const channel = channels[axis];
        const lastChannels = channels.slice(-2);
        const allChannels = channels.join();
        let getValueSnippet = `if (${channel} < ${offsets[0]}) {
        return getChannel(
            getT0(${allChannels}), vec2(${lastChannels.join()}));
        }`;
        for(let i = 1; i < offsets.length; i++){
            const shift = offsets[i - 1];
            // Note: the >= comparison below may seem unnecessary given the check
            // above but is needed to workaround branch execution issues on some
            // devices. It makes all the conditions exclusive without relying on
            // execution order.
            getValueSnippet += `
        if (${channel} < ${offsets[i]}  && ${channel} >= ${offsets[i - 1]}) {
          return getChannel(
            getT${i}(${shiftedChannels(channels, channel, shift)}),
            vec2(${shiftedChannels(lastChannels, channel, shift)}));
        }`;
        }
        const lastIndex = offsets.length;
        const shift = offsets[offsets.length - 1];
        getValueSnippet += `
        return getChannel(
          getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),
          vec2(${shiftedChannels(lastChannels, channel, shift)}));`;
        this.userCode = `
      float getValue(${channels.map((x)=>"int " + x)}) {
        ${getValueSnippet}
      }

      void main() {
        ${dtype} coords = getOutputCoords();
        vec4 result = vec4(getValue(${coords}), 0., 0., 0.);

        ${coords[rank - 1]} = ${coords[rank - 1]} + 1;
        if (${coords[rank - 1]} < ${shape[rank - 1]}) {
          result.g = getValue(${coords});
        }

        ${coords[rank - 2]} = ${coords[rank - 2]} + 1;
        if (${coords[rank - 2]} < ${shape[rank - 2]}) {
          result.a = getValue(${coords});
        }

        ${coords[rank - 1]} = ${coords[rank - 1]} - 1;
        if (${coords[rank - 2]} < ${shape[rank - 2]} &&
            ${coords[rank - 1]} < ${shape[rank - 1]}) {
          result.b = getValue(${coords});
        }
        setOutput(result);
      }
    `;
    }
}
/**
 * Return an expression for coordinates into a vector where a given channel
 * will be offset by [shift].
 *
 * @param channels the channels to consider
 * @param channel the channel we want shifted
 * @param shift  the amount to subtract from the channel.
 *
 * @returns a string of the form 'x, y-[shift], z' where any one channel can
 * have the shift applied.
 */ function shiftedChannels(channels, channel, shift) {
    const channelIdx = channels.indexOf(channel);
    const res = channels.map((c, idx)=>{
        if (idx === channelIdx) return `${c} - ${shift}`;
        else return c;
    });
    return res.join();
}

},{"@tensorflow/tfjs-core":"21Ftl","./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3PMfw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "imag", ()=>imag);
parcelHelpers.export(exports, "imagConfig", ()=>imagConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _identity = require("./Identity");
function imag(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    const inputData = backend.texData.get(input.dataId);
    return (0, _identity.identity)({
        inputs: {
            x: inputData.complexTensorInfos.imag
        },
        backend
    });
}
const imagConfig = {
    kernelName: (0, _tfjsCore.Imag),
    backendName: "webgl",
    kernelFunc: imag
};

},{"@tensorflow/tfjs-core":"21Ftl","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"425yr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2d", ()=>conv2d);
parcelHelpers.export(exports, "conv2DConfig", ()=>conv2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convGpu = require("../conv_gpu");
var _convPackedGpu = require("../conv_packed_gpu");
var _conv2DImpl = require("./Conv2D_impl");
var _reshape = require("./Reshape");
function conv2d(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dataFormat, dilations, dimRoundingMode } = attrs;
    const $dataFormat = (0, _tfjsCore.backend_util).convertConv2DDataFormat(dataFormat);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
    let out;
    if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) out = (0, _conv2DImpl.conv2dByMatMul)({
        x,
        filter,
        convInfo,
        backend
    });
    else if (convInfo.strideWidth <= 2 && $dataFormat === "channelsLast" && (0, _tfjsCore.env)().getBool("WEBGL_EXP_CONV")) {
        const program = new (0, _convPackedGpu.Conv2DPackedProgram)(convInfo);
        const customValues = [
            [
                convInfo.padInfo.top,
                convInfo.padInfo.left
            ],
            [
                convInfo.strideHeight,
                convInfo.strideWidth
            ],
            [
                convInfo.dilationHeight,
                convInfo.dilationWidth
            ],
            [
                convInfo.inHeight,
                convInfo.inWidth
            ]
        ];
        out = backend.runWebGLProgram(program, [
            x,
            filter
        ], "float32", customValues);
    } else if ((0, _tfjsCore.env)().getBool("WEBGL_CONV_IM2COL")) out = (0, _conv2DImpl.conv2dWithIm2Row)({
        x,
        filter,
        convInfo,
        backend
    });
    else {
        const program = new (0, _convGpu.Conv2DProgram)(convInfo);
        out = backend.runWebGLProgram(program, [
            x,
            filter
        ], "float32");
    }
    const outReshaped = (0, _reshape.reshape)({
        inputs: {
            x: out
        },
        backend,
        attrs: {
            shape: convInfo.outShape
        }
    });
    backend.disposeIntermediateTensorInfo(out);
    return outReshaped;
}
const conv2DConfig = {
    kernelName: (0, _tfjsCore.Conv2D),
    backendName: "webgl",
    kernelFunc: conv2d
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_gpu":"3pfYb","../conv_packed_gpu":"hPtK0","./Conv2D_impl":"6Kgrg","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3pfYb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Conv2DProgram", ()=>Conv2DProgram);
parcelHelpers.export(exports, "Conv3DProgram", ()=>Conv3DProgram);
class Conv2DProgram {
    constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false){
        this.variableNames = [
            "x",
            "W"
        ];
        this.outputShape = convInfo.outShape;
        const padTop = convInfo.padInfo.top;
        const padLeft = convInfo.padInfo.left;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationHeight = convInfo.dilationHeight;
        const dilationWidth = convInfo.dilationWidth;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
        const inputDepthVec4Remainder = convInfo.inChannels % 4;
        const isChannelsLast = convInfo.dataFormat === "channelsLast";
        const rowDim = isChannelsLast ? 1 : 2;
        const colDim = isChannelsLast ? 2 : 3;
        const channelDim = isChannelsLast ? 3 : 1;
        let activationSnippet = "", applyActivationSnippet = "";
        if (activation) {
            if (hasPreluActivationWeights) activationSnippet = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${activation}
        }`;
            else if (hasLeakyreluAlpha) activationSnippet = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${activation}
        }`;
            else activationSnippet = `
          float activation(float x) {
            ${activation}
          }
        `;
            applyActivationSnippet = `result = activation(result);`;
        }
        const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
        if (addBias) this.variableNames.push("bias");
        if (hasPreluActivationWeights) this.variableNames.push("preluActivationWeights");
        if (hasLeakyreluAlpha) this.variableNames.push("leakyreluAlpha");
        this.userCode = `
      ${activationSnippet}

      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d2 = coords[${channelDim}];

        ivec2 xRCCorner =
            ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          int xR = xRCorner + wR * ${dilationHeight};

          if (xR < 0 || xR >= ${convInfo.inHeight}) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int xC = xCCorner + wC * ${dilationWidth};

            if (xC < 0 || xC >= ${convInfo.inWidth}) {
              continue;
            }

            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
              vec4 wValues = vec4(
                getW(wR, wC, d1, d2),
                getW(wR, wC, d1 + 1, d2),
                getW(wR, wC, d1 + 2, d2),
                getW(wR, wC, d1 + 3, d2)
              );

              if (${isChannelsLast}) {
                vec4 xValues = vec4(
                  getX(batch, xR, xC, d1),
                  getX(batch, xR, xC, d1 + 1),
                  getX(batch, xR, xC, d1 + 2),
                  getX(batch, xR, xC, d1 + 3)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec4 xValues = vec4(
                  getX(batch, d1, xR, xC),
                  getX(batch, d1 + 1, xR, xC),
                  getX(batch, d1 + 2, xR, xC),
                  getX(batch, d1 + 3, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }
            }

            if (${inputDepthVec4Remainder === 1}) {

              if (${isChannelsLast}) {
                dotProd +=
                    getX(batch, xR, xC, ${inputDepthNearestVec4}) *
                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
              } else {
                dotProd +=
                    getX(batch, ${inputDepthNearestVec4}, xR, xC) *
                    getW(wR, wC, ${inputDepthNearestVec4}, d2);
              }

            } else if (${inputDepthVec4Remainder === 2}) {
              vec2 wValues = vec2(
                getW(wR, wC, ${inputDepthNearestVec4}, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
              );

              if (${isChannelsLast}) {
                vec2 xValues = vec2(
                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec2 xValues = vec2(
                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            } else if (${inputDepthVec4Remainder === 3}) {
              vec3 wValues = vec3(
                getW(wR, wC, ${inputDepthNearestVec4}, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
              );

              if (${isChannelsLast}) {
                vec3 xValues = vec3(
                  getX(batch, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
                  getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
                );
                dotProd += dot(xValues, wValues);
              } else {
                vec3 xValues = vec3(
                  getX(batch, ${inputDepthNearestVec4}, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
                  getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
                );
                dotProd += dot(xValues, wValues);
              }

            }
          }
        }

        float result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `;
    }
}
class Conv3DProgram {
    constructor(convInfo){
        this.variableNames = [
            "x",
            "W"
        ];
        this.outputShape = convInfo.outShape;
        const padFront = convInfo.padInfo.front;
        const padTop = convInfo.padInfo.top;
        const padLeft = convInfo.padInfo.left;
        const strideDepth = convInfo.strideDepth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationDepth = convInfo.dilationDepth;
        const dilationHeight = convInfo.dilationHeight;
        const dilationWidth = convInfo.dilationWidth;
        const filterDepth = convInfo.filterDepth;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
        const inputDepthVec4Remainder = convInfo.inChannels % 4;
        this.userCode = `
      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d2 = coords.u;

        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
        int xFCorner = xFRCCorner.x;
        int xRCorner = xFRCCorner.y;
        int xCCorner = xFRCCorner.z;

        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
        // y(yF, yR, yC, d2). ? = to be determined. : = across all
        // values in that axis.
        float dotProd = 0.0;
        for (int wF = 0; wF < ${filterDepth}; wF++) {
          int xF = xFCorner + wF * ${dilationDepth};

          if (xF < 0 || xF >= ${convInfo.inDepth}) {
            continue;
          }

          for (int wR = 0; wR < ${filterHeight}; wR++) {
            int xR = xRCorner + wR * ${dilationHeight};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int wC = 0; wC < ${filterWidth}; wC++) {
              int xC = xCCorner + wC * ${dilationWidth};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
                vec4 xValues = vec4(
                  getX(batch, xF, xR, xC, d1),
                  getX(batch, xF, xR, xC, d1 + 1),
                  getX(batch, xF, xR, xC, d1 + 2),
                  getX(batch, xF, xR, xC, d1 + 3)
                );
                vec4 wValues = vec4(
                  getW(wF, wR, wC, d1, d2),
                  getW(wF, wR, wC, d1 + 1, d2),
                  getW(wF, wR, wC, d1 + 2, d2),
                  getW(wF, wR, wC, d1 + 3, d2)
                );

                dotProd += dot(xValues, wValues);
              }

              if (${inputDepthVec4Remainder === 1}) {
                dotProd +=
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
              } else if (${inputDepthVec4Remainder === 2}) {
                vec2 xValues = vec2(
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
                );
                vec2 wValues = vec2(
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
                );
                dotProd += dot(xValues, wValues);
              } else if (${inputDepthVec4Remainder === 3}) {
                vec3 xValues = vec3(
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
                );
                vec3 wValues = vec3(
                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
                );
                dotProd += dot(xValues, wValues);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hPtK0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Conv2DPackedProgram", ()=>Conv2DPackedProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gpgpuMath = require("./gpgpu_math");
class Conv2DPackedProgram {
    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false, hasLeakyReluAlpha = false){
        this.variableNames = [
            "x",
            "W"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "pads",
                type: "ivec2"
            },
            {
                name: "strides",
                type: "ivec2"
            },
            {
                name: "dilations",
                type: "ivec2"
            },
            {
                name: "inDims",
                type: "ivec2"
            }
        ];
        this.outputShape = convInfo.outShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        const padLeft = convInfo.padInfo.left;
        const strideWidth = convInfo.strideWidth;
        const dilationWidth = convInfo.dilationWidth;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const texelsAcross = filterWidth;
        let mainLoop = `
       int xR; int xC; int xCOffset;
       vec4 wTexel; vec4 previous; vec4 final;`;
        for(let c = 0; c < filterWidth; c++)mainLoop += `
           vec4 xTexelC${c * 2};
           int xTexelC${c * 2}Ready;
           vec4 xTexelC${c * 2 + 1};
           int xTexelC${c * 2 + 1}Ready;
           vec4 xC${c};`;
        /**
         * This vectorized implementation works by gathering the values needed for
         * each output channel's dot product into vec4's and then multiplying them
         * all together (this happens in the final double for-loop below). Most of
         * the main loop consists of constructing these vec4's with the minimum
         * number of texture2D calls, which means making use of all four returned
         * values from a texture2D call at once.
         */ mainLoop += `
     for (int r = 0; r < ${filterHeight}; r++) {
      for (int d1 = 0; d1 < ${convInfo.inChannels}; d1 += 2) {
       `;
        for(let c = 0; c < filterWidth; c++)mainLoop += `
           xTexelC${c * 2} = vec4(0.0);
           xTexelC${c * 2}Ready = 0;
           xTexelC${c * 2 + 1} = vec4(0.0);
           xTexelC${c * 2 + 1}Ready = 0;
           xC${c} = vec4(0.0);`;
        mainLoop += `
         xR = xRCorner + r * dilations[0];
         if (xR >=0 && xR < inDims[0]) {
       `;
        for(let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++){
            const colIndex = texelC * 2;
            mainLoop += `
           xC = xCCorner + ${colIndex * dilationWidth};
           `;
            if (strideWidth === 1) {
                if (colIndex < filterWidth) {
                    // If padding is odd, the outer texels have to be composed.
                    if (padLeft % 2 === 1) {
                        // TODO: Ensure vec4 previous does not result in redundant sample,
                        // and avoid setting xTexelRC's that exceed the boundary in the
                        // first place rather than resetting them to vec4(0)).
                        // To compute xCOffset:
                        // - If padding is odd, we must add 1 to ensure we ask for an
                        // even-numbered row.
                        // - We subtract 2 to access the previous texel.
                        mainLoop += `
                 xCOffset = xC + 1;
                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);

                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${colIndex}.zw = vec2(0.0);
                   }
                   xTexelC${colIndex}Ready = 1;
                 }
               `;
                        // This texel has been read in previous iteration if the dilation
                        // is 1.
                        if (dilationWidth === 1 && colIndex > 0) mainLoop += `
                 xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);
                 `;
                        else mainLoop += `
                   xCOffset = xC + 1 - 2;

                   if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);

                     // Need to manually clear unused channels in case
                     // we're reading from recycled texture.
                     if (xCOffset + 1 >= inDims[1]) {
                       previous.zw = vec2(0.0);
                     }

                     xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);
                   } else {
                     xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);
                   }
                   `;
                    } else // Padding is even, so xRC corresponds to a single texel.
                    mainLoop += `
                 if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
                   xTexelC${colIndex} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${colIndex}.zw = vec2(0.0);
                   }
                   xTexelC${colIndex}Ready = 1;
                 }

                 xC${colIndex} = xTexelC${colIndex};
                 `;
                    if (colIndex + 1 < filterWidth) {
                        // If dilation is even, the second entry should match the first
                        // (either both are composed or both are single samples). But if
                        // dilation is odd, then the second entry should be the opposite
                        // of the first (if the first is composed, the second is a single
                        // sample, and vice versa.)
                        const nextTexelOffset = padLeft % 2 === 0 ? (0, _tfjsCore.util).nearestLargerEven(dilationWidth) : dilationWidth;
                        if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {
                            mainLoop += `
                   xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};

                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                     xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);

                     // Need to manually clear unused channels in case
                     // we're reading from recycled texture.
                     if (xCOffset + 1 >= inDims[1]) {
                       xTexelC${colIndex + 1}.zw = vec2(0.0);
                     }
                     xTexelC${colIndex + 1}Ready = 1;
                   }
                   `;
                            // If dilation > 1 then the xRC's will not be able to share any
                            // values, so each xRC will require two unique calls to getX.
                            if (dilationWidth > 1) mainLoop += `
                     xCOffset -= 2;
                     if (xCOffset >= 0 && xCOffset < inDims[1]) {
                      previous = getX(batch, xR, xCOffset, d1);
                      xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);
                     } else {
                      xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);
                     }
                     `;
                            else mainLoop += `
                     xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);
                     `;
                        } else // If dilation is 1 and padding is odd, we have already read the
                        // texel when constructing the previous x value. Here we can
                        // simply skip the texture read.
                        if (nextTexelOffset === 1) mainLoop += `
                     xC${colIndex + 1} = xTexelC${colIndex};
                     `;
                        else mainLoop += `
                     xCOffset = xC + ${nextTexelOffset};

                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                       xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
                       if (xCOffset + 1 >= inDims[1]) {
                         xTexelC${colIndex + 1}.zw = vec2(0.0);
                       }
                       xTexelC${colIndex + 1}Ready = 1;
                     }

                     xC${colIndex + 1} = xTexelC${colIndex + 1};
                     `;
                    }
                }
            } else if (colIndex < filterWidth) {
                // Depending on whether padLeft is even or odd, we want either the
                // xy or zw channels from X texels for xC${colIndex}. If padLeft is
                // even, xC${colIndex +1} is simply the zw channels of texels we've
                // already sampled. But if padLeft is odd, xC{$c + 1}.zw will
                // need to come from the xy channels of a new texel, hence the `
                // vec4
                // final` initialized below.
                if (padLeft % 2 === 1) {
                    mainLoop += `
                 xCOffset = xC + 1 - strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
                   xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${colIndex}.zw = vec2(0.0);
                   }
                   xTexelC${colIndex}Ready = 1;
                 }

                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                   xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);
                   // Need to manually clear unused channels in case
                   // we're reading from recycled texture.
                   if (xC + 2 >= inDims[1]) {
                     xTexelC${colIndex + 1}.zw = vec2(0.0);
                   }
                   xTexelC${colIndex + 1}Ready = 1;
                 }

                 xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
               `;
                    if (colIndex + 1 < filterWidth) mainLoop += `
                   final = vec4(0.0);
                   xCOffset = xC + 1 + strides[1];
                   if(xCOffset >= 0 && xCOffset < inDims[1]) {
                     final = getX(batch, xR, xCOffset, d1);
                   }
                   xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);
                 `;
                } else {
                    mainLoop += `
                 if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
                   xTexelC${colIndex} = getX(batch, xR, xC, d1);
                   if (xC + 1 >= inDims[1]) {
                     xTexelC${colIndex}.zw = vec2(0.0);
                   }
                   xTexelC${colIndex}Ready = 1;
                 }

                 xCOffset = xC + strides[1];
                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                   xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
                   if (xCOffset + 1 >= inDims[1]) {
                     xTexelC${colIndex + 1}.zw = vec2(0.);
                   }
                   xTexelC${colIndex + 1}Ready = 1;
                 }

                 xC${colIndex} = vec4(
                   xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);
               `;
                    if (colIndex + 1 < filterWidth) mainLoop += `
                   xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
                 `;
                }
            }
            // localize the dotProd accumulation within the loop, the theory is for
            // GPU with limited cache, accumulate sum across large amount of
            // veriables will cause lots of cache misses. (i.e. 5x5 filter will have
            // 50 variables)
            if (colIndex < filterWidth) {
                mainLoop += `
             wTexel = getW(r, ${colIndex}, d1, d2);
             dotProd += xC${colIndex}.xxzz * vec4(wTexel.xy, wTexel.xy);
             if(d1 + 1 < ${convInfo.inChannels}) {
               dotProd += xC${colIndex}.yyww * vec4(wTexel.zw, wTexel.zw);
             }
           `;
                if (colIndex + 1 < filterWidth) mainLoop += `
               wTexel = getW(r, ${colIndex + 1}, d1, d2);
               dotProd += xC${colIndex + 1}.xxzz * vec4(wTexel.xy, wTexel.xy);
               if(d1 + 1 < ${convInfo.inChannels}) {
                 dotProd += xC${colIndex + 1}.yyww * vec4(wTexel.zw, wTexel.zw);
               }
             `;
            }
        }
        mainLoop += `
     }
   `;
        mainLoop += `
     }
   `;
        mainLoop += `
     }
   `;
        let activationSnippet = "", applyActivationSnippet = "";
        if (activation) {
            if (hasPreluActivation) activationSnippet = `vec4 activation(vec4 a) {
           vec4 b = getPreluActivationWeightsAtOutCoords();
           ${activation}
         }`;
            else if (hasLeakyReluAlpha) activationSnippet = `vec4 activation(vec4 a) {
           vec4 b = getLeakyreluAlphaAtOutCoords();
           ${activation}
         }`;
            else activationSnippet = `vec4 activation(vec4 x) {
           ${activation}
         }`;
            applyActivationSnippet = `result = activation(result);`;
        }
        const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
        if (addBias) this.variableNames.push("bias");
        if (hasPreluActivation) this.variableNames.push("preluActivationWeights");
        if (hasLeakyReluAlpha) this.variableNames.push("leakyreluAlpha");
        this.userCode = `
       ${activationSnippet}

       void main() {
         ivec4 coords = getOutputCoords();
         int batch = coords.x;
         ivec2 xRCCorner = coords.yz * strides - pads;
         int d2 = coords.w;
         int xRCorner = xRCCorner.x;
         int xCCorner = xRCCorner.y;

         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
         vec4 dotProd = vec4(0.000000000000001);

         ${mainLoop}

         vec4 result = dotProd - vec4(0.000000000000001);
         ${addBiasSnippet}
         ${applyActivationSnippet}
         setOutput(result);
       }
     `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6Kgrg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
// For 1x1 kernels that iterate through every point in the input, convolution
// can be expressed as matrix multiplication (without need for memory
// remapping).
parcelHelpers.export(exports, "conv2dByMatMul", ()=>conv2dByMatMul);
// Implements the im2row algorithm as outlined in "High Performance
// Convolutional Neural Networks for Document Processing" (Suvisoft, 2006)
parcelHelpers.export(exports, "conv2dWithIm2Row", ()=>conv2dWithIm2Row);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _im2ColPackedGpu = require("../im2col_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _mulmatPackedGpu = require("../mulmat_packed_gpu");
var _webglUtil = require("../webgl_util");
var _batchMatMulImpl = require("./BatchMatMul_impl");
var _identity = require("./Identity");
var _reshape = require("./Reshape");
// Both conv2dByMatMul and conv2dWithIm2Row fuse height and width into one
// dimension to compute batchMatMul, so bias and activation weights are also
// supposed to fuse the two dimensions into one.
//
// This function computes the target shape for fusing height and width
// dimensions. Returning null means the shape is already compatible.
//
// Even though the bias is not supposed to be a 3-D or a 4-D (including
// batch) tensor and PReLU activiation weights is not supposed to be a 4-D
// tensor, we still need to support them, because we haven't disabled
// them for NHWC format.
// https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/conv2d.ts#L181-L196
function getShapeForBatchMatMul(shape, isChannelsLast) {
    const length = shape.length;
    if (length >= 3) return isChannelsLast ? [
        ...shape.slice(0, -3),
        shape[length - 3] * shape[length - 2],
        shape[length - 1]
    ] : [
        ...shape.slice(0, -3),
        shape[length - 3],
        shape[length - 2] * shape[length - 1]
    ];
    else if (!isChannelsLast && length === 1 && shape[0] > 1) return [
        shape[0],
        1
    ];
    else return null;
}
function conv2dByMatMul({ x, filter, convInfo, backend, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
    // Reshapes conv2D input to 2D tensors, uses matMul and then reshape the
    // result from 2D to 4D.
    const xShape = x.shape;
    const xTexData = backend.texData.get(x.dataId);
    const sharedMatMulDim = convInfo.inChannels;
    const outerShapeX = xShape[0] * xShape[1] * xShape[2];
    const outerShapeFilter = convInfo.outChannels;
    const isChannelsLast = convInfo.dataFormat === "channelsLast";
    const transposeA = false;
    const transposeB = false;
    let out;
    const intermediates = [];
    if (preluActivationWeights != null) {
        const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
        if (targetShape != null) {
            preluActivationWeights = (0, _reshape.reshape)({
                inputs: {
                    x: preluActivationWeights
                },
                backend,
                attrs: {
                    shape: targetShape
                }
            });
            intermediates.push(preluActivationWeights);
        }
    }
    if (bias != null) {
        const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
        if (targetShape != null) {
            bias = (0, _reshape.reshape)({
                inputs: {
                    x: bias
                },
                backend,
                attrs: {
                    shape: targetShape
                }
            });
            intermediates.push(bias);
        }
    }
    // TODO: Once reduction ops are packed, batchMatMul will always be packed
    // and we can remove this condition.
    const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > (0, _batchMatMulImpl.MATMUL_SHARED_DIM_THRESHOLD);
    // The algorithm in the if condition assumes (1) the output will be packed,
    // (2) x is packed, (3) x isChannelsLast, (4)  x's packed texture is already
    // on GPU, (5) col is odd, (6) the width, height and inChannels are the same
    // for xTexData.shape and xShape.
    const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && (0, _tfjsCore.util).arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));
    if (canOptimize) {
        // We avoid expensive packed 2x2 reshape by padding col count to next,
        // even number. When col is odd, the result of packed batchMatMul is
        // the same (has the same texture layout and and values in the texture) as
        // it is for next even col. We make the odd-cols tensor to look like
        // even-cols tensor before the operation and, after the batchMatMul,
        // fix the even-cols result to have odd number of cols.
        const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);
        const xReshaped = {
            dataId: x.dataId,
            shape: [
                1,
                targetShape,
                convInfo.inChannels
            ],
            dtype: x.dtype
        };
        // xTexData.shape gets referenced from GPGPUBinary.inShapeInfos.
        // Decrementing col count, after batchMatMul->...->compileProgram leads to
        // invalid col count within the reference in GPGPUBinary.inShapeInfos.
        // Alternative fix would be to provide a copy to GPGPUBinary.inShapeInfos
        // in compileProgram method, but that would affect compilation of all
        // programs - instead, provide a copy here, with even col count, before
        // calling batchMatMul->...->compileProgram and after that, the original
        // xTexData.shape is restored.
        const originalXTexDataShape = xTexData.shape;
        xTexData.shape = xTexData.shape.slice();
        xTexData.shape[xTexData.shape.length - 2]++;
        (0, _tfjsCore.util).assert(_webglUtil.isReshapeFree(xTexData.shape, xReshaped.shape), ()=>`packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);
        const filterReshaped = (0, _reshape.reshape)({
            inputs: {
                x: filter
            },
            backend,
            attrs: {
                shape: [
                    1,
                    convInfo.inChannels,
                    convInfo.outChannels
                ]
            }
        });
        intermediates.push(filterReshaped);
        const pointwiseConv = (0, _batchMatMulImpl.batchMatMulImpl)({
            a: xReshaped,
            b: filterReshaped,
            backend,
            transposeA,
            transposeB,
            bias,
            activation,
            preluActivationWeights,
            leakyreluAlpha
        });
        const pointwiseConvTexData = backend.texData.get(pointwiseConv.dataId);
        (0, _tfjsCore.util).assert(pointwiseConvTexData.isPacked, ()=>"batchMatMul result is expected to be packed");
        // Restore the input shape to original.
        xTexData.shape = originalXTexDataShape;
        // Set the output shape - there is no need for expensive reshape as data
        // layout is already correct.
        pointwiseConvTexData.shape = convInfo.outShape;
        out = (0, _identity.identity)({
            inputs: {
                x: pointwiseConv
            },
            backend
        });
        out.shape = convInfo.outShape;
        intermediates.push(pointwiseConv);
    } else {
        const numCols = convInfo.outHeight * convInfo.outWidth;
        const xReshaped = (0, _reshape.reshape)({
            inputs: {
                x
            },
            backend,
            attrs: {
                shape: isChannelsLast ? [
                    convInfo.batchSize,
                    numCols,
                    convInfo.inChannels
                ] : [
                    convInfo.batchSize,
                    convInfo.inChannels,
                    numCols
                ]
            }
        });
        const filterReshaped = (0, _reshape.reshape)({
            inputs: {
                x: filter
            },
            backend,
            attrs: {
                shape: [
                    1,
                    convInfo.inChannels,
                    convInfo.outChannels
                ]
            }
        });
        const result = (0, _batchMatMulImpl.batchMatMulImpl)({
            a: isChannelsLast ? xReshaped : filterReshaped,
            b: isChannelsLast ? filterReshaped : xReshaped,
            transposeA: !isChannelsLast,
            transposeB,
            backend,
            bias,
            activation,
            preluActivationWeights,
            leakyreluAlpha
        });
        out = (0, _reshape.reshape)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                shape: convInfo.outShape
            }
        });
        intermediates.push(xReshaped);
        intermediates.push(filterReshaped);
        intermediates.push(result);
    }
    for (const i of intermediates)backend.disposeIntermediateTensorInfo(i);
    return out;
}
function conv2dWithIm2Row({ x, filter, convInfo, backend, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
    // Rearranges conv2d input so each block to be convolved over forms the
    // column of a new matrix with shape [filterWidth * filterHeight *
    // inChannels, outHeight * outWidth]. The filter is also rearranged so each
    // output channel forms a row of a new matrix with shape [outChannels,
    // filterWidth * filterHeight * inChannels]. The convolution is then
    // computed by multiplying these matrices and reshaping the result.
    const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;
    const isChannelsLast = dataFormat === "channelsLast";
    const sharedDim = filterWidth * filterHeight * inChannels;
    const numCols = outHeight * outWidth;
    const x2ColShape = [
        convInfo.batchSize,
        sharedDim,
        numCols
    ];
    const transposeA = true;
    const transposeB = false;
    const intermediates = [];
    if (preluActivationWeights != null) {
        const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
        if (targetShape != null) {
            preluActivationWeights = (0, _reshape.reshape)({
                inputs: {
                    x: preluActivationWeights
                },
                backend,
                attrs: {
                    shape: targetShape
                }
            });
            intermediates.push(preluActivationWeights);
        }
    }
    if (bias != null) {
        const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
        if (targetShape != null) {
            bias = (0, _reshape.reshape)({
                inputs: {
                    x: bias
                },
                backend,
                attrs: {
                    shape: targetShape
                }
            });
            intermediates.push(bias);
        }
    }
    const w2Row = (0, _reshape.reshape)({
        inputs: {
            x: filter
        },
        backend,
        attrs: {
            shape: [
                1,
                sharedDim,
                (0, _tfjsCore.util).sizeFromShape(filter.shape) / sharedDim
            ]
        }
    });
    intermediates.push(w2Row);
    const im2ColProgram = new (0, _im2ColPackedGpu.Im2ColPackedProgram)(x2ColShape, convInfo);
    const customValues = [
        x.shape,
        [
            convInfo.padInfo.top,
            convInfo.padInfo.left
        ],
        [
            convInfo.strideHeight,
            convInfo.strideWidth
        ],
        [
            convInfo.dilationHeight,
            convInfo.dilationWidth
        ],
        [
            convInfo.inChannels
        ],
        [
            convInfo.filterWidth * convInfo.inChannels
        ],
        [
            convInfo.outWidth
        ]
    ];
    const im2Col = backend.runWebGLProgram(im2ColProgram, [
        x
    ], "float32", customValues);
    const im2ColReshaped = (0, _reshape.reshape)({
        inputs: {
            x: im2Col
        },
        backend,
        attrs: {
            shape: x2ColShape
        }
    });
    intermediates.push(im2Col);
    intermediates.push(im2ColReshaped);
    const hasBias = bias != null;
    const hasPreluActivationWeights = preluActivationWeights != null;
    const hasLeakyreluAlpha = activation === "leakyrelu";
    const fusedActivation = activation ? (0, _kernelFuncsUtils.mapActivationToShaderProgram)(activation, true) : null;
    const matmulProgram = new (0, _mulmatPackedGpu.MatMulPackedProgram)(isChannelsLast ? im2ColReshaped.shape : w2Row.shape, isChannelsLast ? w2Row.shape : im2ColReshaped.shape, isChannelsLast ? [
        convInfo.batchSize,
        numCols,
        convInfo.outChannels
    ] : [
        convInfo.batchSize,
        convInfo.outChannels,
        numCols
    ], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
    const inputs = isChannelsLast ? [
        im2ColReshaped,
        w2Row
    ] : [
        w2Row,
        im2ColReshaped
    ];
    if (bias) inputs.push(bias);
    if (hasPreluActivationWeights) inputs.push(preluActivationWeights);
    if (hasLeakyreluAlpha) {
        const $leakyreluAlpha = backend.makeTensorInfo([], "float32", (0, _tfjsCore.util).createScalarValue(leakyreluAlpha, "float32"));
        inputs.push($leakyreluAlpha);
        intermediates.push($leakyreluAlpha);
    }
    const product = backend.runWebGLProgram(matmulProgram, inputs, "float32");
    const out = (0, _reshape.reshape)({
        inputs: {
            x: product
        },
        backend,
        attrs: {
            shape: convInfo.outShape
        }
    });
    intermediates.push(product);
    for (const i of intermediates)backend.disposeIntermediateTensorInfo(i);
    return out;
}

},{"@tensorflow/tfjs-core":"21Ftl","../im2col_packed_gpu":"hGWmW","../kernel_utils/kernel_funcs_utils":"grPtN","../mulmat_packed_gpu":"cdOmp","../webgl_util":"fjf7t","./BatchMatMul_impl":"kyEQG","./Identity":"hY9aT","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hGWmW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Im2ColPackedProgram", ()=>Im2ColPackedProgram);
var _glslVersion = require("./glsl_version");
var _gpgpuMath = require("./gpgpu_math");
class Im2ColPackedProgram {
    constructor(outputShape, convInfo){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "inputShape",
                type: "ivec4"
            },
            {
                name: "pad",
                type: "ivec2"
            },
            {
                name: "stride",
                type: "ivec2"
            },
            {
                name: "dilation",
                type: "ivec2"
            },
            {
                name: "inChannels",
                type: "int"
            },
            {
                name: "itemsPerBlockRow",
                type: "int"
            },
            {
                name: "outWidth",
                type: "int"
            }
        ];
        this.outputShape = outputShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        const { dataFormat } = convInfo;
        const glsl = (0, _glslVersion.getGlslDifferences)();
        const isChannelsLast = dataFormat === "channelsLast";
        const rowDim = isChannelsLast ? 1 : 2;
        const colDim = isChannelsLast ? 2 : 3;
        const boundsCheckingSnippet = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${outputShape[2]} && pos < ${outputShape[1]}) {`;
        let unrolled = ``;
        for(let row = 0; row <= 1; row++)for(let col = 0; col <= 1; col++)unrolled += `
          blockIndex = rc.z + ${col};
          pos = rc.y + ${row};

          ${boundsCheckingSnippet}
            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);

            if(d0 < inputShape[${rowDim}] && d0 >= 0) {
              // Use custom imod instead mod. On Intel GPU, mod may generate
              // unexpected value.
              // https://github.com/tensorflow/tfjs/issues/5447
              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
                  inChannels);

              if(d1 < inputShape[${colDim}] && d1 >= 0) {

                ch = imod(pos, inChannels);

                if (${isChannelsLast}) {
                  innerDims = vec2(d1, ch);
                  result[${row * 2 + col}] = getChannel(
                    getA(rc.x, d0, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                } else {
                  innerDims = vec2(d0, d1);
                  result[${row * 2 + col}] = getChannel(
                    getA(rc.x, ch, int(innerDims.x),
                    int(innerDims.y)), innerDims);
                }
              }
            }
          }
        `;
        this.userCode = `
      void main() {
        ivec3 rc = getOutputCoords();

        vec4 result = vec4(0);

        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
        vec2 innerDims;

        ${unrolled}

        ${glsl.output} = result;
      }
    `;
    }
}

},{"./glsl_version":"kG2vE","./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iNkBM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2DBackpropFilter", ()=>conv2DBackpropFilter);
parcelHelpers.export(exports, "conv2DBackpropFilterConfig", ()=>conv2DBackpropFilterConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convBackpropGpu = require("../conv_backprop_gpu");
function conv2DBackpropFilter(args) {
    const { inputs, backend, attrs } = args;
    const { x, dy } = inputs;
    const { strides, pad, dataFormat, dimRoundingMode, filterShape } = attrs;
    const $dataFormat = (0, _tfjsCore.backend_util).convertConv2DDataFormat(dataFormat);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */ , pad, dimRoundingMode, false, $dataFormat);
    const program = new (0, _convBackpropGpu.Conv2DDerFilterProgram)(convInfo);
    return backend.runWebGLProgram(program, [
        x,
        dy
    ], "float32");
}
const conv2DBackpropFilterConfig = {
    kernelName: (0, _tfjsCore.Conv2DBackpropFilter),
    backendName: "webgl",
    kernelFunc: conv2DBackpropFilter
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_backprop_gpu":"1yzYs","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1yzYs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Conv2DDerFilterProgram", ()=>Conv2DDerFilterProgram);
parcelHelpers.export(exports, "Conv2DDerInputProgram", ()=>Conv2DDerInputProgram);
parcelHelpers.export(exports, "Conv3DDerFilterProgram", ()=>Conv3DDerFilterProgram);
parcelHelpers.export(exports, "Conv3DDerInputProgram", ()=>Conv3DDerInputProgram);
class Conv2DDerFilterProgram {
    constructor(convInfo){
        this.variableNames = [
            "x",
            "dy"
        ];
        this.outputShape = convInfo.filterShape;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const padTop = convInfo.padInfo.top;
        const padLeft = convInfo.padInfo.left;
        const isChannelsLast = convInfo.dataFormat === "channelsLast";
        this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int d2 = coords.w;

        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
            int xR = wR + yR * ${strideHeight} - ${padTop};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
              int xC = wC + yC * ${strideWidth} - ${padLeft};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              ${isChannelsLast ? `float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);` : `float dyValue = getDy(b, d2, yR, yC);
              float xValue = getX(b, d1, xR, xC);
              dotProd += (xValue * dyValue);`}
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}
class Conv2DDerInputProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy",
            "W"
        ];
        this.outputShape = convInfo.inShape;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const isChannelsLast = convInfo.dataFormat === "channelsLast";
        const padTop = filterHeight - 1 - convInfo.padInfo.top;
        const padLeft = filterWidth - 1 - convInfo.padInfo.left;
        const rowDim = isChannelsLast ? 1 : 2;
        const colDim = isChannelsLast ? 2 : 3;
        const channelDim = isChannelsLast ? 3 : 1;
        this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[${channelDim}];

        ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${filterHeight} - 1 - wR;

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${filterWidth} - 1 - wC;

            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {

              if (${isChannelsLast}) {
                float xValue = getDy(batch, idyR, idyC, d2);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              } else {
                float xValue = getDy(batch, d2, idyR, idyC);
                float wValue = getW(wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }

            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}
class Conv3DDerFilterProgram {
    constructor(convInfo){
        this.variableNames = [
            "x",
            "dy"
        ];
        this.outputShape = convInfo.filterShape;
        const strideDepth = convInfo.strideDepth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const padFront = convInfo.padInfo.front;
        const padTop = convInfo.padInfo.top;
        const padLeft = convInfo.padInfo.left;
        this.userCode = `
      void main() {
        ivec5 coords = getOutputCoords();
        int wF = coords.x;
        int wR = coords.y;
        int wC = coords.z;
        int d1 = coords.w;
        int d2 = coords.u;

        float dotProd = 0.0;

        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
            int xF = wF + yF * ${strideDepth} - ${padFront};

            if (xF < 0 || xF >= ${convInfo.inDepth}) {
              continue;
            }

            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
              int xR = wR + yR * ${strideHeight} - ${padTop};

              if (xR < 0 || xR >= ${convInfo.inHeight}) {
                continue;
              }

              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
                int xC = wC + yC * ${strideWidth} - ${padLeft};

                if (xC < 0 || xC >= ${convInfo.inWidth}) {
                  continue;
                }

                float dyValue = getDy(b, yF, yR, yC, d2);
                float xValue = getX(b, xF, xR, xC, d1);
                dotProd += (xValue * dyValue);
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}
class Conv3DDerInputProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy",
            "W"
        ];
        this.outputShape = convInfo.inShape;
        const filterDepth = convInfo.filterDepth;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const strideDepth = convInfo.strideDepth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const padFront = filterDepth - 1 - convInfo.padInfo.front;
        const padTop = filterHeight - 1 - convInfo.padInfo.top;
        const padLeft = filterWidth - 1 - convInfo.padInfo.left;
        this.userCode = `
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.u;


        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyFCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        float dotProd = 0.0;
        for (int wF = 0; wF < ${filterDepth}; wF++) {
          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;

          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
            continue;
          }
          int idyF = int(dyF);

          int wFPerm = ${filterDepth} - 1 - wF;

          for (int wR = 0; wR < ${filterHeight}; wR++) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
              fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            int wRPerm = ${filterHeight} - 1 - wR;

            for (int wC = 0; wC < ${filterWidth}; wC++) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              int wCPerm = ${filterWidth} - 1 - wC;

              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
                float xValue = getDy(batch, idyF, idyR, idyC, d2);
                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
                dotProd += xValue * wValue;
              }
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jm349":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2DBackpropInput", ()=>conv2DBackpropInput);
parcelHelpers.export(exports, "conv2DBackpropInputConfig", ()=>conv2DBackpropInputConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convBackpropGpu = require("../conv_backprop_gpu");
var _convBackpropPackedGpu = require("../conv_backprop_packed_gpu");
function conv2DBackpropInput(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { inputShape, strides, pad, dataFormat, dimRoundingMode } = attrs;
    const $dataFormat = (0, _tfjsCore.backend_util).convertConv2DDataFormat(dataFormat);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */ , pad, dimRoundingMode, false, $dataFormat);
    if ((0, _tfjsCore.env)().getBool("WEBGL_PACK_CONV2DTRANSPOSE") && $dataFormat === "channelsLast") {
        const customValues = [
            [
                convInfo.strideHeight,
                convInfo.strideWidth
            ]
        ];
        const program = new (0, _convBackpropPackedGpu.Conv2DDerInputPackedProgram)(convInfo);
        return backend.runWebGLProgram(program, [
            dy,
            filter
        ], "float32", customValues);
    } else {
        const program = new (0, _convBackpropGpu.Conv2DDerInputProgram)(convInfo);
        return backend.runWebGLProgram(program, [
            dy,
            filter
        ], "float32");
    }
}
const conv2DBackpropInputConfig = {
    kernelName: (0, _tfjsCore.Conv2DBackpropInput),
    backendName: "webgl",
    kernelFunc: conv2DBackpropInput
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_backprop_gpu":"1yzYs","../conv_backprop_packed_gpu":"3Ktdq","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3Ktdq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Conv2DDerInputPackedProgram", ()=>Conv2DDerInputPackedProgram);
var _gpgpuMath = require("./gpgpu_math");
class Conv2DDerInputPackedProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy",
            "W"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "strides",
                type: "vec2"
            }
        ];
        this.outputShape = convInfo.inShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const padTop = filterHeight - 1 - convInfo.padInfo.top;
        const padLeft = filterWidth - 1 - convInfo.padInfo.left;
        this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];

        ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        vec4 result = vec4(0.);
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          float dyR = float(dyRCorner + wR) / strides[0];
          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);
          int wRPerm = ${filterHeight} - 1 - wR;

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int wCPerm = ${filterWidth} - 1 - wC;

            float dyC = float(dyCCorner + wC) / strides[1];
            bool idyCVal = (dyC >= 0.0) && (dyC < ${convInfo.outWidth}.0)
              && (fract(dyC) == 0.0);
            int idyC = int(dyC);

            float dyC2 = float(dyCCorner + wC + 1) / strides[1];
            bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${convInfo.outWidth}.0)
              && (fract(dyC2) == 0.0);
            int idyC2 = int(dyC2);

            if (idyCVal && idyCVal2) {
              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?
                  dySample : getDy(batch, idyR, idyC2, d2);

                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));

                dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample2.xy : dySample2.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal) {
              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC, d2);
                vec2 dyValue = mod(float(idyC), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.xy += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            } else if (idyCVal2) {
              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2 += 2) {
                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);
                vec4 dySample = getDy(batch, idyR, idyC2, d2);
                vec2 dyValue = mod(float(idyC2), 2.) == 0. ?
                  dySample.xy : dySample.zw;
                result.zw += vec2(dot(dyValue, wValue.xy),
                  dot(dyValue, wValue.zw));
              }
            }
          }
        }
        setOutput(result);
      }
    `;
    }
}

},{"./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c8Gvg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3D", ()=>conv3D);
parcelHelpers.export(exports, "conv3DConfig", ()=>conv3DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convGpu = require("../conv_gpu");
function conv3D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dilations } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);
    const program = new (0, _convGpu.Conv3DProgram)(convInfo);
    return backend.runWebGLProgram(program, [
        x,
        filter
    ], "float32");
}
const conv3DConfig = {
    kernelName: (0, _tfjsCore.Conv3D),
    backendName: "webgl",
    kernelFunc: conv3D
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_gpu":"3pfYb","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lV1Z3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3DBackpropFilterV2", ()=>conv3DBackpropFilterV2);
parcelHelpers.export(exports, "conv3DBackpropFilterV2Config", ()=>conv3DBackpropFilterV2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convBackpropGpu = require("../conv_backprop_gpu");
function conv3DBackpropFilterV2(args) {
    const { inputs, backend, attrs } = args;
    const { x, dy } = inputs;
    const { strides, pad, filterShape } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */ , pad);
    const program = new (0, _convBackpropGpu.Conv3DDerFilterProgram)(convInfo);
    return backend.runWebGLProgram(program, [
        x,
        dy
    ], "float32");
}
const conv3DBackpropFilterV2Config = {
    kernelName: (0, _tfjsCore.Conv3DBackpropFilterV2),
    backendName: "webgl",
    kernelFunc: conv3DBackpropFilterV2
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_backprop_gpu":"1yzYs","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fnJFd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3DBackpropInput", ()=>conv3DBackpropInput);
parcelHelpers.export(exports, "conv3DBackpropInputConfig", ()=>conv3DBackpropInputConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convBackpropGpu = require("../conv_backprop_gpu");
function conv3DBackpropInput(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { pad, strides, inputShape } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */ , pad);
    const program = new (0, _convBackpropGpu.Conv3DDerInputProgram)(convInfo);
    return backend.runWebGLProgram(program, [
        dy,
        filter
    ], "float32");
}
const conv3DBackpropInputConfig = {
    kernelName: (0, _tfjsCore.Conv3DBackpropInputV2),
    backendName: "webgl",
    kernelFunc: conv3DBackpropInput
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_backprop_gpu":"1yzYs","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2H7EX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cos", ()=>cos);
parcelHelpers.export(exports, "cosConfig", ()=>cosConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const COS = (0, _kernelFuncsUtils.CHECK_NAN_SNIPPET_UNARY) + `
  return cos(x);
`;
const COS_PACKED = `
  vec4 result = cos(x);
  bvec4 isNaN = isnan(x);
  ${(0, _binaryopPackedGpu.CHECK_NAN_SNIPPET_PACKED)}
  return result;
`;
const cos = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: COS,
    packedOpSnippet: COS_PACKED
});
const cosConfig = {
    kernelName: (0, _tfjsCore.Cos),
    backendName: "webgl",
    kernelFunc: cos
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_packed_gpu":"hkzp7","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jBLJs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cosh", ()=>cosh);
parcelHelpers.export(exports, "coshConfig", ()=>coshConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const COSH = `
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`;
const cosh = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: COSH
});
const coshConfig = {
    kernelName: (0, _tfjsCore.Cosh),
    backendName: "webgl",
    kernelFunc: cosh
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aCuUK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cropAndResize", ()=>cropAndResize);
parcelHelpers.export(exports, "cropAndResizeConfig", ()=>cropAndResizeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cropAndResizeGpu = require("../crop_and_resize_gpu");
const cropAndResize = (args)=>{
    const { inputs, backend, attrs } = args;
    const { image, boxes, boxInd } = inputs;
    const { cropSize, method, extrapolationValue } = attrs;
    const program = new (0, _cropAndResizeGpu.CropAndResizeProgram)(image.shape, boxes.shape, cropSize, method, extrapolationValue);
    return backend.runWebGLProgram(program, [
        image,
        boxes,
        boxInd
    ], "float32");
};
const cropAndResizeConfig = {
    kernelName: (0, _tfjsCore.CropAndResize),
    backendName: "webgl",
    kernelFunc: cropAndResize
};

},{"@tensorflow/tfjs-core":"21Ftl","../crop_and_resize_gpu":"86aq9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"86aq9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "CropAndResizeProgram", ()=>CropAndResizeProgram);
class CropAndResizeProgram {
    constructor(imageShape, boxShape, cropSize, method, extrapolationValue){
        this.variableNames = [
            "Image",
            "Boxes",
            "BoxInd"
        ];
        this.outputShape = [];
        const [batch, imageHeight, imageWidth, depth] = imageShape;
        const [numBoxes] = boxShape;
        const [cropHeight, cropWidth] = cropSize;
        this.outputShape = [
            numBoxes,
            cropHeight,
            cropWidth,
            depth
        ];
        const methodId = method === "bilinear" ? 1 : 0;
        const [inputHeightFloat, inputWidthFloat] = [
            `${imageHeight - 1}.0`,
            `${imageWidth - 1}.0`
        ];
        const [heightRatio, heightScale, inY] = cropHeight > 1 ? [
            `${(imageHeight - 1) / (cropHeight - 1)}`,
            "(y2-y1) * height_ratio",
            `y1*${inputHeightFloat} + float(y)*(height_scale)`
        ] : [
            "0.0",
            "0.0",
            `0.5 * (y1+y2) * ${inputHeightFloat}`
        ];
        const [widthRatio, widthScale, inX] = cropWidth > 1 ? [
            `${(imageWidth - 1) / (cropWidth - 1)}`,
            "(x2-x1) * width_ratio",
            `x1*${inputWidthFloat} + float(x)*(width_scale)`
        ] : [
            "0.0",
            "0.0",
            `0.5 * (x1+x2) * ${inputWidthFloat}`
        ];
        // Reference implementation
        // tslint:disable-next-line:max-line-length
        // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc
        this.userCode = `
      const float height_ratio = float(${heightRatio});
      const float width_ratio = float(${widthRatio});
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int y = coords[1];
        int x = coords[2];
        int d = coords[3];

        // get box vals
        float y1 = getBoxes(b,0);
        float x1 = getBoxes(b,1);
        float y2 = getBoxes(b,2);
        float x2 = getBoxes(b,3);

        // get image in batch index
        int bInd = round(getBoxInd(b));
        if(bInd < 0 || bInd >= ${batch}) {
          return;
        }

        float height_scale = ${heightScale};
        float width_scale = ${widthScale};

        float in_y = ${inY};
        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
          setOutput(float(${extrapolationValue}));
          return;
        }
        float in_x = ${inX};
        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
          setOutput(float(${extrapolationValue}));
          return;
        }

        vec2 sourceFracIndexCR = vec2(in_x,in_y);
        if(${methodId} == 1) {
          // Compute the four integer indices.
          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));

          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);

          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);

          float top = topLeft + (topRight - topLeft) * fracCR.x;
          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
          float newValue = top + (bottom - top) * fracCR.y;
          setOutput(newValue);
        } else {
          // Compute the coordinators of nearest neighbor point.
          ivec2 sourceNearestCR = ivec2(floor(
            sourceFracIndexCR + vec2(0.5,0.5)));
          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
          setOutput(newValue);
        }
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fIbv6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cumprod", ()=>cumprod);
parcelHelpers.export(exports, "cumprodConfig", ()=>cumprodConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cumGpu = require("../cum_gpu");
var _cumImpl = require("./Cum_impl");
function cumprod(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, exclusive, reverse } = attrs;
    return (0, _cumImpl.cumImpl)((0, _cumGpu.CumOpType).Prod, x, backend, axis, exclusive, reverse);
}
const cumprodConfig = {
    kernelName: (0, _tfjsCore.Cumprod),
    backendName: "webgl",
    kernelFunc: cumprod
};

},{"@tensorflow/tfjs-core":"21Ftl","../cum_gpu":"2Zgho","./Cum_impl":"4IsXf","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2Zgho":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "CumOpType", ()=>CumOpType);
parcelHelpers.export(exports, "CumProgram", ()=>CumProgram);
var _shaderCompiler = require("./shader_compiler");
var CumOpType;
(function(CumOpType) {
    CumOpType["Prod"] = "*";
    CumOpType["Sum"] = "+";
})(CumOpType || (CumOpType = {}));
class CumProgram {
    constructor(op, outputShape, exclusive, reverse){
        this.op = op;
        this.outputShape = outputShape;
        this.variableNames = [
            "x"
        ];
        this.customUniforms = [
            {
                name: "index",
                type: "float"
            }
        ];
        const rank = this.outputShape.length;
        const initVal = this.op === CumOpType.Prod ? "1.0" : "0.0";
        const val = exclusive ? initVal : `getX(${getCoords(rank, "coords", this.op)})`;
        const length = this.outputShape[this.outputShape.length - 1];
        let condition = "";
        let idxString = "";
        // When exclusive is set, the cum op becomes roll op that copies the
        // value from the previous index based on the direction specified by the
        // reverse flag.
        if (exclusive) {
            condition = reverse ? `end != ${length - 1}` : "end != 0";
            idxString = reverse ? "end + 1" : "end - 1";
        } else {
            condition = reverse ? `end + pow2 < ${length}` : "end >= pow2";
            idxString = reverse ? "end + pow2" : "end - pow2";
        }
        this.userCode = `
      void main() {
        ${(0, _shaderCompiler.getCoordsDataType)(rank)} coords = getOutputCoords();
        int end = ${getFinalCoord(rank, "coords", this.op)};
        float val = ${val};
        int pow2 = int(pow(2.0, index));
        if (${condition}) {
          int idx = ${idxString};
          ${getFinalCoord(rank, "coords", this.op)} = idx;
          val ${this.op}= getX(${getCoords(rank, "coords", this.op)});
        }
        setOutput(val);
      }
    `;
    }
}
function getCoords(rank, name, op) {
    if (rank === 1) return `${name}`;
    else if (rank === 2) return `${name}.x, ${name}.y`;
    else if (rank === 3) return `${name}.x, ${name}.y, ${name}.z`;
    else if (rank === 4) return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
    else throw new Error(`Cumulative ${op} for rank ${rank} is not yet supported`);
}
function getFinalCoord(rank, name, op) {
    if (rank === 1) return `${name}`;
    else if (rank === 2) return `${name}.y`;
    else if (rank === 3) return `${name}.z`;
    else if (rank === 4) return `${name}.w`;
    else throw new Error(`Cumulative ${op} for rank ${rank} is not yet supported`);
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4IsXf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cumImpl", ()=>cumImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cumGpu = require("../cum_gpu");
var _identity = require("./Identity");
var _transpose = require("./Transpose");
function cumImpl(op, x, backend, axis, exclusive, reverse) {
    const xRank = x.shape.length;
    const permutation = (0, _tfjsCore.backend_util).getAxesPermutation([
        axis
    ], xRank);
    let permutedX = x;
    if (permutation != null) permutedX = (0, _transpose.transpose)({
        inputs: {
            x
        },
        backend,
        attrs: {
            perm: permutation
        }
    });
    const permutedAxis = (0, _tfjsCore.backend_util).getInnerMostAxes(1, xRank)[0];
    if (permutedAxis !== xRank - 1) throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} ` + `but got axis=${axis}`);
    const size = permutedX.shape[permutedAxis];
    let result = (0, _identity.identity)({
        inputs: {
            x: permutedX
        },
        backend
    });
    // Use cum parallel algorithm, inspired by:
    // https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-39-parallel-prefix-sum-scan-cuda
    // Note: although the algorithm is called sum, it works for any associtative
    // operator with an identity.
    for(let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++){
        const program = new (0, _cumGpu.CumProgram)(op, permutedX.shape, false, reverse);
        const customValues = [
            [
                i
            ]
        ];
        const prevResult = result;
        result = backend.runWebGLProgram(program, [
            result
        ], result.dtype, customValues);
        backend.disposeIntermediateTensorInfo(prevResult);
    }
    // For exclusive cum, shift the end result in the direction of product or sum
    // and add 1 for product or 0 for sum to the front index.
    if (exclusive) {
        const program = new (0, _cumGpu.CumProgram)(op, permutedX.shape, exclusive, reverse);
        const prevResult = result;
        result = backend.runWebGLProgram(program, [
            result
        ], result.dtype);
        backend.disposeIntermediateTensorInfo(prevResult);
    }
    if (permutation != null) {
        const reversePermutation = (0, _tfjsCore.backend_util).getUndoAxesPermutation(permutation);
        const reverseTransposedResult = (0, _transpose.transpose)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                perm: reversePermutation
            }
        });
        backend.disposeIntermediateTensorInfo(result);
        backend.disposeIntermediateTensorInfo(permutedX);
        return reverseTransposedResult;
    }
    return result;
}

},{"@tensorflow/tfjs-core":"21Ftl","../cum_gpu":"2Zgho","./Identity":"hY9aT","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l2jIP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cumsum", ()=>cumsum);
parcelHelpers.export(exports, "cumsumConfig", ()=>cumsumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cumGpu = require("../cum_gpu");
var _cumImpl = require("./Cum_impl");
function cumsum(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, exclusive, reverse } = attrs;
    return (0, _cumImpl.cumImpl)((0, _cumGpu.CumOpType).Sum, x, backend, axis, exclusive, reverse);
}
const cumsumConfig = {
    kernelName: (0, _tfjsCore.Cumsum),
    backendName: "webgl",
    kernelFunc: cumsum
};

},{"@tensorflow/tfjs-core":"21Ftl","../cum_gpu":"2Zgho","./Cum_impl":"4IsXf","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7qaEa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "denseBincount", ()=>denseBincount);
parcelHelpers.export(exports, "denseBincountConfig", ()=>denseBincountConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function denseBincount(args) {
    const { inputs, backend, attrs } = args;
    const { x, weights } = inputs;
    const { size, binaryOutput } = attrs;
    if (x.shape.length === 1) {
        const xVals = backend.readSync(x.dataId);
        const weightsVals = backend.readSync(weights.dataId);
        const outVals = (0, _shared.bincountImplCPU)(xVals, weightsVals, weights.dtype, weights.shape, size);
        return backend.makeTensorInfo([
            size
        ], weights.dtype, outVals);
    } else if (x.shape.length === 2) {
        const xBuf = backend.bufferSync(x);
        const weightsBuf = backend.bufferSync(weights);
        const outBuf = (0, _shared.bincountReduceImplCPU)(xBuf, weightsBuf, size, binaryOutput);
        return backend.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
    }
    throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank` + `${x.shape.length}.`);
}
const denseBincountConfig = {
    kernelName: (0, _tfjsCore.DenseBincount),
    backendName: "webgl",
    kernelFunc: denseBincount
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"926cY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthToSpace", ()=>depthToSpace);
parcelHelpers.export(exports, "depthToSpaceConfig", ()=>depthToSpaceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _depthToSpaceGpu = require("../depth_to_space_gpu");
function depthToSpace(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { blockSize, dataFormat } = attrs;
    const batchSize = x.shape[0];
    const inputHeight = dataFormat === "NHWC" ? x.shape[1] : x.shape[2];
    const inputWidth = dataFormat === "NHWC" ? x.shape[2] : x.shape[3];
    const inputDepth = dataFormat === "NHWC" ? x.shape[3] : x.shape[1];
    const outputHeight = inputHeight * blockSize;
    const outputWidth = inputWidth * blockSize;
    const outputDepth = inputDepth / (blockSize * blockSize);
    const outputShape = dataFormat === "NHWC" ? [
        batchSize,
        outputHeight,
        outputWidth,
        outputDepth
    ] : [
        batchSize,
        outputDepth,
        outputHeight,
        outputWidth
    ];
    const program = new (0, _depthToSpaceGpu.DepthToSpaceProgram)(outputShape, blockSize, dataFormat);
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype);
}
const depthToSpaceConfig = {
    kernelName: (0, _tfjsCore.DepthToSpace),
    backendName: "webgl",
    kernelFunc: depthToSpace
};

},{"@tensorflow/tfjs-core":"21Ftl","../depth_to_space_gpu":"Zy6T9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"Zy6T9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DepthToSpaceProgram", ()=>DepthToSpaceProgram);
class DepthToSpaceProgram {
    constructor(outputShape, blockSize, dataFormat){
        this.variableNames = [
            "x"
        ];
        this.outputShape = [];
        this.outputShape = outputShape;
        this.blockSize = blockSize;
        this.dataFormat = dataFormat;
        this.userCode = `
    void main() {
      ivec4 coords = getOutputCoords();
      int b = coords[0];
      int h = ${this.getHeightCoordString()};
      int w = ${this.getWidthCoordString()};
      int d = ${this.getDepthCoordString()};

      int in_h = h / ${blockSize};
      int offset_h = imod(h, ${blockSize});
      int in_w = w / ${blockSize};
      int offset_w = imod(w, ${blockSize});
      int offset_d = (offset_h * ${blockSize} + offset_w) *
        ${this.getOutputDepthSize()};
      int in_d = d + offset_d;

      float result = ${this.getInputSamplingString()};
      setOutput(result);
    }
  `;
    }
    getHeightCoordString() {
        if (this.dataFormat === "NHWC") return `coords[1]`;
        else return `coords[2]`;
    }
    getWidthCoordString() {
        if (this.dataFormat === "NHWC") return `coords[2]`;
        else return `coords[3]`;
    }
    getDepthCoordString() {
        if (this.dataFormat === "NHWC") return `coords[3]`;
        else return `coords[1]`;
    }
    getOutputDepthSize() {
        if (this.dataFormat === "NHWC") return this.outputShape[3];
        else return this.outputShape[1];
    }
    getInputSamplingString() {
        if (this.dataFormat === "NHWC") return `getX(b, in_h, in_w, in_d)`;
        else return `getX(b, in_d, in_h, in_w)`;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9xbgW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNative", ()=>depthwiseConv2dNative);
parcelHelpers.export(exports, "depthwiseConv2dNativeConfig", ()=>depthwiseConv2dNativeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convGpuDepthwise = require("../conv_gpu_depthwise");
var _convPackedGpuDepthwise = require("../conv_packed_gpu_depthwise");
function depthwiseConv2dNative(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dilations, dimRoundingMode } = attrs;
    let $dilations = dilations;
    if ($dilations == null) $dilations = [
        1,
        1
    ];
    (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, $dilations), ()=>"Error in depthwiseConv2d: Either strides or dilations must be " + `1. Got strides ${strides} and dilations '${$dilations}'`);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true);
    let program;
    if ((0, _tfjsCore.env)().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) program = new (0, _convPackedGpuDepthwise.DepthwiseConvPacked2DProgram)(convInfo);
    else program = new (0, _convGpuDepthwise.DepthwiseConv2DProgram)(convInfo);
    const customValues = [
        [
            convInfo.padInfo.top,
            convInfo.padInfo.left
        ],
        [
            convInfo.strideHeight,
            convInfo.strideWidth
        ],
        [
            convInfo.dilationHeight,
            convInfo.dilationWidth
        ],
        [
            convInfo.inHeight,
            convInfo.inWidth
        ]
    ];
    return backend.runWebGLProgram(program, [
        x,
        filter
    ], "float32", customValues);
}
const depthwiseConv2dNativeConfig = {
    kernelName: (0, _tfjsCore.DepthwiseConv2dNative),
    backendName: "webgl",
    kernelFunc: depthwiseConv2dNative
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_gpu_depthwise":"31MV7","../conv_packed_gpu_depthwise":"aG31s","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"31MV7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DepthwiseConv2DProgram", ()=>DepthwiseConv2DProgram);
var _gpgpuMath = require("./gpgpu_math");
class DepthwiseConv2DProgram {
    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false, hasLeakyReluAlpha = false){
        this.variableNames = [
            "x",
            "W"
        ];
        this.customUniforms = [
            {
                name: "pads",
                type: "ivec2"
            },
            {
                name: "strides",
                type: "ivec2"
            },
            {
                name: "dilations",
                type: "ivec2"
            },
            {
                name: "inDims",
                type: "ivec2"
            }
        ];
        this.outputShape = convInfo.outShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const channelMul = convInfo.outChannels / convInfo.inChannels;
        let activationSnippet = "", applyActivationSnippet = "";
        if (activation) {
            if (hasPreluActivation) activationSnippet = `float activation(float a) {
          float b = getPreluActivationWeightsAtOutCoords();
          ${activation}
        }`;
            else if (hasLeakyReluAlpha) activationSnippet = `float activation(float a) {
          float b = getLeakyreluAlphaAtOutCoords();
          ${activation}
        }`;
            else activationSnippet = `
          float activation(float x) {
            ${activation}
          }
        `;
            applyActivationSnippet = `result = activation(result);`;
        }
        const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
        if (addBias) this.variableNames.push("bias");
        if (hasPreluActivation) this.variableNames.push("preluActivationWeights");
        if (hasLeakyReluAlpha) this.variableNames.push("leakyreluAlpha");
        this.userCode = `
      ${activationSnippet}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${channelMul};
        int q = d2 - d1 * ${channelMul};

        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
        for (int wR = 0; wR < ${filterHeight}; wR++) {
          int xR = xRCorner + wR * dilations[0];

          if (xR < 0 || xR >= inDims[0]) {
            continue;
          }

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            int xC = xCCorner + wC * dilations[1];

            if (xC < 0 || xC >= inDims[1]) {
              continue;
            }

            float xVal = getX(batch, xR, xC, d1);
            float wVal = getW(wR, wC, d1, q);
            dotProd += xVal * wVal;
          }
        }

        float result = dotProd;
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `;
    }
}

},{"./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aG31s":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DepthwiseConvPacked2DProgram", ()=>DepthwiseConvPacked2DProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gpgpuMath = require("./gpgpu_math");
class DepthwiseConvPacked2DProgram {
    constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false, hasLeakyReluAlpha = false){
        this.variableNames = [
            "x",
            "W"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "pads",
                type: "ivec2"
            },
            {
                name: "strides",
                type: "ivec2"
            },
            {
                name: "dilations",
                type: "ivec2"
            },
            {
                name: "inDims",
                type: "ivec2"
            }
        ];
        this.outputShape = convInfo.outShape;
        this.enableShapeUniforms = (0, _gpgpuMath.useShapeUniforms)(this.outputShape.length);
        const channelMul = convInfo.outChannels / convInfo.inChannels;
        const padLeft = convInfo.padInfo.left;
        const strideWidth = convInfo.strideWidth;
        const dilationWidth = convInfo.dilationWidth;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const texelsAcross = filterWidth;
        let mainLoop = `
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;
        for(let c = 0; c < filterWidth; c++)mainLoop += `
          vec4 xTexelC${c * 2};
          int xTexelC${c * 2}Ready;
          vec4 xTexelC${c * 2 + 1};
          int xTexelC${c * 2 + 1}Ready;
          vec4 xC${c};`;
        /**
         * This vectorized implementation works by gathering the values needed for
         * each output channel's dot product into vec4's and then multiplying them
         * all together (this happens in the final double for-loop below). Most of
         * the main loop consists of constructing these vec4's with the minimum
         * number of texture2D calls, which means making use of all four returned
         * values from a texture2D call at once.
         */ mainLoop += `
    for (int r = 0; r < ${filterHeight}; r++) {
      `;
        for(let c = 0; c < filterWidth; c++)mainLoop += `
          xTexelC${c * 2} = vec4(0.0);
          xTexelC${c * 2}Ready = 0;
          xTexelC${c * 2 + 1} = vec4(0.0);
          xTexelC${c * 2 + 1}Ready = 0;
          xC${c} = vec4(0.0);`;
        mainLoop += `
        xR = xRCorner + r * dilations[0];
        if (xR >=0 && xR < inDims[0]) {
      `;
        for(let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++){
            const colIndex = texelC * 2;
            mainLoop += `
          xC = xCCorner + ${colIndex * dilationWidth};
          `;
            if (strideWidth === 1) {
                if (colIndex < filterWidth) {
                    // If padding is odd, the outer texels have to be composed.
                    if (padLeft % 2 === 1) {
                        // TODO: Ensure vec4 previous does not result in redundant sample,
                        // and avoid setting xTexelRC's that exceed the boundary in the
                        // first place rather than resetting them to vec4(0)).
                        // To compute xCOffset:
                        // - If padding is odd, we must add 1 to ensure we ask for an
                        // even-numbered row.
                        // - We subtract 2 to access the previous texel.
                        mainLoop += `
                xCOffset = xC + 1;
                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);

                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${colIndex}.zw = vec2(0.0);
                  }
                  xTexelC${colIndex}Ready = 1;
                }
              `;
                        // This texel has been read in previous iteration if the dilation
                        // is 1.
                        if (dilationWidth === 1 && colIndex > 0) mainLoop += `
                xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);
                `;
                        else mainLoop += `
                  xCOffset = xC + 1 - 2;

                  if (xCOffset >= 0 && xCOffset < inDims[1]) {
                    previous = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      previous.zw = vec2(0.0);
                    }

                    xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);
                  } else {
                    xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);
                  }
                  `;
                    } else // Padding is even, so xRC corresponds to a single texel.
                    mainLoop += `
                if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${colIndex}.zw = vec2(0.0);
                  }
                  xTexelC${colIndex}Ready = 1;
                }

                xC${colIndex} = xTexelC${colIndex};
                `;
                    if (colIndex + 1 < filterWidth) {
                        // If dilation is even, the second entry should match the first
                        // (either both are composed or both are single samples). But if
                        // dilation is odd, then the second entry should be the opposite
                        // of the first (if the first is composed, the second is a single
                        // sample, and vice versa.)
                        const nextTexelOffset = padLeft % 2 === 0 ? (0, _tfjsCore.util).nearestLargerEven(dilationWidth) : dilationWidth;
                        if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {
                            mainLoop += `
                  xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};

                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                    xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);

                    // Need to manually clear unused channels in case
                    // we're reading from recycled texture.
                    if (xCOffset + 1 >= inDims[1]) {
                      xTexelC${colIndex + 1}.zw = vec2(0.0);
                    }
                    xTexelC${colIndex + 1}Ready = 1;
                  }
                  `;
                            // If dilation > 1 then the xRC's will not be able to share any
                            // values, so each xRC will require two unique calls to getX.
                            if (dilationWidth > 1) mainLoop += `
                    xCOffset -= 2;
                    if (xCOffset >= 0 && xCOffset < inDims[1]) {
                     previous = getX(batch, xR, xCOffset, d1);
                     xC${colIndex + 1} = vec4(previous.zw, xTexelC${colIndex + 1}.xy);
                    } else {
                     xC${colIndex + 1} = vec4(0.0, 0.0, xTexelC${colIndex + 1}.xy);
                    }
                    `;
                            else mainLoop += `
                    xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);
                    `;
                        } else // If dilation is 1 and padding is odd, we have already read the
                        // texel when constructing the previous x value. Here we can
                        // simply skip the texture read.
                        if (nextTexelOffset === 1) mainLoop += `
                    xC${colIndex + 1} = xTexelC${colIndex};
                    `;
                        else mainLoop += `
                    xCOffset = xC + ${nextTexelOffset};

                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                      xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
                      if (xCOffset + 1 >= inDims[1]) {
                        xTexelC${colIndex + 1}.zw = vec2(0.0);
                      }
                      xTexelC${colIndex + 1}Ready = 1;
                    }

                    xC${colIndex + 1} = xTexelC${colIndex + 1};
                    `;
                    }
                }
            } else if (colIndex < filterWidth) {
                // Depending on whether padLeft is even or odd, we want either the
                // xy or zw channels from X texels for xC${colIndex}. If padLeft is
                // even, xC${colIndex +1} is simply the zw channels of texels we've
                // already sampled. But if padLeft is odd, xC{$c + 1}.zw will
                // need to come from the xy channels of a new texel, hence the `
                // vec4
                // final` initialized below.
                if (padLeft % 2 === 1) {
                    mainLoop += `
                xCOffset = xC + 1 - strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
                  xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${colIndex}.zw = vec2(0.0);
                  }
                  xTexelC${colIndex}Ready = 1;
                }

                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                  xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);
                  // Need to manually clear unused channels in case
                  // we're reading from recycled texture.
                  if (xC + 2 >= inDims[1]) {
                    xTexelC${colIndex + 1}.zw = vec2(0.0);
                  }
                  xTexelC${colIndex + 1}Ready = 1;
                }

                xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
              `;
                    if (colIndex + 1 < filterWidth) mainLoop += `
                  final = vec4(0.0);
                  xCOffset = xC + 1 + strides[1];
                  if(xCOffset >= 0 && xCOffset < inDims[1]) {
                    final = getX(batch, xR, xCOffset, d1);
                  }
                  xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);
                `;
                } else {
                    mainLoop += `
                if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
                  xTexelC${colIndex} = getX(batch, xR, xC, d1);
                  if (xC + 1 >= inDims[1]) {
                    xTexelC${colIndex}.zw = vec2(0.0);
                  }
                  xTexelC${colIndex}Ready = 1;
                }

                xCOffset = xC + strides[1];
                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
                  xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
                  if (xCOffset + 1 >= inDims[1]) {
                    xTexelC${colIndex + 1}.zw = vec2(0.);
                  }
                  xTexelC${colIndex + 1}Ready = 1;
                }

                xC${colIndex} = vec4(
                  xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);
              `;
                    if (colIndex + 1 < filterWidth) mainLoop += `
                  xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
                `;
                }
            }
            // localize the dotProd accumulation within the loop, the theory is for
            // GPU with limited cache, accumulate sum across large amount of
            // veriables will cause lots of cache misses. (i.e. 5x5 filter will have
            // 50 variables)
            if (colIndex < filterWidth) {
                mainLoop += `
            wTexel = getW(r, ${colIndex}, d1, q);
            dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);
          `;
                if (colIndex + 1 < filterWidth) mainLoop += `
              wTexel = getW(r, ${colIndex + 1}, d1, q);
              dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);
            `;
            }
        }
        mainLoop += `
    }
  `;
        mainLoop += `
      }
    `;
        let activationSnippet = "", applyActivationSnippet = "";
        if (activation) {
            if (hasPreluActivation) activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getPreluActivationWeightsAtOutCoords();
          ${activation}
        }`;
            else if (hasLeakyReluAlpha) activationSnippet = `vec4 activation(vec4 a) {
          vec4 b = getLeakyreluAlphaAtOutCoords();
          ${activation}
        }`;
            else activationSnippet = `vec4 activation(vec4 x) {
          ${activation}
        }`;
            applyActivationSnippet = `result = activation(result);`;
        }
        const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
        if (addBias) this.variableNames.push("bias");
        if (hasPreluActivation) this.variableNames.push("preluActivationWeights");
        if (hasLeakyReluAlpha) this.variableNames.push("leakyreluAlpha");
        this.userCode = `
      ${activationSnippet}

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        ivec2 xRCCorner = coords.yz * strides - pads;
        int d2 = coords.w;
        int d1 = d2 / ${channelMul};
        int q = d2 - d1 * ${channelMul};
        int xRCorner = xRCCorner.x;
        int xCCorner = xRCCorner.y;

        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
        vec4 dotProd = vec4(0.000000000000001);

        ${mainLoop}

        vec4 result = dotProd - vec4(0.000000000000001);
        ${addBiasSnippet}
        ${applyActivationSnippet}
        setOutput(result);
      }
    `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","./gpgpu_math":"btJo5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aWI5l":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropFilter", ()=>depthwiseConv2dNativeBackpropFilter);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropFilterConfig", ()=>depthwiseConv2dNativeBackpropFilterConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convBackpropGpuDepthwise = require("../conv_backprop_gpu_depthwise");
function depthwiseConv2dNativeBackpropFilter(args) {
    const { inputs, backend, attrs } = args;
    const { x, dy } = inputs;
    const { strides, dilations, pad, dimRoundingMode, filterShape } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true);
    const program = new (0, _convBackpropGpuDepthwise.DepthwiseConv2DDerFilterProgram)(convInfo);
    return backend.runWebGLProgram(program, [
        x,
        dy
    ], "float32");
}
const depthwiseConv2dNativeBackpropFilterConfig = {
    kernelName: (0, _tfjsCore.DepthwiseConv2dNativeBackpropFilter),
    backendName: "webgl",
    kernelFunc: depthwiseConv2dNativeBackpropFilter
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_backprop_gpu_depthwise":"8UMrV","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8UMrV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DepthwiseConv2DDerFilterProgram", ()=>DepthwiseConv2DDerFilterProgram);
parcelHelpers.export(exports, "DepthwiseConv2DDerInputProgram", ()=>DepthwiseConv2DDerInputProgram);
class DepthwiseConv2DDerFilterProgram {
    constructor(convInfo){
        this.variableNames = [
            "x",
            "dy"
        ];
        this.outputShape = convInfo.filterShape;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const padTop = convInfo.padInfo.top;
        const padLeft = convInfo.padInfo.left;
        const channelMul = convInfo.outChannels / convInfo.inChannels;
        this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int wR = coords.x;
        int wC = coords.y;
        int d1 = coords.z;
        int dm = coords.w;
        int d2 = d1 * ${channelMul} + dm;

        float dotProd = 0.0;

        // TO DO: Vec4 over the batch size
        for (int b = 0; b < ${convInfo.batchSize}; b++) {
          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
            int xR = wR + yR * ${strideHeight} - ${padTop};

            if (xR < 0 || xR >= ${convInfo.inHeight}) {
              continue;
            }

            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
              int xC = wC + yC * ${strideWidth} - ${padLeft};

              if (xC < 0 || xC >= ${convInfo.inWidth}) {
                continue;
              }

              float dyValue = getDy(b, yR, yC, d2);
              float xValue = getX(b, xR, xC, d1);
              dotProd += (xValue * dyValue);
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}
class DepthwiseConv2DDerInputProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy",
            "W"
        ];
        this.outputShape = convInfo.inShape;
        const filterHeight = convInfo.filterHeight;
        const filterWidth = convInfo.filterWidth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const padTop = filterHeight - 1 - convInfo.padInfo.top;
        const padLeft = filterWidth - 1 - convInfo.padInfo.left;
        const channelMul = convInfo.outChannels / convInfo.inChannels;
        this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords[0];
        int d1 = coords[3];
        ivec2 dyCorner = coords.yz - pads;
        int dyRCorner = dyCorner.x;
        int dyCCorner = dyCorner.y;

        float dotProd = 0.0;

        for (int wR = 0; wR < ${filterHeight}; wR++) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          int wRPerm = ${filterHeight} - 1 - wR;

          for (int wC = 0; wC < ${filterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            int wCPerm = ${filterWidth} - 1 - wC;

            // TO DO: Vec4 over the channelMul
            for (int dm = 0; dm < ${channelMul}; dm++) {
              int d2 = d1 * ${channelMul} + dm;
              float xValue = getDy(batch, idyR, idyC, d2);
              float wValue = getW(wRPerm, wCPerm, d1, dm);
              dotProd += xValue * wValue;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6HZVm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropInput", ()=>depthwiseConv2dNativeBackpropInput);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropInputConfig", ()=>depthwiseConv2dNativeBackpropInputConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convBackpropGpuDepthwise = require("../conv_backprop_gpu_depthwise");
function depthwiseConv2dNativeBackpropInput(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { strides, dilations, pad, dimRoundingMode, inputShape } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true);
    const program = new (0, _convBackpropGpuDepthwise.DepthwiseConv2DDerInputProgram)(convInfo);
    return backend.runWebGLProgram(program, [
        dy,
        filter
    ], "float32");
}
const depthwiseConv2dNativeBackpropInputConfig = {
    kernelName: (0, _tfjsCore.DepthwiseConv2dNativeBackpropInput),
    backendName: "webgl",
    kernelFunc: depthwiseConv2dNativeBackpropInput
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_backprop_gpu_depthwise":"8UMrV","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7TZuU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "diag", ()=>diag);
parcelHelpers.export(exports, "diagConfig", ()=>diagConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _diagGpu = require("../diag_gpu");
var _reshape = require("./Reshape");
function diag(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    const outShape = [
        ...x.shape,
        ...x.shape
    ];
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const flat = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: [
                xSize
            ]
        }
    });
    const program = new (0, _diagGpu.DiagProgram)(xSize);
    const res = backend.runWebGLProgram(program, [
        flat
    ], flat.dtype);
    const out = (0, _reshape.reshape)({
        inputs: {
            x: res
        },
        backend,
        attrs: {
            shape: outShape
        }
    });
    backend.disposeIntermediateTensorInfo(flat);
    backend.disposeIntermediateTensorInfo(res);
    return out;
}
const diagConfig = {
    kernelName: (0, _tfjsCore.Diag),
    backendName: "webgl",
    kernelFunc: diag
};

},{"@tensorflow/tfjs-core":"21Ftl","../diag_gpu":"aR5cG","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aR5cG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DiagProgram", ()=>DiagProgram);
class DiagProgram {
    constructor(size){
        this.variableNames = [
            "X"
        ];
        this.outputShape = [
            size,
            size
        ];
        this.userCode = `
      void main() {
          ivec2 coords = getOutputCoords();
          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
          setOutput(val);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lLrqs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "dilation2D", ()=>dilation2D);
parcelHelpers.export(exports, "dilation2DConfig", ()=>dilation2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _dilationGpu = require("../dilation_gpu");
var _reshape = require("./Reshape");
function dilation2D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dilations } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computeDilation2DInfo(x.shape, filter.shape, strides, pad, "NHWC" /* dataFormat */ , dilations);
    let out;
    const program = new (0, _dilationGpu.Dilation2DProgram)(convInfo);
    out = backend.runWebGLProgram(program, [
        x,
        filter
    ], "float32");
    const outReshaped = (0, _reshape.reshape)({
        inputs: {
            x: out
        },
        backend,
        attrs: {
            shape: convInfo.outShape
        }
    });
    backend.disposeIntermediateTensorInfo(out);
    return outReshaped;
}
const dilation2DConfig = {
    kernelName: (0, _tfjsCore.Dilation2D),
    backendName: "webgl",
    kernelFunc: dilation2D
};

},{"@tensorflow/tfjs-core":"21Ftl","../dilation_gpu":"3pogG","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3pogG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "Dilation2DProgram", ()=>Dilation2DProgram);
class Dilation2DProgram {
    constructor(convInfo){
        this.variableNames = [
            "x",
            "W"
        ];
        this.outputShape = convInfo.outShape;
        const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;
        const { top: padTop, left: padLeft } = padInfo;
        this.userCode = `
      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
      const ivec2 pads = ivec2(${padTop}, ${padLeft});
      const float neg_infinity = -3.4e38;

      void main() {
        ivec4 coords = getOutputCoords();
        int batch = coords.x;
        int d1 = coords.w;
        ivec2 outTopLeftCorner =
            coords.yz * strides - pads;
        int hBeg = outTopLeftCorner.x;
        int wBeg = outTopLeftCorner.y;

        float curVal = neg_infinity;
        for (int h = 0; h < ${filterHeight}; h++) {
          int hIn = hBeg + h * ${dilationHeight};

          if (hIn >= 0 && hIn < ${inHeight}) {
            for (int w = 0; w < ${filterWidth}; w++) {
              int wIn = wBeg + w * ${dilationWidth};

              if (wIn >= 0 && wIn < ${inWidth}) {
                float xVal = getX(batch, hIn, wIn, d1);
                float wVal = getW(h, w, d1);

                float val = xVal + wVal;
                if (val > curVal) {
                  curVal = val;
                }
              }
            }
          }
        }

        float result = curVal;
        setOutput(result);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hmWYw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "einsum", ()=>einsum);
parcelHelpers.export(exports, "einsumConfig", ()=>einsumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _multiply = require("./Multiply");
var _reshape = require("./Reshape");
var _sum = require("./Sum");
var _transpose = require("./Transpose");
function einsum(args) {
    const { inputs, backend, attrs } = args;
    const { equation } = attrs;
    const tensors = inputs;
    const { allDims, summedDims, idDims } = (0, _tfjsCore.backend_util).decodeEinsumEquation(equation, tensors.length);
    (0, _tfjsCore.backend_util).checkEinsumDimSizes(allDims.length, idDims, tensors);
    const { path, steps } = (0, _tfjsCore.backend_util).getEinsumComputePath(summedDims, idDims);
    const nSteps = steps.length;
    let out = null;
    let numDimsRemaining = allDims.length;
    const tensorsToDispose = [];
    for(let i = 0; i < nSteps; ++i){
        for (const idTerm of steps[i]){
            const { permutationIndices: perm, expandDims: dimsToExpand } = (0, _tfjsCore.backend_util).getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
            let x;
            if ((0, _tfjsCore.backend_util).isIdentityPermutation(perm)) x = tensors[idTerm];
            else {
                x = (0, _transpose.transpose)({
                    inputs: {
                        x: tensors[idTerm]
                    },
                    backend,
                    attrs: {
                        perm
                    }
                });
                tensorsToDispose.push(x);
            }
            const targetShape = x.shape.slice();
            for(let k = 0; k < dimsToExpand.length; ++k)targetShape.splice(dimsToExpand[k], 0, 1);
            if (!(0, _tfjsCore.util).arraysEqual(x.shape, targetShape)) {
                x = (0, _reshape.reshape)({
                    inputs: {
                        x
                    },
                    backend,
                    attrs: {
                        shape: targetShape
                    }
                });
                tensorsToDispose.push(x);
            }
            if (out === null) out = x;
            else {
                // tslint:disable-next-line: no-unnecessary-type-assertion
                out = (0, _multiply.multiply)({
                    inputs: {
                        a: x,
                        b: out
                    },
                    backend
                });
                tensorsToDispose.push(out);
            }
        }
        if (i < nSteps - 1) {
            if (path[i] >= 0) {
                out = (0, _sum.sum)({
                    inputs: {
                        x: out
                    },
                    backend,
                    attrs: {
                        axis: path[i] - (allDims.length - numDimsRemaining),
                        keepDims: false
                    }
                });
                tensorsToDispose.push(out);
            }
            numDimsRemaining--;
        }
    }
    // Clean up intermediate tensors.
    for (const tensorInfo of tensorsToDispose){
        if (tensorInfo === out) continue;
        backend.disposeIntermediateTensorInfo(tensorInfo);
    }
    return out;
}
const einsumConfig = {
    kernelName: (0, _tfjsCore.Einsum),
    backendName: "webgl",
    kernelFunc: einsum
};

},{"@tensorflow/tfjs-core":"21Ftl","./Multiply":"5ePR8","./Reshape":"fgZen","./Sum":"c4eiD","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fJBTw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "eluConfig", ()=>eluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
const ELU_PACKED = `
  vec4 result;

  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);

  return result;
`;
const elu = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ELU,
    packedOpSnippet: ELU_PACKED
});
const eluConfig = {
    kernelName: (0, _tfjsCore.Elu),
    backendName: "webgl",
    kernelFunc: elu
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hjBqq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "eluGrad", ()=>eluGrad);
parcelHelpers.export(exports, "eluGradConfig", ()=>eluGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
const ELU_DER = `return (b >= 0.0) ? a : a * (b + 1.0);`;
const ELU_DER_PACKED = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
const eluGrad = (args)=>{
    const { inputs, backend } = args;
    const { dy, y } = inputs;
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new (0, _binaryopPackedGpu.BinaryOpPackedProgram)(ELU_DER_PACKED, dy.shape, y.shape) : new (0, _binaryopGpu.BinaryOpProgram)(ELU_DER, dy.shape, y.shape);
    return backend.runWebGLProgram(program, [
        dy,
        y
    ], dy.dtype);
};
const eluGradConfig = {
    kernelName: (0, _tfjsCore.EluGrad),
    backendName: "webgl",
    kernelFunc: eluGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dNiJE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "equal", ()=>equal);
parcelHelpers.export(exports, "equalConfig", ()=>equalConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const PACKED_EQUAL = `
  return vec4(equal(a, b));
`;
const EQUAL = `return float(a == b);`;
const equal = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: EQUAL,
    packedOpSnippet: PACKED_EQUAL,
    dtype: "bool",
    cpuKernelImpl: (0, _shared.equalImplCPU)
});
const equalConfig = {
    kernelName: (0, _tfjsCore.Equal),
    backendName: "webgl",
    kernelFunc: equal
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ahvKl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "erf", ()=>erf);
parcelHelpers.export(exports, "erfConfig", ()=>erfConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const ERF = `
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${(0, _tfjsCore.backend_util).ERF_P};
  float a1 = ${(0, _tfjsCore.backend_util).ERF_A1};
  float a2 = ${(0, _tfjsCore.backend_util).ERF_A2};
  float a3 = ${(0, _tfjsCore.backend_util).ERF_A3};
  float a4 = ${(0, _tfjsCore.backend_util).ERF_A4};
  float a5 = ${(0, _tfjsCore.backend_util).ERF_A5};

  float sign = sign(x);
  x = abs(x);
  float t = 1.0 / (1.0 + p * x);
  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`;
const erf = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ERF
});
const erfConfig = {
    kernelName: (0, _tfjsCore.Erf),
    backendName: "webgl",
    kernelFunc: erf
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"i4xew":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "EXP", ()=>EXP);
parcelHelpers.export(exports, "exp", ()=>exp);
parcelHelpers.export(exports, "expConfig", ()=>expConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const EXP = (0, _kernelFuncsUtils.CHECK_NAN_SNIPPET_UNARY) + `
  return exp(x);
`;
const EXP_PACKED = `
  vec4 result = exp(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
const exp = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: EXP,
    packedOpSnippet: EXP_PACKED,
    cpuKernelImpl: (0, _shared.expImplCPU),
    dtype: "float32"
});
const expConfig = {
    kernelName: (0, _tfjsCore.Exp),
    backendName: "webgl",
    kernelFunc: exp
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5OvP3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "expandDims", ()=>expandDims);
parcelHelpers.export(exports, "expandDimsConfig", ()=>expandDimsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reshape = require("./Reshape");
function expandDims(args) {
    const { inputs, attrs, backend } = args;
    const { dim } = attrs;
    const { input } = inputs;
    const inputRank = input.shape.length;
    const newShape = input.shape.slice();
    let $dim = dim;
    if (dim < 0) {
        // Negative value is counted from the tail of rank.
        (0, _tfjsCore.util).assert(-(inputRank + 1) <= dim, ()=>`Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
        $dim = inputRank + dim + 1;
    }
    newShape.splice($dim, 0, 1);
    return (0, _reshape.reshape)({
        inputs: {
            x: input
        },
        backend,
        attrs: {
            shape: newShape
        }
    });
}
const expandDimsConfig = {
    kernelName: (0, _tfjsCore.ExpandDims),
    backendName: "webgl",
    kernelFunc: expandDims
};

},{"@tensorflow/tfjs-core":"21Ftl","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"baClA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "expm1", ()=>expm1);
parcelHelpers.export(exports, "expm1Config", ()=>expm1Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const EXPM1 = `return exp(x) - 1.0;`;
const expm1 = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: EXPM1,
    packedOpSnippet: EXPM1,
    cpuKernelImpl: (0, _shared.expm1ImplCPU)
});
const expm1Config = {
    kernelName: (0, _tfjsCore.Expm1),
    backendName: "webgl",
    kernelFunc: expm1
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"zq0LC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fft", ()=>fft);
parcelHelpers.export(exports, "fftConfig", ()=>fftConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fftImpl = require("./FFT_impl");
function fft(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    return (0, _fftImpl.fftImpl)(input, false, backend);
}
const fftConfig = {
    kernelName: (0, _tfjsCore.FFT),
    backendName: "webgl",
    kernelFunc: fft
};

},{"@tensorflow/tfjs-core":"21Ftl","./FFT_impl":"ji88K","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ji88K":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fftImpl", ()=>fftImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fftGpu = require("../fft_gpu");
var _complex = require("./Complex");
var _reshape = require("./Reshape");
function fftImpl(x, inverse, backend) {
    const xData = backend.texData.get(x.dataId);
    const inputSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    // Collapse all outer dimensions to a single batch dimension.
    const innerDimensionSize = x.shape[x.shape.length - 1];
    const batch = inputSize / innerDimensionSize;
    const input2D = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: [
                batch,
                innerDimensionSize
            ]
        }
    });
    const xShape = input2D.shape;
    const realProgram = new (0, _fftGpu.FFTProgram)("real", xShape, inverse);
    const imagProgram = new (0, _fftGpu.FFTProgram)("imag", xShape, inverse);
    const inputs = [
        {
            dataId: xData.complexTensorInfos.real.dataId,
            dtype: xData.complexTensorInfos.real.dtype,
            shape: xShape
        },
        {
            dataId: xData.complexTensorInfos.imag.dataId,
            dtype: xData.complexTensorInfos.imag.dtype,
            shape: xShape
        }
    ];
    const realPart = backend.runWebGLProgram(realProgram, inputs, "float32");
    const imagPart = backend.runWebGLProgram(imagProgram, inputs, "float32");
    const complexOutput = (0, _complex.complex)({
        inputs: {
            real: realPart,
            imag: imagPart
        },
        backend
    });
    backend.disposeIntermediateTensorInfo(realPart);
    backend.disposeIntermediateTensorInfo(imagPart);
    const complexOutputReshaped = (0, _reshape.reshape)({
        inputs: {
            x: complexOutput
        },
        backend,
        attrs: {
            shape: x.shape
        }
    });
    backend.disposeIntermediateTensorInfo(input2D);
    backend.disposeIntermediateTensorInfo(complexOutput);
    return complexOutputReshaped;
}

},{"@tensorflow/tfjs-core":"21Ftl","../fft_gpu":"9R9lf","./Complex":"55NfL","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9R9lf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "FFTProgram", ()=>FFTProgram);
class FFTProgram {
    constructor(component, inputShape, inverse){
        this.variableNames = [
            "real",
            "imag"
        ];
        const innerDim = inputShape[1];
        this.outputShape = inputShape;
        const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;
        const resultDenominator = inverse ? `${innerDim}.0` : "1.0";
        let opString;
        if (component === "real") opString = "return real * expR - imag * expI;";
        else if (component === "imag") opString = "return real * expI + imag * expR;";
        else throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);
        this.userCode = `
      const float exponentMultiplier = ${exponentMultiplierSnippet};

      float unaryOpComplex(float real, float expR, float imag, float expI) {
        ${opString}
      }

      float mulMatDFT(int batch, int index) {
        float indexRatio = float(index) / float(${innerDim});
        float exponentMultiplierTimesIndexRatio =
            exponentMultiplier * indexRatio;

        float result = 0.0;

        for (int i = 0; i < ${innerDim}; i++) {
          // x = (-2|2 * PI / N) * index * i;
          float x = exponentMultiplierTimesIndexRatio * float(i);
          float expR = cos(x);
          float expI = sin(x);
          float real = getReal(batch, i);
          float imag = getImag(batch, i);

          result +=
              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
        }

        return result;
      }

      void main() {
        ivec2 coords = getOutputCoords();
        setOutput(mulMatDFT(coords[0], coords[1]));
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6Rawr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fill", ()=>fill);
parcelHelpers.export(exports, "fillConfig", ()=>fillConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fillGpu = require("../fill_gpu");
function fill(args) {
    const { backend, attrs } = args;
    const { shape, value } = attrs;
    let { dtype } = attrs;
    dtype = dtype || (0, _tfjsCore.util).inferDtype(value);
    if (dtype === "string") {
        // String type should be handled in CPU memory.
        const values = (0, _tfjsCore.util).getArrayFromDType(dtype, (0, _tfjsCore.util).sizeFromShape(shape));
        values.fill(value);
        return backend.makeTensorInfo(shape, dtype, values);
    } else {
        const program = new (0, _fillGpu.FillProgram)(shape, value);
        const customValues = [
            [
                value
            ]
        ];
        return backend.runWebGLProgram(program, [], dtype, customValues);
    }
}
const fillConfig = {
    kernelName: (0, _tfjsCore.Fill),
    backendName: "webgl",
    kernelFunc: fill
};

},{"@tensorflow/tfjs-core":"21Ftl","../fill_gpu":"hKTOw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hKTOw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "FillProgram", ()=>FillProgram);
class FillProgram {
    constructor(shape, value){
        this.outputShape = [];
        this.customUniforms = [
            {
                name: "value",
                type: "float"
            }
        ];
        this.variableNames = [
            "x"
        ];
        this.outputShape = shape;
        this.userCode = `
      void main() {
        // Input can be obtained from uniform value.
        setOutput(value);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8QDUY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "flipLeftRightConfig", ()=>flipLeftRightConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _flipLeftRightGpu = require("../flip_left_right_gpu");
const flipLeftRightConfig = {
    kernelName: (0, _tfjsCore.FlipLeftRight),
    backendName: "webgl",
    kernelFunc: ({ inputs, backend })=>{
        const { image } = inputs;
        const webglBackend = backend;
        const program = new (0, _flipLeftRightGpu.FlipLeftRightProgram)(image.shape);
        const output = webglBackend.runWebGLProgram(program, [
            image
        ], image.dtype);
        return output;
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","../flip_left_right_gpu":"1eMG5","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1eMG5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "FlipLeftRightProgram", ()=>FlipLeftRightProgram);
class FlipLeftRightProgram {
    constructor(imageShape){
        this.variableNames = [
            "Image"
        ];
        this.outputShape = [];
        const imageWidth = imageShape[2];
        this.outputShape = imageShape;
        this.userCode = `
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];

          int coordX = ${imageWidth} - x - 1;
          float outputValue;
          if(coordX >= 0 && coordX < ${imageWidth}) {
            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
          } else {
            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
          }
          setOutput(outputValue);
        }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hB9uc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "floor", ()=>floor);
parcelHelpers.export(exports, "floorConfig", ()=>floorConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const FLOOR = `return floor(x);`;
const floor = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: FLOOR,
    packedOpSnippet: FLOOR,
    cpuKernelImpl: (0, _shared.floorImplCPU)
});
const floorConfig = {
    kernelName: (0, _tfjsCore.Floor),
    backendName: "webgl",
    kernelFunc: floor
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g0Hz7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "floorDiv", ()=>floorDiv);
parcelHelpers.export(exports, "floorDivConfig", ()=>floorDivConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
// We use native integer division to deal with floating point imprecision. Since
// we implement floor division and glsl implements truncated division, we
// correct for this by subtracting 1 from result when the result is negative and
// there is a remainder.
const INT_DIV = `
  float s = sign(a) * sign(b);
  int ia = round(a);
  int ib = round(b);
  if (ib != 0) {
    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
    return float(idiv(ia, ib, s));
  } else {
    return NAN;
  }
`;
const INT_DIV_PACKED = `
  ivec4 ia = round(a);
  ivec4 ib = round(b);
  bvec4 cond = notEqual(ib, ivec4(0));
  ivec4 result = ivec4(0);
  vec4 s = sign(a) * sign(b);

  // Windows (D3D) wants guaranteed non-zero int division at compile-time.
  if (cond[0]) {
    result[0] = idiv(ia[0], ib[0], s[0]);
  }
  if (cond[1]) {
    result[1] = idiv(ia[1], ib[1], s[1]);
  }
  if (cond[2]) {
    result[2] = idiv(ia[2], ib[2], s[2]);
  }
  if (cond[3]) {
    result[3] = idiv(ia[3], ib[3], s[3]);
  }
  return vec4(result);
`;
const floorDiv = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: INT_DIV,
    packedOpSnippet: INT_DIV_PACKED,
    dtype: "int32"
});
const floorDivConfig = {
    kernelName: (0, _tfjsCore.FloorDiv),
    backendName: "webgl",
    kernelFunc: floorDiv
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bNE2l":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fromPixelsConfig", ()=>fromPixelsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _texUtil = require("../tex_util");
var _fromPixelsGpu = require("./FromPixels_utils/from_pixels_gpu");
var _fromPixelsPackedGpu = require("./FromPixels_utils/from_pixels_packed_gpu");
const fromPixelsConfig = {
    kernelName: (0, _tfjsCore.FromPixels),
    backendName: "webgl",
    kernelFunc: fromPixels
};
let fromPixels2DContext;
let willReadFrequently = (0, _tfjsCore.env)().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
function fromPixels(args) {
    const { inputs, backend, attrs } = args;
    let { pixels } = inputs;
    const { numChannels } = attrs;
    const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement;
    const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement;
    const [width, height] = isVideo ? [
        pixels.videoWidth,
        pixels.videoHeight
    ] : [
        pixels.width,
        pixels.height
    ];
    const texShape = [
        height,
        width
    ];
    const outShape = [
        height,
        width,
        numChannels
    ];
    if (isImage || isVideo) {
        const newWillReadFrequently = (0, _tfjsCore.env)().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");
        if (fromPixels2DContext == null || newWillReadFrequently !== willReadFrequently) {
            willReadFrequently = newWillReadFrequently;
            fromPixels2DContext = document.createElement("canvas").getContext("2d", {
                willReadFrequently
            });
        }
        fromPixels2DContext.canvas.width = width;
        fromPixels2DContext.canvas.height = height;
        fromPixels2DContext.drawImage(pixels, 0, 0, width, height);
        pixels = fromPixels2DContext.canvas;
    }
    const tempPixelHandle = backend.makeTensorInfo(texShape, "int32");
    // This is a byte texture with pixels.
    backend.texData.get(tempPixelHandle.dataId).usage = (0, _texUtil.TextureUsage).PIXELS;
    backend.gpgpu.uploadPixelDataToTexture(backend.getTexture(tempPixelHandle.dataId), pixels);
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK") ? new (0, _fromPixelsPackedGpu.FromPixelsPackedProgram)(outShape) : new (0, _fromPixelsGpu.FromPixelsProgram)(outShape);
    const res = backend.runWebGLProgram(program, [
        tempPixelHandle
    ], "int32");
    backend.disposeData(tempPixelHandle.dataId);
    return res;
}

},{"@tensorflow/tfjs-core":"21Ftl","../tex_util":"1oYRe","./FromPixels_utils/from_pixels_gpu":"779bc","./FromPixels_utils/from_pixels_packed_gpu":"a4kH1","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"779bc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "FromPixelsProgram", ()=>FromPixelsProgram);
var _glslVersion = require("../../glsl_version");
class FromPixelsProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        const glsl = (0, _glslVersion.getGlslDifferences)();
        const [height, width] = outputShape;
        this.outputShape = outputShape;
        this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];
        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);

        vec4 values = ${glsl.texture2D}(A, uv);
        float value;
        if (depth == 0) {
          value = values.r;
        } else if (depth == 1) {
          value = values.g;
        } else if (depth == 2) {
          value = values.b;
        } else if (depth == 3) {
          value = values.a;
        }

        setOutput(floor(value * 255.0 + 0.5));
      }
    `;
    }
}

},{"../../glsl_version":"kG2vE","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a4kH1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "FromPixelsPackedProgram", ()=>FromPixelsPackedProgram);
var _glslVersion = require("../../glsl_version");
class FromPixelsPackedProgram {
    constructor(outputShape){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = false;
        this.packedOutput = true;
        const glsl = (0, _glslVersion.getGlslDifferences)();
        const [height, width] = outputShape;
        this.outputShape = outputShape;
        this.userCode = `
      void main() {
        ivec3 coords = getOutputCoords();
        int texR = coords[0];
        int texC = coords[1];
        int depth = coords[2];

        vec4 result = vec4(0.);

        for(int row=0; row<=1; row++) {
          for(int col=0; col<=1; col++) {
            texC = coords[1] + row;
            depth = coords[2] + col;

            vec2 uv = (vec2(texC, texR) + halfCR) /
                       vec2(${width}.0, ${height}.0);
            vec4 values = ${glsl.texture2D}(A, uv);
            float value;
            if (depth == 0) {
              value = values.r;
            } else if (depth == 1) {
              value = values.g;
            } else if (depth == 2) {
              value = values.b;
            } else if (depth == 3) {
              value = values.a;
            }

            result[row * 2 + col] = floor(value * 255.0 + 0.5);
          }
        }

        ${glsl.output} = result;
      }
    `;
    }
}

},{"../../glsl_version":"kG2vE","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5vkH0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fusedConv2d", ()=>fusedConv2d);
parcelHelpers.export(exports, "fusedConv2DConfig", ()=>fusedConv2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convGpu = require("../conv_gpu");
var _convPackedGpu = require("../conv_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _conv2DImpl = require("./Conv2D_impl");
var _reshape = require("./Reshape");
function fusedConv2d(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter, bias, preluActivationWeights } = inputs;
    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
    const $dataFormat = (0, _tfjsCore.backend_util).convertConv2DDataFormat(dataFormat);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
    let out;
    const intermediates = [];
    const hasBias = bias != null;
    const hasPreluActivationWeights = preluActivationWeights != null;
    const hasLeakyreluAlpha = activation === "leakyrelu";
    const prepareInputs = ()=>{
        const inputs = [
            x,
            filter
        ];
        // If the input is a 1-D tensor, align it with the channels.
        //
        // For fusedConv2d, the inputs (x, W, bias, preluActivationWeights) are
        // supposed to be aligned with the dataFormat. The 4-D tensor inputs or
        // scalar inputs are originally aligned, but the 1-D tensor inputs are
        // supposed to be aligned with the channels (only bias and PReLU activation
        // weights could be a 1-D tensor).
        const alignInputWithDataFormat = (input, dataFormat)=>{
            if (dataFormat === "NCHW" && input.shape.length === 1 && input.shape[0] !== 1) {
                const alignedInput = (0, _reshape.reshape)({
                    inputs: {
                        x: input
                    },
                    backend,
                    attrs: {
                        shape: [
                            input.shape[0],
                            1,
                            1
                        ]
                    }
                });
                intermediates.push(alignedInput);
                return alignedInput;
            }
            return input;
        };
        if (hasBias) inputs.push(alignInputWithDataFormat(bias, dataFormat));
        if (hasPreluActivationWeights) inputs.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));
        if (hasLeakyreluAlpha) {
            const $leakyreluAlpha = backend.makeTensorInfo([], "float32", (0, _tfjsCore.util).createScalarValue(leakyreluAlpha, "float32"));
            inputs.push($leakyreluAlpha);
            intermediates.push($leakyreluAlpha);
        }
        return inputs;
    };
    if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) out = (0, _conv2DImpl.conv2dByMatMul)({
        x,
        filter,
        convInfo,
        backend,
        bias,
        activation,
        preluActivationWeights,
        leakyreluAlpha
    });
    else if (convInfo.strideWidth <= 2 && $dataFormat === "channelsLast" && (0, _tfjsCore.env)().getBool("WEBGL_EXP_CONV")) {
        const fusedActivation = activation ? (0, _kernelFuncsUtils.mapActivationToShaderProgram)(activation, true) : null;
        const program = new (0, _convPackedGpu.Conv2DPackedProgram)(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
        const customValues = [
            [
                convInfo.padInfo.top,
                convInfo.padInfo.left
            ],
            [
                convInfo.strideHeight,
                convInfo.strideWidth
            ],
            [
                convInfo.dilationHeight,
                convInfo.dilationWidth
            ],
            [
                convInfo.inHeight,
                convInfo.inWidth
            ]
        ];
        const inputs = prepareInputs();
        out = backend.runWebGLProgram(program, inputs, "float32", customValues);
    } else if ((0, _tfjsCore.env)().getBool("WEBGL_CONV_IM2COL")) out = (0, _conv2DImpl.conv2dWithIm2Row)({
        x,
        filter,
        convInfo,
        backend,
        bias,
        activation,
        preluActivationWeights,
        leakyreluAlpha
    });
    else {
        const fusedActivation = activation ? (0, _kernelFuncsUtils.mapActivationToShaderProgram)(activation, false) : null;
        const program = new (0, _convGpu.Conv2DProgram)(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
        const inputs = prepareInputs();
        out = backend.runWebGLProgram(program, inputs, "float32");
    }
    const outReshaped = (0, _reshape.reshape)({
        inputs: {
            x: out
        },
        backend,
        attrs: {
            shape: convInfo.outShape
        }
    });
    intermediates.push(out);
    intermediates.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return outReshaped;
}
const fusedConv2DConfig = {
    kernelName: (0, _tfjsCore.FusedConv2D),
    backendName: "webgl",
    kernelFunc: fusedConv2d
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_gpu":"3pfYb","../conv_packed_gpu":"hPtK0","../kernel_utils/kernel_funcs_utils":"grPtN","./Conv2D_impl":"6Kgrg","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5SfAK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fusedDepthwiseConv2D", ()=>fusedDepthwiseConv2D);
parcelHelpers.export(exports, "fusedDepthwiseConv2DConfig", ()=>fusedDepthwiseConv2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _convGpuDepthwise = require("../conv_gpu_depthwise");
var _convPackedGpuDepthwise = require("../conv_packed_gpu_depthwise");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
function fusedDepthwiseConv2D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter, bias, preluActivationWeights } = inputs;
    const { strides, pad, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
    const intermediates = [];
    let $dilations = dilations;
    if ($dilations == null) $dilations = [
        1,
        1
    ];
    (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, $dilations), ()=>"Error in depthwiseConv2d: Either strides or dilations must be " + `1. Got strides ${strides} and dilations '${$dilations}'`);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true);
    const shouldPackDepthwiseConv = (0, _tfjsCore.env)().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;
    const fusedActivation = activation ? (0, _kernelFuncsUtils.mapActivationToShaderProgram)(activation, shouldPackDepthwiseConv) : null;
    const programInputs = [
        x,
        filter
    ];
    const hasBias = bias != null;
    const hasPreluActivationWeights = preluActivationWeights != null;
    const hasLeakyreluAlpha = activation === "leakyrelu";
    if (hasBias) programInputs.push(bias);
    if (hasPreluActivationWeights) programInputs.push(preluActivationWeights);
    if (hasLeakyreluAlpha) {
        const $leakyreluAlpha = backend.makeTensorInfo([], "float32", (0, _tfjsCore.util).createScalarValue(leakyreluAlpha, "float32"));
        programInputs.push($leakyreluAlpha);
        intermediates.push($leakyreluAlpha);
    }
    let program;
    if (shouldPackDepthwiseConv) program = new (0, _convPackedGpuDepthwise.DepthwiseConvPacked2DProgram)(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
    else program = new (0, _convGpuDepthwise.DepthwiseConv2DProgram)(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
    const customValues = [
        [
            convInfo.padInfo.top,
            convInfo.padInfo.left
        ],
        [
            convInfo.strideHeight,
            convInfo.strideWidth
        ],
        [
            convInfo.dilationHeight,
            convInfo.dilationWidth
        ],
        [
            convInfo.inHeight,
            convInfo.inWidth
        ]
    ];
    const result = backend.runWebGLProgram(program, programInputs, "float32", customValues);
    intermediates.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return result;
}
const fusedDepthwiseConv2DConfig = {
    kernelName: (0, _tfjsCore.FusedDepthwiseConv2D),
    backendName: "webgl",
    kernelFunc: fusedDepthwiseConv2D
};

},{"@tensorflow/tfjs-core":"21Ftl","../conv_gpu_depthwise":"31MV7","../conv_packed_gpu_depthwise":"aG31s","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4xUDy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gatherNd", ()=>gatherNd);
parcelHelpers.export(exports, "gatherNdConfig", ()=>gatherNdConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gatherNdGpu = require("../gather_nd_gpu");
var _shared = require("../kernel_utils/shared");
var _reshape = require("./Reshape");
function gatherNd(args) {
    const { inputs, backend } = args;
    const { params, indices } = inputs;
    const indicesShape = indices.shape;
    const sliceRank = indicesShape[indicesShape.length - 1];
    const paramsSize = (0, _tfjsCore.util).sizeFromShape(params.shape);
    const [resultShape, numSlices, sliceSize, strides] = (0, _tfjsCore.backend_util).prepareAndValidate(params, indices);
    const flattenIndices = (0, _reshape.reshape)({
        inputs: {
            x: indices
        },
        backend,
        attrs: {
            shape: [
                numSlices,
                sliceRank
            ]
        }
    });
    const flattenX = (0, _reshape.reshape)({
        inputs: {
            x: params
        },
        backend,
        attrs: {
            shape: [
                (0, _tfjsCore.util).sizeFromShape(params.shape) / sliceSize,
                sliceSize
            ]
        }
    });
    if (backend.shouldExecuteOnCPU([
        params,
        indices
    ]) || params.dtype === "string") {
        const indicesData = backend.readSync(indices.dataId);
        const paramsBuf = backend.bufferSync(params);
        const outValue = (0, _shared.gatherNdImplCPU)(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
        return backend.makeTensorInfo(resultShape, params.dtype, outValue.values);
    }
    const program = new (0, _gatherNdGpu.GatherNDProgram)(sliceRank, strides, [
        numSlices,
        sliceSize
    ], params.shape);
    const res = backend.runWebGLProgram(program, [
        flattenX,
        flattenIndices
    ], flattenX.dtype);
    const reshaped = (0, _reshape.reshape)({
        inputs: {
            x: res
        },
        backend,
        attrs: {
            shape: resultShape
        }
    });
    backend.disposeIntermediateTensorInfo(flattenIndices);
    backend.disposeIntermediateTensorInfo(flattenX);
    backend.disposeIntermediateTensorInfo(res);
    return reshaped;
}
const gatherNdConfig = {
    kernelName: (0, _tfjsCore.GatherNd),
    backendName: "webgl",
    kernelFunc: gatherNd
};

},{"@tensorflow/tfjs-core":"21Ftl","../gather_nd_gpu":"8orFL","../kernel_utils/shared":"e4M7v","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8orFL":[function(require,module,exports) {
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "GatherNDProgram", ()=>GatherNDProgram);
var _shaderCompiler = require("./shader_compiler");
class GatherNDProgram {
    constructor(sliceDim, strides, shape, paramsShape){
        this.sliceDim = sliceDim;
        this.strides = strides;
        this.paramsShape = paramsShape;
        this.variableNames = [
            "x",
            "indices"
        ];
        this.outputShape = shape;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(shape.length);
        let mainLoop = `
    int index;`;
        for(let j = 0; j < this.sliceDim; j++)mainLoop += `
          index = round(getIndices(coords[0], ${j}));
          out_of_bounds = out_of_bounds || index < 0;
          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[j]};
          flattenIndex += index * ${this.strides[j]};`;
        this.userCode = `
         void main() {
          ${dtype} coords = getOutputCoords();
          int flattenIndex = 0;
          bool out_of_bounds = false;

          ${mainLoop}

          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
        }
      `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cBFXe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gatherV2", ()=>gatherV2);
parcelHelpers.export(exports, "gatherV2Config", ()=>gatherV2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gatherGpu = require("../gather_gpu");
var _shared = require("../kernel_utils/shared");
var _reshape = require("./Reshape");
function gatherV2(args) {
    const { inputs, backend, attrs } = args;
    const { x, indices } = inputs;
    const { axis, batchDims } = attrs;
    const parsedAxis = (0, _tfjsCore.util).parseAxisParam(axis, x.shape)[0];
    if ((0, _tfjsCore.env)().get("DEBUG")) {
        // In debug mode, throw error when any index is out of bound.
        // Otherwise, just fill out of bounds with zeroes.
        const indicesVals = backend.readSync(indices.dataId);
        const axisDim = x.shape[parsedAxis];
        for(let i = 0; i < indicesVals.length; ++i){
            const index = indicesVals[i];
            (0, _tfjsCore.util).assert(index <= axisDim - 1 && index >= 0, ()=>`GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
        }
    }
    const shapeInfo = (0, _tfjsCore.backend_util).segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
    const indicesSize = (0, _tfjsCore.util).sizeFromShape(indices.shape);
    const toDispose = [];
    const flattenX = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: [
                shapeInfo.batchSize,
                shapeInfo.outerSize,
                shapeInfo.dimSize,
                shapeInfo.sliceSize
            ]
        }
    });
    const flattenIndex = (0, _reshape.reshape)({
        inputs: {
            x: indices
        },
        backend,
        attrs: {
            shape: [
                shapeInfo.batchSize,
                indicesSize / shapeInfo.batchSize
            ]
        }
    });
    toDispose.push(flattenX);
    toDispose.push(flattenIndex);
    const flattenOutputShape = [
        shapeInfo.batchSize,
        shapeInfo.outerSize,
        indicesSize / shapeInfo.batchSize,
        shapeInfo.sliceSize
    ];
    if (backend.shouldExecuteOnCPU([
        x,
        indices
    ]) || x.dtype === "string") {
        const indicesBuf = backend.bufferSync(flattenIndex);
        const xBuf = backend.bufferSync(flattenX);
        const outBuf = (0, _shared.gatherV2ImplCPU)(xBuf, indicesBuf, flattenOutputShape);
        toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
        return backend.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
    }
    const program = new (0, _gatherGpu.GatherProgram)(flattenX.shape, flattenOutputShape);
    const res = backend.runWebGLProgram(program, [
        flattenX,
        flattenIndex
    ], flattenX.dtype);
    toDispose.push(res);
    const reshaped = (0, _reshape.reshape)({
        inputs: {
            x: res
        },
        backend,
        attrs: {
            shape: shapeInfo.outputShape
        }
    });
    toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return reshaped;
}
const gatherV2Config = {
    kernelName: (0, _tfjsCore.GatherV2),
    backendName: "webgl",
    kernelFunc: gatherV2
};

},{"@tensorflow/tfjs-core":"21Ftl","../gather_gpu":"dakUw","../kernel_utils/shared":"e4M7v","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dakUw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "GatherProgram", ()=>GatherProgram);
var _shaderCompiler = require("./shader_compiler");
class GatherProgram {
    constructor(aShape, outputShape){
        this.variableNames = [
            "A",
            "indices"
        ];
        this.outputShape = outputShape;
        this.rank = outputShape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(this.rank);
        const sourceCoords = getSourceCoords(aShape, 2);
        this.userCode = `
      void main() {
        ${dtype} resRC = getOutputCoords();
        int index = int(getIndices(resRC.x, resRC.z));
        float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;
        setOutput(inBounds * getA(${sourceCoords}));
      }
    `;
    }
}
// The input and output are always flattened into rank 4 tensors.
function getSourceCoords(aShape, axis) {
    const currentCoords = [
        "resRC.x",
        "resRC.y",
        "resRC.z",
        "resRC.w"
    ];
    const sourceCoords = [];
    for(let i = 0; i < aShape.length; i++)if (i === 2) sourceCoords.push("index");
    else sourceCoords.push(`${currentCoords[i]}`);
    return sourceCoords.join();
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eDhqn":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "greater", ()=>greater);
parcelHelpers.export(exports, "greaterConfig", ()=>greaterConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const GREATER = `return float(a > b);`;
const GREATER_PACKED = `
  return vec4(greaterThan(a, b));
`;
const greater = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: GREATER,
    packedOpSnippet: GREATER_PACKED,
    cpuKernelImpl: (0, _shared.greaterImplCPU),
    dtype: "bool"
});
const greaterConfig = {
    kernelName: (0, _tfjsCore.Greater),
    backendName: "webgl",
    kernelFunc: greater
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1w6uF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "greaterEqual", ()=>greaterEqual);
parcelHelpers.export(exports, "greaterEqualConfig", ()=>greaterEqualConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const GREATER_EQUAL = `return float(a >= b);`;
const GREATER_EQUAL_PACKED = `
  return vec4(greaterThanEqual(a, b));
`;
const greaterEqual = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: GREATER_EQUAL,
    packedOpSnippet: GREATER_EQUAL_PACKED,
    dtype: "bool",
    cpuKernelImpl: (0, _shared.greaterEqualImplCPU)
});
const greaterEqualConfig = {
    kernelName: (0, _tfjsCore.GreaterEqual),
    backendName: "webgl",
    kernelFunc: greaterEqual
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1mdy7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ifft", ()=>ifft);
parcelHelpers.export(exports, "ifftConfig", ()=>ifftConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fftImpl = require("./FFT_impl");
function ifft(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    return (0, _fftImpl.fftImpl)(input, true, backend);
}
const ifftConfig = {
    kernelName: (0, _tfjsCore.IFFT),
    backendName: "webgl",
    kernelFunc: ifft
};

},{"@tensorflow/tfjs-core":"21Ftl","./FFT_impl":"ji88K","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aWWtq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isFinite", ()=>isFinite);
parcelHelpers.export(exports, "isFiniteConfig", ()=>isFiniteConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const IS_FINITE = `return float(!isnan(x) && !isinf(x));`;
const isFinite = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: IS_FINITE,
    dtype: "bool"
});
const isFiniteConfig = {
    kernelName: (0, _tfjsCore.IsFinite),
    backendName: "webgl",
    kernelFunc: isFinite
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bxXPH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isInf", ()=>isInf);
parcelHelpers.export(exports, "isInfConfig", ()=>isInfConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const IS_INF = `return float(isinf(x));`;
const isInf = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: IS_INF,
    dtype: "bool"
});
const isInfConfig = {
    kernelName: (0, _tfjsCore.IsInf),
    backendName: "webgl",
    kernelFunc: isInf
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jUwW0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isNaN", ()=>isNaN);
parcelHelpers.export(exports, "isNaNConfig", ()=>isNaNConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const IS_NAN = `return float(isnan(x));`;
const isNaN = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: IS_NAN,
    dtype: "bool"
});
const isNaNConfig = {
    kernelName: (0, _tfjsCore.IsNan),
    backendName: "webgl",
    kernelFunc: isNaN
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"htToM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "less", ()=>less);
parcelHelpers.export(exports, "lessConfig", ()=>lessConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const LESS = `return float(a < b);`;
const LESS_PACKED = `
  return vec4(lessThan(a, b));
`;
const less = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: LESS,
    packedOpSnippet: LESS_PACKED,
    cpuKernelImpl: (0, _shared.lessImplCPU),
    dtype: "bool"
});
const lessConfig = {
    kernelName: (0, _tfjsCore.Less),
    backendName: "webgl",
    kernelFunc: less
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eXbD5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "LESS_EQUAL", ()=>LESS_EQUAL);
parcelHelpers.export(exports, "LESS_EQUAL_PACKED", ()=>LESS_EQUAL_PACKED);
parcelHelpers.export(exports, "lessEqual", ()=>lessEqual);
parcelHelpers.export(exports, "lessEqualConfig", ()=>lessEqualConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const LESS_EQUAL = `return float(a <= b);`;
const LESS_EQUAL_PACKED = `
  return vec4(lessThanEqual(a, b));
`;
const lessEqual = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: LESS_EQUAL,
    packedOpSnippet: LESS_EQUAL_PACKED,
    cpuKernelImpl: (0, _shared.lessEqualImplCPU),
    dtype: "bool"
});
const lessEqualConfig = {
    kernelName: (0, _tfjsCore.LessEqual),
    backendName: "webgl",
    kernelFunc: lessEqual
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kM2RJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "linSpace", ()=>linSpace);
parcelHelpers.export(exports, "linSpaceConfig", ()=>linSpaceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function linSpace(args) {
    const { backend, attrs } = args;
    const { start, stop, num } = attrs;
    // TODO: Use CPU implementation due to the precision problem in Safari.
    const outVals = (0, _shared.linSpaceImplCPU)(start, stop, num);
    return backend.makeTensorInfo([
        outVals.length
    ], "float32", outVals);
}
const linSpaceConfig = {
    kernelName: (0, _tfjsCore.LinSpace),
    backendName: "webgl",
    kernelFunc: linSpace
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fVZGS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "log", ()=>log);
parcelHelpers.export(exports, "logConfig", ()=>logConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
// Windows chrome return 0 if the input is negative value. We will specifically
// return NaN if the input is 0 to solve compatiblity issue.
const LOG = (0, _kernelFuncsUtils.CHECK_NAN_SNIPPET_UNARY) + `
  return x < 0.0 ? 0./0. : log(x);
`;
const LOG_PACKED = `
  vec4 result = log(x);
  bvec4 isNaN = isnan(x);
  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
  return result;
`;
const log = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: LOG,
    packedOpSnippet: LOG_PACKED,
    cpuKernelImpl: (0, _shared.logImplCPU)
});
const logConfig = {
    kernelName: (0, _tfjsCore.Log),
    backendName: "webgl",
    kernelFunc: log
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"czYhe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "log1p", ()=>log1p);
parcelHelpers.export(exports, "log1pConfig", ()=>log1pConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const LOG1P = (0, _kernelFuncsUtils.CHECK_NAN_SNIPPET_UNARY) + `
  return log(1.0 + x);
`;
const log1p = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: LOG1P
});
const log1pConfig = {
    kernelName: (0, _tfjsCore.Log1p),
    backendName: "webgl",
    kernelFunc: log1p
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"37RLf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalAnd", ()=>logicalAnd);
parcelHelpers.export(exports, "logicalAndConfig", ()=>logicalAndConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;
const LOGICAL_AND_PACKED = `
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`;
const logicalAnd = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: LOGICAL_AND,
    packedOpSnippet: LOGICAL_AND_PACKED,
    dtype: "bool"
});
const logicalAndConfig = {
    kernelName: (0, _tfjsCore.LogicalAnd),
    backendName: "webgl",
    kernelFunc: logicalAnd
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b9oed":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalNot", ()=>logicalNot);
parcelHelpers.export(exports, "logicalNotConfig", ()=>logicalNotConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const LOGICAL_NOT = `return float(!(x >= 1.0));`;
const logicalNot = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: LOGICAL_NOT
});
const logicalNotConfig = {
    kernelName: (0, _tfjsCore.LogicalNot),
    backendName: "webgl",
    kernelFunc: logicalNot
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4Kv3V":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalOr", ()=>logicalOr);
parcelHelpers.export(exports, "logicalOrConfig", ()=>logicalOrConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;
const LOGICAL_OR_PACKED = `
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`;
const logicalOr = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: LOGICAL_OR,
    packedOpSnippet: LOGICAL_OR_PACKED,
    dtype: "bool"
});
const logicalOrConfig = {
    kernelName: (0, _tfjsCore.LogicalOr),
    backendName: "webgl",
    kernelFunc: logicalOr
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6ZjnF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "lrn", ()=>lrn);
parcelHelpers.export(exports, "LRNConfig", ()=>LRNConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _lrnGpu = require("../lrn_gpu");
var _lrnPackedGpu = require("../lrn_packed_gpu");
const lrn = (args)=>{
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { depthRadius, bias, alpha, beta } = attrs;
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_NORMALIZATION") ? new (0, _lrnPackedGpu.LRNPackedProgram)(x.shape, depthRadius, bias, alpha, beta) : new (0, _lrnGpu.LRNProgram)(x.shape, depthRadius, bias, alpha, beta);
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype);
};
const LRNConfig = {
    kernelName: (0, _tfjsCore.LRN),
    backendName: "webgl",
    kernelFunc: lrn
};

},{"@tensorflow/tfjs-core":"21Ftl","../lrn_gpu":"lrUAN","../lrn_packed_gpu":"7G0Pe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lrUAN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "LRNProgram", ()=>LRNProgram);
class LRNProgram {
    constructor(xShape, radius, bias, alpha, beta){
        this.variableNames = [
            "x"
        ];
        this.outputShape = [];
        const rad = radius;
        const maxD = xShape[3] - 1;
        this.outputShape = xShape;
        // optimize pow(bias + alpha * sum, -beta)
        // src: https://github.com/tensorflow/tensorflow/..
        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
        // tensorflow/core/kernels/mkl_lrn_op.cc#L320
        let powOperator;
        const basis = `float(${bias}) + float(${alpha}) * sum`;
        if (beta === 0.5) powOperator = `inversesqrt(${basis})`;
        else if (beta === 1.0) powOperator = `1.0/(${basis})`;
        else powOperator = `exp(log(${basis}) * float(-${beta}));`;
        this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];
        int d = coords[3];
        float x = getX(b, r, c, d);
        float sum = 0.0;
        for (int j = -${rad}; j <= ${rad}; j++) {
          int idx = d + j;
          if (idx >= 0 && idx <=  ${maxD}) {
            float z = getX(b, r, c, idx);
            sum += z * z;
          }
        }
        float val = x * ${powOperator};
        setOutput(val);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7G0Pe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "LRNPackedProgram", ()=>LRNPackedProgram);
class LRNPackedProgram {
    constructor(xShape, radius, bias, alpha, beta){
        this.variableNames = [
            "x"
        ];
        this.outputShape = [];
        this.packedInputs = true;
        this.packedOutput = true;
        const rad = radius;
        const maxD = xShape[3] - 1;
        this.outputShape = xShape;
        // optimize pow(bias + alpha * sum, -beta)
        // src: https://github.com/tensorflow/tensorflow/..
        // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
        // tensorflow/core/kernels/mkl_lrn_op.cc#L320
        let powOperator;
        const basis = `float(${bias}) + float(${alpha}) * sum`;
        if (beta === 0.5) powOperator = `inversesqrt(${basis})`;
        else if (beta === 1.0) powOperator = `1.0/(${basis})`;
        else powOperator = `exp(log(${basis}) * float(-${beta}));`;
        this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords.x;
        int r = coords.y;
        int c = coords.z;
        int d = coords.w;

        bool hasNextCol = d < ${this.outputShape[3]};
        bool hasNextRow = c < ${this.outputShape[2]};

        vec4 sum = vec4(0.);
        vec4 xFragAtOutputCoords = getX(b, r, c, d);

        vec4 xAtOutputCoords = vec4(
          getChannel(xFragAtOutputCoords, vec2(c, d)),
          hasNextCol ?
            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
          hasNextRow ?
            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
          (hasNextRow && hasNextCol) ?
            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
        );

        int firstChannel = d - ${rad};
        vec2 cache = vec2(0.);
        if(firstChannel >= 0){
          vec4 firstChannelFrag = getX(b, r, c, firstChannel);
          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
            if(hasNextRow){
              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
            }
        }

        ivec2 depth = ivec2(d, d + 1);
        for (int j = - ${rad}; j <= ${rad}; j++) {
          ivec2 idx = depth + j;
          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));

          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;

          if(depthInRange || depthPlusOneInRange){
            vec4 z = vec4(0.);
            vec4 xFragAtCurrentDepth;
            z.xz = cache.xy;
            if(depthPlusOneInRange && hasNextCol){
              xFragAtCurrentDepth = idx.y != d ?
                getX(b, r, c, idx.y) : xFragAtOutputCoords;
              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
              if(hasNextRow){
                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
              }
            }
            cache.xy = z.yw;
            sum += z * z;
          }
        }
        vec4 result = xAtOutputCoords * ${powOperator};
        setOutput(result);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"BMOEB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "lrnGrad", ()=>lrnGrad);
parcelHelpers.export(exports, "LRNGradConfig", ()=>LRNGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _lrnGradGpu = require("../lrn_grad_gpu");
const lrnGrad = (args)=>{
    const { inputs, backend, attrs } = args;
    const { x, y, dy } = inputs;
    const { depthRadius, bias, alpha, beta } = attrs;
    const program = new (0, _lrnGradGpu.LRNGradProgram)(x.shape, depthRadius, bias, alpha, beta);
    return backend.runWebGLProgram(program, [
        x,
        y,
        dy
    ], x.dtype);
};
const LRNGradConfig = {
    kernelName: (0, _tfjsCore.LRNGrad),
    backendName: "webgl",
    kernelFunc: lrnGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../lrn_grad_gpu":"dolDe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dolDe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "LRNGradProgram", ()=>LRNGradProgram);
class LRNGradProgram {
    constructor(inputShape, depthRadius, bias, alpha, beta){
        this.variableNames = [
            "inputImage",
            "outputImage",
            "dy"
        ];
        this.outputShape = [];
        this.outputShape = inputShape;
        this.depth = inputShape[3];
        this.depthRadius = depthRadius;
        this.bias = bias;
        this.alpha = alpha;
        this.beta = beta;
        this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int r = coords[1];
        int c = coords[2];

        float result = 0.0;
        for (int d = 0; d < ${this.depth}; ++d) {
          int depthBegin = int(max(0.0, float(d - ${depthRadius})));
          int depthEnd = int(min(float(${this.depth}),
              float(d + ${depthRadius} + 1)));

          const int MIN_DEPTH_BEGIN = 0;
          const int MAX_DEPTH_END = ${this.depth};

          float norm = 0.0;
          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd) {
              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
            }
            else {
              break;
            }
          }

          norm = float(${alpha}) * norm + float(${bias});

          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
            if (k < depthBegin){
              continue;
            }
            else if (k >= depthBegin && k < depthEnd){
              float dyi = -2.0 * float(${alpha})
                * float(${beta})
                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)
                / norm;
              if (k == d) {
                dyi += pow(norm, -1.0 * ${beta});
              }
              if (k == coords[3]) {
                dyi *= getDy(b, r, c, d);
                result += dyi;
              }
            }
            else {
              break;
            }
          }
      }
      setOutput(result);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"11YMi":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "max", ()=>max);
parcelHelpers.export(exports, "maxConfig", ()=>maxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _maxImpl = require("./Max_impl");
var _transposeImpl = require("./Transpose_impl");
function max(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { reductionIndices, keepDims } = attrs;
    const xRank = x.shape.length;
    const origAxes = (0, _tfjsCore.util).parseAxisParam(reductionIndices, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    const maxInputIsTransposed = permutedAxes != null;
    const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([
        x
    ]);
    let maxInput = x;
    if (maxInputIsTransposed) {
        if (shouldExecuteOnCPU) {
            const xTexData = backend.texData.get(maxInput.dataId);
            const values = xTexData.values;
            const newShape = new Array(xRank);
            for(let i = 0; i < newShape.length; i++)newShape[i] = x.shape[permutedAxes[i]];
            const maxInputValues = (0, _transposeImpl.transposeImplCPU)(values, x.shape, x.dtype, permutedAxes, newShape);
            maxInput = backend.makeTensorInfo(newShape, x.dtype);
            const maxInputData = backend.texData.get(maxInput.dataId);
            maxInputData.values = maxInputValues;
        } else maxInput = (0, _transposeImpl.transposeImpl)(x, permutedAxes, backend);
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, xRank);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("max", axes, xRank);
    const [maxOutShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(maxInput.shape, axes);
    let outShape = maxOutShape;
    if (keepDims) // rather than reshape at the end, set the target shape here.
    outShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(maxOutShape, origAxes);
    let out;
    if (shouldExecuteOnCPU) {
        const xTexData = backend.texData.get(maxInput.dataId);
        const values = xTexData.values;
        const outValues = (0, _shared.maxImplCPU)(values, (0, _tfjsCore.util).sizeFromShape(reduceShape), outShape, x.dtype);
        out = backend.makeTensorInfo(outShape, x.dtype);
        const outData = backend.texData.get(out.dataId);
        outData.values = outValues;
    } else out = (0, _maxImpl.maxImpl)(maxInput, reduceShape, outShape, backend);
    if (maxInputIsTransposed) backend.disposeIntermediateTensorInfo(maxInput);
    return out;
}
const maxConfig = {
    kernelName: (0, _tfjsCore.Max),
    backendName: "webgl",
    kernelFunc: max
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","./Max_impl":"8LQsj","./Transpose_impl":"arYj9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8LQsj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxImpl", ()=>maxImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reduce = require("../kernel_utils/reduce");
var _reshape = require("../kernels/Reshape");
function maxImpl(x, reduceShape, outShape, backend) {
    const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const batchSize = xSize / inSize;
    const reshapedInput = (0, _reshape.reshape)({
        inputs: {
            x
        },
        attrs: {
            shape: [
                batchSize,
                inSize
            ]
        },
        backend
    });
    const reduced = (0, _reduce.reduce)(reshapedInput, x.dtype, "max", backend);
    const reshapedOutput = (0, _reshape.reshape)({
        inputs: {
            x: reduced
        },
        attrs: {
            shape: outShape
        },
        backend
    });
    backend.disposeIntermediateTensorInfo(reshapedInput);
    backend.disposeIntermediateTensorInfo(reduced);
    return reshapedOutput;
}

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reduce":"dr0Xl","../kernels/Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9B0Ki":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maximum", ()=>maximum);
parcelHelpers.export(exports, "maximumConfig", ()=>maximumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const MAXIMUM = (0, _binaryopGpu.CHECK_NAN_SNIPPET) + `
  return max(a, b);
`;
const MAXIMUM_PACKED = `
  vec4 result = vec4(max(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  ` + (0, _binaryopPackedGpu.CHECK_NAN_SNIPPET_PACKED) + `
  return result;
`;
const maximum = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: MAXIMUM,
    packedOpSnippet: MAXIMUM_PACKED,
    cpuKernelImpl: (0, _shared.maximumImplCPU)
});
const maximumConfig = {
    kernelName: (0, _tfjsCore.Maximum),
    backendName: "webgl",
    kernelFunc: maximum
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"74RsU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool", ()=>maxPool);
parcelHelpers.export(exports, "maxPoolConfig", ()=>maxPoolConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _poolGpu = require("../pool_gpu");
var _webglUtil = require("../webgl_util");
var _identity = require("./Identity");
function maxPool(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    (0, _webglUtil.assertNotComplex)(x, "maxPool");
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const dilations = 1;
    (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in maxPool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && (0, _tfjsCore.util).arraysEqual(convInfo.inShape, convInfo.outShape)) return (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    const maxPoolProgram = new (0, _poolGpu.Pool2DProgram)(convInfo, "max", false);
    return backend.runWebGLProgram(maxPoolProgram, [
        x
    ], x.dtype);
}
const maxPoolConfig = {
    kernelName: (0, _tfjsCore.MaxPool),
    backendName: "webgl",
    kernelFunc: maxPool
};

},{"@tensorflow/tfjs-core":"21Ftl","../pool_gpu":"b1t1V","../webgl_util":"fjf7t","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iXVcU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool3d", ()=>maxPool3d);
parcelHelpers.export(exports, "maxPool3DConfig", ()=>maxPool3DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _poolGpu = require("../pool_gpu");
function maxPool3d(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { filterSize, strides, pad, dataFormat, dimRoundingMode } = attrs;
    const dilations = [
        1,
        1,
        1
    ];
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode, dataFormat);
    const maxPoolProgram = new (0, _poolGpu.Pool3DProgram)(convInfo, "max", false);
    return backend.runWebGLProgram(maxPoolProgram, [
        x
    ], x.dtype);
}
const maxPool3DConfig = {
    kernelName: (0, _tfjsCore.MaxPool3D),
    backendName: "webgl",
    kernelFunc: maxPool3d
};

},{"@tensorflow/tfjs-core":"21Ftl","../pool_gpu":"b1t1V","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eejL1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool3DGrad", ()=>maxPool3DGrad);
parcelHelpers.export(exports, "maxPool3DGradConfig", ()=>maxPool3DGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _maxPoolBackpropGpu = require("../max_pool_backprop_gpu");
var _poolGpu = require("../pool_gpu");
function maxPool3DGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input } = inputs;
    const x = input;
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const dilations = [
        1,
        1,
        1
    ];
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
    const maxPool3dPositionsProgram = new (0, _poolGpu.Pool3DProgram)(convInfo, "max", true);
    const maxPool3dPositions = backend.runWebGLProgram(maxPool3dPositionsProgram, [
        x
    ], x.dtype);
    const maxPoolBackpropProgram = new (0, _maxPoolBackpropGpu.MaxPool3DBackpropProgram)(convInfo);
    const result = backend.runWebGLProgram(maxPoolBackpropProgram, [
        dy,
        maxPool3dPositions
    ], x.dtype);
    backend.disposeIntermediateTensorInfo(maxPool3dPositions);
    return result;
}
const maxPool3DGradConfig = {
    kernelName: (0, _tfjsCore.MaxPool3DGrad),
    backendName: "webgl",
    kernelFunc: maxPool3DGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../max_pool_backprop_gpu":"6LIcT","../pool_gpu":"b1t1V","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6LIcT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MaxPool2DBackpropProgram", ()=>MaxPool2DBackpropProgram);
parcelHelpers.export(exports, "MaxPool3DBackpropProgram", ()=>MaxPool3DBackpropProgram);
class MaxPool2DBackpropProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy",
            "maxPos"
        ];
        this.outputShape = convInfo.inShape;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationHeight = convInfo.dilationHeight;
        const effectiveFilterHeight = convInfo.effectiveFilterHeight;
        const effectiveFilterWidth = convInfo.effectiveFilterWidth;
        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
        const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
        this.userCode = `
      const ivec2 pads = ivec2(${padTop}, ${padLeft});

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];

        ivec2 dyRCCorner = coords.yz - pads;
        int dyRCorner = dyRCCorner.x;
        int dyCCorner = dyRCCorner.y;

        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;
        for (int wR = 0; wR < ${effectiveFilterHeight};
          wR += ${dilationHeight}) {
          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
            continue;
          }
          int idyR = int(dyR);

          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                fract(dyC) > 0.0) {
              continue;
            }
            int idyC = int(dyC);

            float dyValue = getDy(b, idyR, idyC, d);
            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));

            // Get the current value, check it against the value from the
            // position matrix.
            int curPosValue = wR * ${effectiveFilterWidth} + wC;
            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

            dotProd += dyValue * mask;
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}
class MaxPool3DBackpropProgram {
    constructor(convInfo){
        this.variableNames = [
            "dy",
            "maxPos"
        ];
        this.outputShape = convInfo.inShape;
        const strideDepth = convInfo.strideDepth;
        const strideHeight = convInfo.strideHeight;
        const strideWidth = convInfo.strideWidth;
        const dilationDepth = convInfo.dilationDepth;
        const dilationHeight = convInfo.dilationHeight;
        const dilationWidth = convInfo.dilationWidth;
        const effectiveFilterDepth = convInfo.effectiveFilterDepth;
        const effectiveFilterHeight = convInfo.effectiveFilterHeight;
        const effectiveFilterWidth = convInfo.effectiveFilterWidth;
        const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
        const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
        const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
        const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
        this.userCode = `
      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});

      void main() {
        ivec5 coords = getOutputCoords();
        int batch = coords.x;
        int ch = coords.u;

        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
        int dyDCorner = dyCorner.x;
        int dyRCorner = dyCorner.y;
        int dyCCorner = dyCorner.z;

        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
        // dx(xD, xR, xC, ch).
        // ? = to be determined. : = across all values in that axis.
        float dotProd = 0.0;

        for (int wD = 0; wD < ${effectiveFilterDepth};
           wD += ${dilationDepth}) {
          float dyD = float(dyDCorner + wD) / ${strideDepth}.0;

          if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
            continue;
          }
          int idyD = int(dyD);

          for (int wR = 0; wR < ${effectiveFilterHeight};
              wR += ${dilationHeight}) {
            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;

            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
                fract(dyR) > 0.0) {
              continue;
            }
            int idyR = int(dyR);

            for (int wC = 0; wC < ${effectiveFilterWidth};
                wC += ${dilationWidth}) {
              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;

              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
                  fract(dyC) > 0.0) {
                continue;
              }
              int idyC = int(dyC);

              float dyValue = getDy(batch, idyD, idyR, idyC, ch);
              int maxPosValue = ${lastIndex} -
                  int(getMaxPos(batch, idyD, idyR, idyC, ch));

              // Get the current value, check it against the value from the
              // position matrix.
              int curPosValue =
                  wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
                  wR * ${effectiveFilterWidth} + wC;
              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);

              dotProd += dyValue * mask;
            }
          }
        }
        setOutput(dotProd);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eaA7G":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPoolGrad", ()=>maxPoolGrad);
parcelHelpers.export(exports, "maxPoolGradConfig", ()=>maxPoolGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _maxPoolBackpropGpu = require("../max_pool_backprop_gpu");
var _poolGpu = require("../pool_gpu");
var _webglUtil = require("../webgl_util");
function maxPoolGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input, output } = inputs;
    const x = input;
    (0, _webglUtil.assertNotComplex)([
        input,
        output
    ], "maxPoolGrad");
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */ , pad, dimRoundingMode);
    const getPositions = true;
    const maxPoolPositionsProgram = new (0, _poolGpu.Pool2DProgram)(convInfo, "max", getPositions);
    const maxPoolPositions = backend.runWebGLProgram(maxPoolPositionsProgram, [
        x
    ], x.dtype);
    const maxPoolBackPropProgram = new (0, _maxPoolBackpropGpu.MaxPool2DBackpropProgram)(convInfo);
    const result = backend.runWebGLProgram(maxPoolBackPropProgram, [
        dy,
        maxPoolPositions
    ], x.dtype);
    backend.disposeIntermediateTensorInfo(maxPoolPositions);
    return result;
}
const maxPoolGradConfig = {
    kernelName: (0, _tfjsCore.MaxPoolGrad),
    backendName: "webgl",
    kernelFunc: maxPoolGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../max_pool_backprop_gpu":"6LIcT","../pool_gpu":"b1t1V","../webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jaMAy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPoolWithArgmaxConfig", ()=>maxPoolWithArgmaxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _maxPoolWithArgmaxImpl = require("./MaxPoolWithArgmax_impl");
const maxPoolWithArgmaxConfig = {
    kernelName: (0, _tfjsCore.MaxPoolWithArgmax),
    backendName: "webgl",
    kernelFunc: ({ inputs, attrs, backend })=>{
        const { x } = inputs;
        const { filterSize, strides, pad, includeBatchInIndex } = attrs;
        const webglBackend = backend;
        (0, _tfjsCore.util).assert(x.shape.length === 4, ()=>`Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);
        const dilations = [
            1,
            1
        ];
        (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in maxPool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
        const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, dilations, pad);
        const [result, indexes] = (0, _maxPoolWithArgmaxImpl.maxPoolWithArgmaxImpl)(x, includeBatchInIndex, convInfo, webglBackend);
        return [
            result,
            indexes
        ];
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","./MaxPoolWithArgmax_impl":"k4DUz","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"k4DUz":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPoolWithArgmaxImpl", ()=>maxPoolWithArgmaxImpl);
var _poolGpu = require("../pool_gpu");
function maxPoolWithArgmaxImpl(x, includeBatchInIndex, convInfo, backend) {
    let program = new (0, _poolGpu.Pool2DProgram)(convInfo, "max", false);
    const poolOutput = backend.runWebGLProgram(program, [
        x
    ], "float32");
    program = new (0, _poolGpu.Pool2DProgram)(convInfo, "max", true, true, includeBatchInIndex);
    const indexOutput = backend.runWebGLProgram(program, [
        x
    ], "float32");
    return [
        poolOutput,
        indexOutput
    ];
}

},{"../pool_gpu":"b1t1V","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4HPXU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "meanConfig", ()=>meanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _meanImpl = require("./Mean_impl");
var _transposeImpl = require("./Transpose_impl");
const meanConfig = {
    kernelName: (0, _tfjsCore.Mean),
    backendName: "webgl",
    kernelFunc: ({ inputs, attrs, backend })=>{
        const { x } = inputs;
        const { keepDims, axis } = attrs;
        const webglBackend = backend;
        const xRank = x.shape.length;
        const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
        let axes = origAxes;
        const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
        const meanInputIsTransposed = permutedAxes != null;
        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([
            x
        ]);
        const intermediates = [];
        let meanInput = x;
        if (meanInputIsTransposed) {
            if (shouldExecuteOnCPU) {
                const xTexData = webglBackend.texData.get(meanInput.dataId);
                const values = xTexData.values;
                const newShape = new Array(xRank);
                for(let i = 0; i < newShape.length; i++)newShape[i] = x.shape[permutedAxes[i]];
                const meanInputValues = (0, _transposeImpl.transposeImplCPU)(values, x.shape, x.dtype, permutedAxes, newShape);
                meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
                const meanInputData = webglBackend.texData.get(meanInput.dataId);
                meanInputData.values = meanInputValues;
            } else meanInput = (0, _transposeImpl.transposeImpl)(x, permutedAxes, webglBackend);
            intermediates.push(meanInput);
            axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, xRank);
        }
        (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("sum", axes, xRank);
        const [meanOutShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(meanInput.shape, axes);
        let outShape = meanOutShape;
        if (keepDims) // rather than reshape at the end, set the target shape here.
        outShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(meanOutShape, origAxes);
        const out = (0, _meanImpl.meanImpl)(meanInput, reduceShape, outShape, webglBackend);
        for (const i of intermediates)webglBackend.disposeIntermediateTensorInfo(i);
        return out;
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","./Mean_impl":"iSHQB","./Transpose_impl":"arYj9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iSHQB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "meanImpl", ()=>meanImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reduce = require("../kernel_utils/reduce");
var _reshape = require("../kernels/Reshape");
function meanImpl(x, reduceShape, outShape, backend) {
    const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const batchSize = xSize / inSize;
    const reshapedInput = (0, _reshape.reshape)({
        inputs: {
            x
        },
        attrs: {
            shape: [
                batchSize,
                inSize
            ]
        },
        backend
    });
    const reduced = (0, _reduce.reduce)(reshapedInput, "float32", "mean", backend);
    const reshapedOutput = (0, _reshape.reshape)({
        inputs: {
            x: reduced
        },
        attrs: {
            shape: outShape
        },
        backend
    });
    backend.disposeIntermediateTensorInfo(reshapedInput);
    backend.disposeIntermediateTensorInfo(reduced);
    return reshapedOutput;
}

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reduce":"dr0Xl","../kernels/Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"pHfvF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "min", ()=>min);
parcelHelpers.export(exports, "minConfig", ()=>minConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reduce = require("../kernel_utils/reduce");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function min(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    const xRank = x.shape.length;
    const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
        permutedX = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, x.shape.length);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("min", axes, xRank);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(permutedX.shape, axes);
    const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const a2D = (0, _reshape.reshape)({
        inputs: {
            x: permutedX
        },
        backend,
        attrs: {
            shape: [
                -1,
                inSize
            ]
        }
    });
    const reduced = (0, _reduce.reduce)(a2D, a2D.dtype, "min", backend);
    let res;
    if (keepDims) {
        const newShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(outShape, origAxes);
        res = (0, _reshape.reshape)({
            inputs: {
                x: reduced
            },
            backend,
            attrs: {
                shape: newShape
            }
        });
    } else res = (0, _reshape.reshape)({
        inputs: {
            x: reduced
        },
        backend,
        attrs: {
            shape: outShape
        }
    });
    backend.disposeIntermediateTensorInfo(a2D);
    backend.disposeIntermediateTensorInfo(reduced);
    if (permutedAxes != null) backend.disposeIntermediateTensorInfo(permutedX);
    return res;
}
const minConfig = {
    kernelName: (0, _tfjsCore.Min),
    backendName: "webgl",
    kernelFunc: min
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reduce":"dr0Xl","./Reshape":"fgZen","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5hTEN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "minimum", ()=>minimum);
parcelHelpers.export(exports, "minimumConfig", ()=>minimumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopGpu = require("../binaryop_gpu");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const MINIMUM = (0, _binaryopGpu.CHECK_NAN_SNIPPET) + `
  return min(a, b);
`;
const MINIMUM_PACKED = `
  vec4 result = vec4(min(a, b));
  bvec4 isNaNA = isnan(a);
  bvec4 isNaNB = isnan(b);
  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);
  ` + (0, _binaryopPackedGpu.CHECK_NAN_SNIPPET_PACKED) + `
  return result;
`;
const minimum = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: MINIMUM,
    packedOpSnippet: MINIMUM_PACKED,
    cpuKernelImpl: (0, _shared.minimumImplCPU)
});
const minimumConfig = {
    kernelName: (0, _tfjsCore.Minimum),
    backendName: "webgl",
    kernelFunc: minimum
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_gpu":"d0A5z","../binaryop_packed_gpu":"hkzp7","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fGu7V":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mirrorPadKernelFunc", ()=>mirrorPadKernelFunc);
parcelHelpers.export(exports, "mirrorPadConfig", ()=>mirrorPadConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _mirrorPadGpu = require("../mirror_pad_gpu");
var _mirrorPadPackedGpu = require("../mirror_pad_packed_gpu");
const mirrorPadKernelFunc = ({ inputs, backend, attrs })=>{
    const { x } = inputs;
    const { paddings, mode } = attrs;
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new (0, _mirrorPadPackedGpu.MirrorPadPackedProgram)(x.shape, paddings, mode) : new (0, _mirrorPadGpu.MirrorPadProgram)(x.shape, paddings, mode);
    const output = backend.runWebGLProgram(program, [
        x
    ], x.dtype);
    return output;
};
const mirrorPadConfig = {
    kernelName: (0, _tfjsCore.MirrorPad),
    backendName: "webgl",
    kernelFunc: mirrorPadKernelFunc
};

},{"@tensorflow/tfjs-core":"21Ftl","../mirror_pad_gpu":"1S45c","../mirror_pad_packed_gpu":"fauBg","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1S45c":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MirrorPadProgram", ()=>MirrorPadProgram);
var _shaderCompiler = require("./shader_compiler");
class MirrorPadProgram {
    constructor(xShape, paddings, mode){
        this.variableNames = [
            "x"
        ];
        this.outputShape = paddings.map((p, i)=>p[0] + xShape[i] + p[1]);
        const rank = xShape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
        const start = paddings.map((p)=>p[0]).join(",");
        const end = paddings.map((p, i)=>p[0] + xShape[i]).join(",");
        const unpackedCoords = [
            "coords[0]",
            "coords[1]",
            "coords[2]",
            "coords[3]"
        ].slice(0, rank);
        const offset = mode === "reflect" ? 0 : 1;
        if (rank === 1) {
            this.userCode = `
        int start = ${start};
        int end = ${end};

        void main() {
          int outC = getOutputCoords();
          if (outC < start) {
            outC = start * 2 - outC - ${offset};
          } else if(outC >= end) {
            outC = (end - 1) * 2 - outC + ${offset};
          }
          setOutput(getX(outC - start));
        }
      `;
            return;
        }
        this.userCode = `
      ${dtype} start = ${dtype}(${start});
      ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outC = getOutputCoords();
        for (int i = 0; i < ${rank}; i++) {
          if (outC[i] < start[i]) {
            outC[i] = start[i] * 2 - outC[i] - ${offset};
          } else if(outC[i] >= end[i]) {
            outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
          }
        }
        ${dtype} coords = outC - start;
        setOutput(getX(${unpackedCoords}));
      }
    `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fauBg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Example shader code for
 * `mirrorPad(tf.tensor1d([1, 2, 3], 'int32'), [[2, 2]], 'reflect')`
 * ```
 *    const int start = int(2);
 *    const int end = int(5);
 *
 *    void main() {
 *       int outputLoc = getOutputCoords();
 *       vec4 result = vec4(0.);
 *
 *       int rc = outputLoc;
 *
 *       int source = rc;
 *       if (source < start) {
 *         source = start * 2 - source - 0;
 *       } else if (source >= end) {
 *         source = (end - 1) * 2 - source + 0;
 *       }
 *       source -= start;
 *
 *       result[0] = getChannel(getX(source), source);
 *       rc += 1;
 *       if(rc < 6) {
 *          int source = rc;
 *          if (source < start) {
 *            source = start * 2 - source - 0;
 *          } else if (source >= end) {
 *            source = (end - 1) * 2 - source + 0;
 *          }
 *          source -= start;
 *
 *         result[1] = getChannel(getX(source), source);
 *       }
 *
 *       setOutput(result);
 *     }
 * ```
 */ parcelHelpers.export(exports, "MirrorPadPackedProgram", ()=>MirrorPadPackedProgram);
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class MirrorPadPackedProgram {
    constructor(xShape, paddings, mode){
        this.variableNames = [
            "x"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = paddings.map((p, i)=>p[0] + xShape[i] + p[1]);
        const rank = xShape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
        const start = paddings.map((p)=>p[0]).join(",");
        const end = paddings.map((p, i)=>p[0] + xShape[i]).join(",");
        const coords = (0, _packingUtil.getChannels)("rc", rank);
        const source = (0, _packingUtil.getChannels)("source", rank);
        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;
        const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
        const offset = mode === "reflect" ? 0 : 1;
        let mainLoop = "";
        if (rank === 1) {
            const padSetup = `
        ${dtype} source = rc;
        if (source < start) {
          source = start * 2 - source - ${offset};
        } else if (source >= end) {
          source = (end - 1) * 2 - source + ${offset};
        }
        source -= start;
      `;
            mainLoop = `
        ${dtype} rc = outputLoc;
        ${padSetup}
        result[0] = getChannel(getX(${source.join()}), ${innerDims});
        ${coords[rank - 1]} += 1;
        if(${cLimit}) {
          ${padSetup}
          result[1] = getChannel(getX(${source.join()}), ${innerDims});
        }
      `;
        } else {
            const padSetup = `
        ${dtype} source = rc;
        ${dtype} lt = ${dtype}(lessThan(source, start));
        ${dtype} gte = ${dtype}(greaterThanEqual(source, end));
        ${dtype} orig = 1 - (lt + gte);
        source = orig * source +
                lt * (start * 2 - source - ${offset}) +
                gte * ((end - 1) * 2 - source + ${offset});
        source -= start;
      `;
            mainLoop = `
        ${dtype} rc = outputLoc;
        ${padSetup}
        result[0] = getChannel(getX(${source.join()}), ${innerDims});
        ${coords[rank - 1]} += 1;
        if(${cLimit}) {
          ${padSetup}
          result[1] = getChannel(getX(${source.join()}), ${innerDims});
        }
        rc = outputLoc;
        ${coords[rank - 2]} += 1;
        if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {
          ${padSetup}
          result[2] = getChannel(getX(${source.join()}), ${innerDims});
          ${coords[rank - 1]} += 1;
          if(${cLimit}) {
            ${padSetup}
            result[3] = getChannel(getX(${source.join()}), ${innerDims});
          }
        }
      `;
        }
        this.userCode = `
      const ${dtype} start = ${dtype}(${start});
      const ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${mainLoop}
        setOutput(result);
      }
    `;
    }
}

},{"./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"f0WUU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mod", ()=>mod);
parcelHelpers.export(exports, "modConfig", ()=>modConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const MOD = `if (b == 0.0) return NAN;
  return mod(a, b);`;
const MOD_PACKED = `
  vec4 result = mod(a, b);
  bvec4 isNaN = equal(b, vec4(0.0));
  ` + (0, _binaryopPackedGpu.CHECK_NAN_SNIPPET_PACKED) + `
  return result;
`;
const mod = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: MOD,
    packedOpSnippet: MOD_PACKED
});
const modConfig = {
    kernelName: (0, _tfjsCore.Mod),
    backendName: "webgl",
    kernelFunc: mod
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_packed_gpu":"hkzp7","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"wTvWa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "multinomial", ()=>multinomial);
parcelHelpers.export(exports, "multinomialConfig", ()=>multinomialConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _multinomialGpu = require("../multinomial_gpu");
var _softmax = require("./Softmax");
function multinomial(args) {
    const { inputs, backend, attrs } = args;
    const { logits } = inputs;
    const { numSamples, seed, normalized } = attrs;
    const probs = normalized ? logits : (0, _softmax.softmax)({
        inputs: {
            logits
        },
        backend,
        attrs: {
            dim: logits.shape.length - 1
        }
    });
    const batchSize = probs.shape[0];
    const numOutcomes = probs.shape[1];
    const program = new (0, _multinomialGpu.MultinomialProgram)(batchSize, numOutcomes, numSamples);
    const customValues = [
        [
            seed
        ]
    ];
    const res = backend.runWebGLProgram(program, [
        probs
    ], "int32", customValues);
    if (!normalized) backend.disposeIntermediateTensorInfo(probs);
    return res;
}
const multinomialConfig = {
    kernelName: (0, _tfjsCore.Multinomial),
    backendName: "webgl",
    kernelFunc: multinomial
};

},{"@tensorflow/tfjs-core":"21Ftl","../multinomial_gpu":"6mU57","./Softmax":"cHx7b","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6mU57":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MultinomialProgram", ()=>MultinomialProgram);
class MultinomialProgram {
    constructor(batchSize, numOutcomes, numSamples){
        this.variableNames = [
            "probs"
        ];
        this.customUniforms = [
            {
                name: "seed",
                type: "float"
            }
        ];
        this.outputShape = [
            batchSize,
            numSamples
        ];
        this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];

        float r = random(seed);
        float cdf = 0.0;

        for (int i = 0; i < ${numOutcomes - 1}; i++) {
          cdf += getProbs(batch, i);

          if (r < cdf) {
            setOutput(float(i));
            return;
          }
        }

        // If no other event happened, last event happened.
        setOutput(float(${numOutcomes - 1}));
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cHx7b":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "softmax", ()=>softmax);
parcelHelpers.export(exports, "softmaxConfig", ()=>softmaxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _exp = require("./Exp");
var _max = require("./Max");
var _realDiv = require("./RealDiv");
var _reshape = require("./Reshape");
var _sub = require("./Sub");
var _sum = require("./Sum");
function softmax(args) {
    const { inputs, backend, attrs } = args;
    const { logits } = inputs;
    const { dim } = attrs;
    const axes = (0, _tfjsCore.util).parseAxisParam([
        dim
    ], logits.shape);
    const maxLogit = (0, _max.max)({
        inputs: {
            x: logits
        },
        backend,
        attrs: {
            reductionIndices: axes,
            keepDims: false
        }
    });
    const expandedShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(maxLogit.shape, axes);
    const maxLogitsReshaped = (0, _reshape.reshape)({
        inputs: {
            x: maxLogit
        },
        backend,
        attrs: {
            shape: expandedShape
        }
    });
    const a = (0, _sub.sub)({
        inputs: {
            a: logits,
            b: maxLogitsReshaped
        },
        backend
    });
    const b = (0, _exp.exp)({
        inputs: {
            x: a
        },
        backend
    });
    const sumExp = (0, _sum.sum)({
        inputs: {
            x: b
        },
        backend,
        attrs: {
            axis: axes,
            keepDims: false
        }
    });
    const sumExpReshaped = (0, _reshape.reshape)({
        inputs: {
            x: sumExp
        },
        backend,
        attrs: {
            shape: expandedShape
        }
    });
    const res = (0, _realDiv.realDiv)({
        inputs: {
            a: b,
            b: sumExpReshaped
        },
        backend
    });
    backend.disposeIntermediateTensorInfo(maxLogit);
    backend.disposeIntermediateTensorInfo(maxLogitsReshaped);
    backend.disposeIntermediateTensorInfo(a);
    backend.disposeIntermediateTensorInfo(b);
    backend.disposeIntermediateTensorInfo(sumExp);
    backend.disposeIntermediateTensorInfo(sumExpReshaped);
    return res;
}
const softmaxConfig = {
    kernelName: (0, _tfjsCore.Softmax),
    backendName: "webgl",
    kernelFunc: softmax
};

},{"@tensorflow/tfjs-core":"21Ftl","./Exp":"i4xew","./Max":"11YMi","./RealDiv":"cbtMz","./Reshape":"fgZen","./Sub":"gV1EU","./Sum":"c4eiD","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cbtMz":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "realDiv", ()=>realDiv);
parcelHelpers.export(exports, "realDivConfig", ()=>realDivConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
// Without the equality check div produces 0.9999 for a = b, which when
// floored can cause errors.
const DIV = `
if (a == b) {
  return 1.0;
};
return a / b;`;
// We do the same as in ./binaryop_gpu, with vec4 and ivec4.
// On Linux, the vectorized implementation produces NaNs when a and b are 0.
const DIV_PACKED = `
  // vec4 one = vec4(equal(a, b));
  // return one + (vec4(1.0) - one) * a / b;
  vec4 result = a / b;
  if(a.x == b.x) {
    result.x = 1.;
  }
  if(a.y == b.y) {
    result.y = 1.;
  }
  if(a.z == b.z) {
    result.z = 1.;
  }
  if(a.w == b.w) {
    result.w = 1.;
  }

  return result;
`;
const realDiv = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: DIV,
    packedOpSnippet: DIV_PACKED,
    checkOutOfBounds: true
});
const realDivConfig = {
    kernelName: (0, _tfjsCore.RealDiv),
    backendName: "webgl",
    kernelFunc: realDiv
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gV1EU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sub", ()=>sub);
parcelHelpers.export(exports, "subConfig", ()=>subConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const SUB = "return a - b;";
const sub = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: SUB,
    packedOpSnippet: SUB,
    supportsComplex: true,
    cpuKernelImpl: (0, _shared.subImplCPU)
});
const subConfig = {
    kernelName: (0, _tfjsCore.Sub),
    backendName: "webgl",
    kernelFunc: sub
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6P8i7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
// This doesn't use unaryKernelFunc because negImplCPU is not of type
// SimpleUnaryKernelImplCPU.
parcelHelpers.export(exports, "neg", ()=>neg);
parcelHelpers.export(exports, "negConfig", ()=>negConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _unaryopGpu = require("../unaryop_gpu");
var _unaryopPackedGpu = require("../unaryop_packed_gpu");
const NEG = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  return -x;
`;
const NEG_PACKED = `
  vec4 result = -x;
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
function neg(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    if (backend.shouldExecuteOnCPU([
        x
    ])) {
        const xData = backend.texData.get(x.dataId);
        const [outValues, newShape] = (0, _shared.negImplCPU)(xData.values, x.shape, x.dtype);
        return backend.makeTensorInfo(newShape, x.dtype, outValues);
    }
    let program;
    if ((0, _tfjsCore.env)().getBool("WEBGL_PACK_UNARY_OPERATIONS")) program = new (0, _unaryopPackedGpu.UnaryOpPackedProgram)(x.shape, NEG_PACKED);
    else program = new (0, _unaryopGpu.UnaryOpProgram)(x.shape, NEG);
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype);
}
const negConfig = {
    kernelName: (0, _tfjsCore.Neg),
    backendName: "webgl",
    kernelFunc: neg
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../unaryop_gpu":"gIMrR","../unaryop_packed_gpu":"c4D5K","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fEqP1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV3", ()=>nonMaxSuppressionV3);
parcelHelpers.export(exports, "nonMaxSuppressionV3Config", ()=>nonMaxSuppressionV3Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
const nonMaxSuppressionV3Impl = (0, _tfjsCore.kernel_impls).nonMaxSuppressionV3Impl;
function nonMaxSuppressionV3(args) {
    (0, _tfjsCore.backend_util).warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
    const { inputs, backend, attrs } = args;
    const { boxes, scores } = inputs;
    const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
    const boxesVals = backend.readSync(boxes.dataId);
    const scoresVals = backend.readSync(scores.dataId);
    const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
    return backend.makeTensorInfo([
        selectedIndices.length
    ], "int32", new Int32Array(selectedIndices));
}
const nonMaxSuppressionV3Config = {
    kernelName: (0, _tfjsCore.NonMaxSuppressionV3),
    backendName: "webgl",
    kernelFunc: nonMaxSuppressionV3
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5Qv9T":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV4", ()=>nonMaxSuppressionV4);
parcelHelpers.export(exports, "nonMaxSuppressionV4Config", ()=>nonMaxSuppressionV4Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
const nonMaxSuppressionV4Impl = (0, _tfjsCore.kernel_impls).nonMaxSuppressionV4Impl;
function nonMaxSuppressionV4(args) {
    (0, _tfjsCore.backend_util).warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
    const { inputs, backend, attrs } = args;
    const { boxes, scores } = inputs;
    const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
    const boxesVals = backend.readSync(boxes.dataId);
    const scoresVals = backend.readSync(scores.dataId);
    const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
    return [
        backend.makeTensorInfo([
            selectedIndices.length
        ], "int32", new Int32Array(selectedIndices)),
        backend.makeTensorInfo([], "int32", new Int32Array([
            validOutputs
        ]))
    ];
}
const nonMaxSuppressionV4Config = {
    kernelName: (0, _tfjsCore.NonMaxSuppressionV4),
    backendName: "webgl",
    kernelFunc: nonMaxSuppressionV4
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"T9Z8C":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV5", ()=>nonMaxSuppressionV5);
parcelHelpers.export(exports, "nonMaxSuppressionV5Config", ()=>nonMaxSuppressionV5Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
const nonMaxSuppressionV5Impl = (0, _tfjsCore.kernel_impls).nonMaxSuppressionV5Impl;
function nonMaxSuppressionV5(args) {
    (0, _tfjsCore.backend_util).warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
    const { inputs, backend, attrs } = args;
    const { boxes, scores } = inputs;
    const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
    const boxesVals = backend.readSync(boxes.dataId);
    const scoresVals = backend.readSync(scores.dataId);
    const maxOutputSizeVal = maxOutputSize;
    const iouThresholdVal = iouThreshold;
    const scoreThresholdVal = scoreThreshold;
    const softNmsSigmaVal = softNmsSigma;
    const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
    return [
        backend.makeTensorInfo([
            selectedIndices.length
        ], "int32", new Int32Array(selectedIndices)),
        backend.makeTensorInfo([
            selectedScores.length
        ], "float32", new Float32Array(selectedScores))
    ];
}
const nonMaxSuppressionV5Config = {
    kernelName: (0, _tfjsCore.NonMaxSuppressionV5),
    backendName: "webgl",
    kernelFunc: nonMaxSuppressionV5
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6SVa6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "oneHot", ()=>oneHot);
parcelHelpers.export(exports, "oneHotConfig", ()=>oneHotConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _onehotGpu = require("../onehot_gpu");
var _reshape = require("./Reshape");
const oneHot = (args)=>{
    const { inputs, backend, attrs } = args;
    const { indices } = inputs;
    const { dtype, depth, onValue, offValue } = attrs;
    const indicesSize = (0, _tfjsCore.util).sizeFromShape(indices.shape);
    const program = new (0, _onehotGpu.OneHotProgram)(indicesSize, depth, onValue, offValue);
    const reshaped = (0, _reshape.reshape)({
        inputs: {
            x: indices
        },
        backend,
        attrs: {
            shape: [
                indicesSize
            ]
        }
    });
    const result = backend.runWebGLProgram(program, [
        reshaped
    ], dtype);
    backend.disposeIntermediateTensorInfo(reshaped);
    const outShape = [
        ...indices.shape,
        depth
    ];
    const out = (0, _reshape.reshape)({
        inputs: {
            x: result
        },
        backend,
        attrs: {
            shape: outShape
        }
    });
    backend.disposeIntermediateTensorInfo(result);
    return out;
};
const oneHotConfig = {
    kernelName: (0, _tfjsCore.OneHot),
    backendName: "webgl",
    kernelFunc: oneHot
};

},{"@tensorflow/tfjs-core":"21Ftl","../onehot_gpu":"9D6me","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9D6me":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "OneHotProgram", ()=>OneHotProgram);
class OneHotProgram {
    constructor(numIndices, depth, onValue, offValue){
        this.variableNames = [
            "indices"
        ];
        this.outputShape = [
            numIndices,
            depth
        ];
        this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int index = round(getIndices(coords.x));
        setOutput(mix(float(${offValue}), float(${onValue}),
                      float(index == coords.y)));
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cH3Uy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "onesLike", ()=>onesLike);
parcelHelpers.export(exports, "onesLikeConfig", ()=>onesLikeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _complex = require("./Complex");
var _fill = require("./Fill");
var _imag = require("./Imag");
var _real = require("./Real");
var _zerosLike = require("./ZerosLike");
function onesLike(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    if (x.dtype === "string") throw new Error("onesLike is not supported under string dtype");
    else if (x.dtype === "complex64") {
        const realPart = (0, _real.real)({
            inputs: {
                input: x
            },
            backend
        });
        const r = onesLike({
            inputs: {
                x: realPart
            },
            backend
        });
        const imagPart = (0, _imag.imag)({
            inputs: {
                input: x
            },
            backend
        });
        const i = (0, _zerosLike.zerosLike)({
            inputs: {
                x: imagPart
            },
            backend
        });
        const result = (0, _complex.complex)({
            inputs: {
                real: r,
                imag: i
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(realPart);
        backend.disposeIntermediateTensorInfo(r);
        backend.disposeIntermediateTensorInfo(imagPart);
        backend.disposeIntermediateTensorInfo(i);
        return result;
    } else // TODO(cais, smilkov): Add WebGL shader for onesLike:
    //   https://github.com/tensorflow/tfjs/issues/1293
    return (0, _fill.fill)({
        attrs: {
            shape: x.shape,
            dtype: x.dtype,
            value: 1
        },
        backend
    });
}
const onesLikeConfig = {
    kernelName: (0, _tfjsCore.OnesLike),
    backendName: "webgl",
    kernelFunc: onesLike
};

},{"@tensorflow/tfjs-core":"21Ftl","./Complex":"55NfL","./Fill":"6Rawr","./Imag":"3PMfw","./Real":"4HOZy","./ZerosLike":"28sQG","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"28sQG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "zerosLike", ()=>zerosLike);
parcelHelpers.export(exports, "zerosLikeConfig", ()=>zerosLikeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _complex = require("./Complex");
var _fill = require("./Fill");
var _imag = require("./Imag");
var _real = require("./Real");
function zerosLike(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    if (x.dtype === "complex64") {
        const realPart = (0, _real.real)({
            inputs: {
                input: x
            },
            backend
        });
        const r = zerosLike({
            inputs: {
                x: realPart
            },
            backend
        });
        const imagPart = (0, _imag.imag)({
            inputs: {
                input: x
            },
            backend
        });
        const i = zerosLike({
            inputs: {
                x: imagPart
            },
            backend
        });
        const result = (0, _complex.complex)({
            inputs: {
                real: r,
                imag: i
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(realPart);
        backend.disposeIntermediateTensorInfo(r);
        backend.disposeIntermediateTensorInfo(imagPart);
        backend.disposeIntermediateTensorInfo(i);
        return result;
    } else return (0, _fill.fill)({
        attrs: {
            shape: x.shape,
            dtype: x.dtype,
            value: x.dtype === "string" ? "" : 0
        },
        backend
    });
}
const zerosLikeConfig = {
    kernelName: (0, _tfjsCore.ZerosLike),
    backendName: "webgl",
    kernelFunc: zerosLike
};

},{"@tensorflow/tfjs-core":"21Ftl","./Complex":"55NfL","./Fill":"6Rawr","./Imag":"3PMfw","./Real":"4HOZy","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"73QIj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pack", ()=>pack);
parcelHelpers.export(exports, "packConfig", ()=>packConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _concat = require("./Concat");
var _expandDims = require("./ExpandDims");
function pack(args) {
    const { inputs, backend, attrs } = args;
    const { axis } = attrs;
    if (inputs.length === 1) return (0, _expandDims.expandDims)({
        inputs: {
            input: inputs[0]
        },
        backend,
        attrs: {
            dim: axis
        }
    });
    const shape = inputs[0].shape;
    const dtype = inputs[0].dtype;
    inputs.forEach((t)=>{
        (0, _tfjsCore.util).assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
        (0, _tfjsCore.util).assert(dtype === t.dtype, ()=>"All tensors passed to stack must have matching dtypes");
    });
    const intermediateTensorInfos = [];
    const expandedTensors = inputs.map((t)=>{
        const expandedT = (0, _expandDims.expandDims)({
            inputs: {
                input: t
            },
            backend,
            attrs: {
                dim: axis
            }
        });
        intermediateTensorInfos.push(expandedT);
        return expandedT;
    });
    const result = (0, _concat.concat)({
        inputs: expandedTensors,
        backend,
        attrs: {
            axis
        }
    });
    intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return result;
}
const packConfig = {
    kernelName: (0, _tfjsCore.Pack),
    backendName: "webgl",
    kernelFunc: pack
};

},{"@tensorflow/tfjs-core":"21Ftl","./Concat":"2gCZH","./ExpandDims":"5OvP3","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bOlXa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "padV2", ()=>padV2);
parcelHelpers.export(exports, "padV2Config", ()=>padV2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _padGpu = require("../pad_gpu");
var _padPackedGpu = require("../pad_packed_gpu");
var _fill = require("./Fill");
const padV2 = (args)=>{
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { paddings, constantValue } = attrs;
    if ((0, _tfjsCore.util).sizeFromShape(x.shape) === 0) {
        // Short-circuit the computation, since x doesn't have value, only
        // the shape is used to compute output shape to pad.
        const outputShape = paddings.map((p, i)=>p[0] + x.shape[i] + p[1]);
        return (0, _fill.fill)({
            backend,
            attrs: {
                shape: outputShape,
                value: constantValue,
                dtype: x.dtype
            }
        });
    }
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new (0, _padPackedGpu.PadPackedProgram)(x.shape, paddings, constantValue) : new (0, _padGpu.PadProgram)(x.shape, paddings, constantValue);
    const customValues = [
        [
            constantValue
        ]
    ];
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype, customValues);
};
const padV2Config = {
    kernelName: (0, _tfjsCore.PadV2),
    backendName: "webgl",
    kernelFunc: padV2
};

},{"@tensorflow/tfjs-core":"21Ftl","../pad_gpu":"1vFyL","../pad_packed_gpu":"9Lzxa","./Fill":"6Rawr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1vFyL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "PadProgram", ()=>PadProgram);
var _shaderCompiler = require("./shader_compiler");
class PadProgram {
    constructor(xShape, paddings, constantValue){
        this.variableNames = [
            "x"
        ];
        this.customUniforms = [
            {
                name: "value",
                type: "float"
            }
        ];
        this.outputShape = paddings.map((p, i)=>p[0] + xShape[i] + p[1]);
        const rank = xShape.length;
        const type = (0, _shaderCompiler.getCoordsDataType)(rank);
        const start = paddings.map((p)=>p[0]).join(",");
        const end = paddings.map((p, i)=>p[0] + xShape[i]).join(",");
        const unpackedCoords = [
            "coords[0]",
            "coords[1]",
            "coords[2]",
            "coords[3]"
        ].slice(0, rank);
        if (rank === 1) {
            this.userCode = `
        int start = ${start};
        int end = ${end};

        void main() {
          int outC = getOutputCoords();
          if (outC < start || outC >= end) {
            setOutput(value);
          } else {
            setOutput(getX(outC - start));
          }
        }
      `;
            return;
        }
        this.userCode = `
      ${type} start = ${type}(${start});
      ${type} end = ${type}(${end});

      void main() {
        ${type} outC = getOutputCoords();
        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
          setOutput(value);
        } else {
          ${type} coords = outC - start;
          setOutput(getX(${unpackedCoords}));
        }
      }
    `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9Lzxa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "PadPackedProgram", ()=>PadPackedProgram);
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class PadPackedProgram {
    constructor(xShape, paddings, constantValue){
        this.variableNames = [
            "x"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.customUniforms = [
            {
                name: "value",
                type: "float"
            }
        ];
        this.outputShape = paddings.map((p, i)=>p[0] + xShape[i] + p[1]);
        const rank = xShape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
        const start = paddings.map((p)=>p[0]).join(",");
        const end = paddings.map((p, i)=>p[0] + xShape[i]).join(",");
        const coords = (0, _packingUtil.getChannels)("rc", rank);
        const source = (0, _packingUtil.getChannels)("source", rank);
        const cLimit = `${coords[rank - 1]} < ${this.outputShape[rank - 1]}`;
        const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
        const componentSetup = [
            `${dtype} rc = outputLoc;`,
            `${coords[rank - 1]} += 1;
       if(${cLimit}) {
      `,
            rank === 1 ? "" : `}
       rc = outputLoc;
       ${coords[rank - 2]} += 1;
       if(${coords[rank - 2]} < ${this.outputShape[rank - 2]}) {`,
            rank === 1 ? "" : `  ${coords[rank - 1]} += 1;
         if(${cLimit}) {`
        ];
        const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";
        let mainLoop = "";
        for(let i = 0, j = rank === 1 ? 2 : 4; i < j; i++)mainLoop += `
        ${componentSetup[i]}
        if (${paddingArea}) {
          result[${i}] = float(value);
        } else {
          ${dtype} source = rc - start;
          result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
        }
      `;
        mainLoop += rank === 1 ? `} ` : `}}`;
        this.userCode = `
      const ${dtype} start = ${dtype}(${start});
      const ${dtype} end = ${dtype}(${end});

      void main() {
        ${dtype} outputLoc = getOutputCoords();
        vec4 result = vec4(0.);
        ${mainLoop}
        setOutput(result);
      }
    `;
    }
}

},{"./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5GYaP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pow", ()=>pow);
parcelHelpers.export(exports, "powConfig", ()=>powConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const POW = `
  if(a < 0.0 && floor(b) < b){
    return NAN;
  }
  if (b == 0.0) {
    return 1.0;
  }
  return (round(mod(b, 2.0)) != 1) ?
      pow(abs(a), b) : sign(a) * pow(abs(a), b);
`;
const POW_PACKED = `
  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
  vec4 result = multiplier * pow(abs(a), b);

  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
  bvec4 isExpZero = equal(b, vec4(0.0));
  result.r = isExpZero.r ? 1.0 : result.r;
  result.g = isExpZero.g ? 1.0 : result.g;
  result.b = isExpZero.b ? 1.0 : result.b;
  result.a = isExpZero.a ? 1.0 : result.a;

  bvec4 isNaN1 = lessThan(a, vec4(0.0));
  bvec4 isNaN2 = lessThan(floor(b), b);
  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);
  ` + (0, _binaryopPackedGpu.CHECK_NAN_SNIPPET_PACKED) + `
  return result;
`;
const pow = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: POW,
    packedOpSnippet: POW_PACKED
});
const powConfig = {
    kernelName: (0, _tfjsCore.Pow),
    backendName: "webgl",
    kernelFunc: pow
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_packed_gpu":"hkzp7","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1BmsY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "prod", ()=>prod);
parcelHelpers.export(exports, "prodConfig", ()=>prodConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reduce = require("../kernel_utils/reduce");
var _shared = require("../kernel_utils/shared");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function prod(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    const xRank = x.shape.length;
    const toDispose = [];
    const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    let permutedX = x;
    if (permutedAxes != null) {
        permutedX = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, xRank);
        toDispose.push(permutedX);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("prod", axes, xRank);
    let res;
    if (backend.shouldExecuteOnCPU([
        permutedX
    ])) {
        const xVals = backend.texData.get(permutedX.dataId).values;
        const { outVals, outShape, outDtype } = (0, _shared.prodImplCPU)(permutedX.shape, permutedX.dtype, xVals, axes);
        res = backend.makeTensorInfo(outShape, outDtype, outVals);
    } else {
        const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(permutedX.shape, axes);
        const inSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
        const a2D = (0, _reshape.reshape)({
            inputs: {
                x: permutedX
            },
            backend,
            attrs: {
                shape: [
                    -1,
                    inSize
                ]
            }
        });
        const outputDType = (0, _tfjsCore.sumOutType)(x.dtype);
        const reduced = (0, _reduce.reduce)(a2D, outputDType, "prod", backend);
        res = (0, _reshape.reshape)({
            inputs: {
                x: reduced
            },
            backend,
            attrs: {
                shape: outShape
            }
        });
        toDispose.push(a2D);
        toDispose.push(reduced);
    }
    if (keepDims) {
        toDispose.push(res);
        const newShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(res.shape, origAxes);
        res = (0, _reshape.reshape)({
            inputs: {
                x: res
            },
            backend,
            attrs: {
                shape: newShape
            }
        });
    }
    toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return res;
}
const prodConfig = {
    kernelName: (0, _tfjsCore.Prod),
    backendName: "webgl",
    kernelFunc: prod
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/reduce":"dr0Xl","../kernel_utils/shared":"e4M7v","./Reshape":"fgZen","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dhNCK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedGather", ()=>raggedGather);
parcelHelpers.export(exports, "raggedGatherConfig", ()=>raggedGatherConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function raggedGather(args) {
    const { inputs, backend, attrs } = args;
    const { paramsNestedSplits, paramsDenseValues, indices } = inputs;
    const { outputRaggedRank } = attrs;
    const $paramsNestedSplits = paramsNestedSplits.map((t)=>backend.readSync(t.dataId));
    const $paramsNestedSplitsShapes = paramsNestedSplits.map((t)=>t.shape);
    const $paramsDenseValues = backend.readSync(paramsDenseValues.dataId);
    const $indices = backend.readSync(indices.dataId);
    const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = (0, _shared.raggedGatherImplCPU)($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank);
    const outputNestedSplitsTensors = outputNestedSplits.map((splits)=>backend.makeTensorInfo([
            splits.length
        ], "int32", splits));
    const outputDenseValuesTensor = backend.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues);
    return outputNestedSplitsTensors.concat([
        outputDenseValuesTensor
    ]);
}
const raggedGatherConfig = {
    kernelName: (0, _tfjsCore.RaggedGather),
    backendName: "webgl",
    kernelFunc: raggedGather
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"e99uc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedRange", ()=>raggedRange);
parcelHelpers.export(exports, "raggedRangeConfig", ()=>raggedRangeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function raggedRange(args) {
    const { inputs, backend } = args;
    const { starts, limits, deltas } = inputs;
    const $starts = backend.readSync(starts.dataId);
    const $limits = backend.readSync(limits.dataId);
    const $deltas = backend.readSync(deltas.dataId);
    const [rtNestedSplitsData, rtDenseValuesData] = (0, _shared.raggedRangeImplCPU)($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape);
    const rtNestedSplits = backend.makeTensorInfo([
        rtNestedSplitsData.length
    ], "int32", rtNestedSplitsData);
    const rtDenseValues = backend.makeTensorInfo([
        rtDenseValuesData.length
    ], starts.dtype, rtDenseValuesData);
    return [
        rtNestedSplits,
        rtDenseValues
    ];
}
const raggedRangeConfig = {
    kernelName: (0, _tfjsCore.RaggedRange),
    backendName: "webgl",
    kernelFunc: raggedRange
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c17RR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedTensorToTensor", ()=>raggedTensorToTensor);
parcelHelpers.export(exports, "raggedTensorToTensorConfig", ()=>raggedTensorToTensorConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function raggedTensorToTensor(args) {
    const { inputs, backend, attrs } = args;
    const { shape, values, defaultValue, rowPartitionTensors } = inputs;
    const { rowPartitionTypes } = attrs;
    const $shape = backend.readSync(shape.dataId);
    const $values = backend.readSync(values.dataId);
    const $defaultValue = backend.readSync(defaultValue.dataId);
    const $rowPartitionValues = rowPartitionTensors.map((t)=>backend.readSync(t.dataId));
    const rowPartitionValuesShapes = rowPartitionTensors.map((t)=>t.shape);
    const [outputShape, output] = (0, _shared.raggedTensorToTensorImplCPU)($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);
    return backend.makeTensorInfo(outputShape, values.dtype, output);
}
const raggedTensorToTensorConfig = {
    kernelName: (0, _tfjsCore.RaggedTensorToTensor),
    backendName: "webgl",
    kernelFunc: raggedTensorToTensor
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4ulNZ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "range", ()=>range);
parcelHelpers.export(exports, "rangeConfig", ()=>rangeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
const range = (args)=>{
    const { backend, attrs } = args;
    const { start, stop, step, dtype } = attrs;
    const values = (0, _shared.rangeImplCPU)(start, stop, step, dtype);
    return backend.makeTensorInfo([
        values.length
    ], dtype, values);
};
const rangeConfig = {
    kernelName: (0, _tfjsCore.Range),
    backendName: "webgl",
    kernelFunc: range
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5wKjZ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reciprocal", ()=>reciprocal);
parcelHelpers.export(exports, "reciprocalConfig", ()=>reciprocalConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const RECIPROCAL = `return 1.0 / x;`;
const reciprocal = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: RECIPROCAL
});
const reciprocalConfig = {
    kernelName: (0, _tfjsCore.Reciprocal),
    backendName: "webgl",
    kernelFunc: reciprocal
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fTpFl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "relu", ()=>relu);
parcelHelpers.export(exports, "reluConfig", ()=>reluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const RELU = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  return (x < 0.0) ? 0.0 : x;
`;
const RELU_PACKED = `
  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
const relu = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: RELU,
    packedOpSnippet: RELU_PACKED
});
const reluConfig = {
    kernelName: (0, _tfjsCore.Relu),
    backendName: "webgl",
    kernelFunc: relu
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"inRmD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "relu6", ()=>relu6);
parcelHelpers.export(exports, "relu6Config", ()=>relu6Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _unaryopGpu = require("../unaryop_gpu");
const RELU6 = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
const RELU6_PACKED = `
  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
const relu6 = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: RELU6,
    packedOpSnippet: RELU6_PACKED
});
const relu6Config = {
    kernelName: (0, _tfjsCore.Relu6),
    backendName: "webgl",
    kernelFunc: relu6
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iNia7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeBilinear", ()=>resizeBilinear);
parcelHelpers.export(exports, "resizeBilinearConfig", ()=>resizeBilinearConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _resizeBilinearGpu = require("../resize_bilinear_gpu");
var _resizeBilinearPackedGpu = require("../resize_bilinear_packed_gpu");
function resizeBilinear(args) {
    const { inputs, backend, attrs } = args;
    const { images } = inputs;
    const { alignCorners, halfPixelCenters, size } = attrs;
    const [newHeight, newWidth] = size;
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new (0, _resizeBilinearPackedGpu.ResizeBilinearPackedProgram)(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new (0, _resizeBilinearGpu.ResizeBilinearProgram)(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
    return backend.runWebGLProgram(program, [
        images
    ], "float32");
}
const resizeBilinearConfig = {
    kernelName: (0, _tfjsCore.ResizeBilinear),
    backendName: "webgl",
    kernelFunc: resizeBilinear
};

},{"@tensorflow/tfjs-core":"21Ftl","../resize_bilinear_gpu":"bR7AJ","../resize_bilinear_packed_gpu":"gl6xN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bR7AJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ResizeBilinearProgram", ()=>ResizeBilinearProgram);
class ResizeBilinearProgram {
    constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters){
        this.variableNames = [
            "A"
        ];
        this.outputShape = [];
        const [batch, oldHeight, oldWidth, depth] = inputShape;
        this.outputShape = [
            batch,
            newHeight,
            newWidth,
            depth
        ];
        const effectiveInSize = [
            alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
            alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
        ];
        const effectiveOutSize = [
            alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
            alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
        ];
        let sourceFracIndexRC;
        if (halfPixelCenters) sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC` + ` - vec2(0.5)`;
        else sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
        this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${effectiveInSize[0] / effectiveOutSize[0]},
          ${effectiveInSize[1] / effectiveOutSize[1]});
      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${sourceFracIndexRC};

        // Compute the four integer indices.
        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
        ivec2 sourceCeilRC = ivec2(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);

        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);

        float top = topLeft + (topRight - topLeft) * fracRC.y;
        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
        float newValue = top + (bottom - top) * fracRC.x;

        setOutput(newValue);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gl6xN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ResizeBilinearPackedProgram", ()=>ResizeBilinearPackedProgram);
class ResizeBilinearPackedProgram {
    constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = [];
        const [batch, oldHeight, oldWidth, depth] = inputShape;
        this.outputShape = [
            batch,
            newHeight,
            newWidth,
            depth
        ];
        const effectiveInSize = [
            alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
            alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
        ];
        const effectiveOutSize = [
            alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
            alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
        ];
        let sourceFracIndexRC;
        if (halfPixelCenters) sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * ` + `effectiveInputOverOutputRatioRC - vec3(0.5)`;
        else sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
        this.userCode = `
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${effectiveInSize[0] / effectiveOutSize[0]},
          ${effectiveInSize[1] / effectiveOutSize[1]},
          ${effectiveInSize[1] / effectiveOutSize[1]});
      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
                                     ${oldWidth}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${sourceFracIndexRC};

        // Compute the four integer indices.
        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
        ivec3 sourceCeilRC = ivec3(
          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${depth - 1};
        bool hasNextRow = coords.z < ${newWidth - 1};

        // In parallel, construct four corners for all four components in
        // packed 2x2 cell.
        vec4 topLeft = vec4(
          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 bottomLeft = vec4(
          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);

        vec4 topRight = vec4(
          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec4 bottomRight = vec4(
          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);

        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);

        vec4 top = mix(topLeft, topRight, fracRC.yyzz);
        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
        vec4 newValue = mix(top, bottom, fracRC.x);

        setOutput(newValue);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gEi6O":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeBilinearGrad", ()=>resizeBilinearGrad);
parcelHelpers.export(exports, "resizeBilinearGradConfig", ()=>resizeBilinearGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _resizeBilinearBackpropGpu = require("../resize_bilinear_backprop_gpu");
function resizeBilinearGrad(args) {
    const { inputs, backend, attrs } = args;
    const { images, dy } = inputs;
    const { alignCorners } = attrs;
    const program = new (0, _resizeBilinearBackpropGpu.ResizeBilinearBackpropProgram)(dy.shape, images.shape, alignCorners);
    return backend.runWebGLProgram(program, [
        dy
    ], dy.dtype);
}
const resizeBilinearGradConfig = {
    kernelName: (0, _tfjsCore.ResizeBilinearGrad),
    backendName: "webgl",
    kernelFunc: resizeBilinearGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../resize_bilinear_backprop_gpu":"cwxV9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cwxV9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ResizeBilinearBackpropProgram", ()=>ResizeBilinearBackpropProgram);
class ResizeBilinearBackpropProgram {
    constructor(dyShape, inputShape, alignCorners){
        this.variableNames = [
            "dy"
        ];
        this.outputShape = [];
        this.outputShape = inputShape;
        const [, xHeight, xWidth] = inputShape;
        const [, yHeight, yWidth] = dyShape;
        // In the backwards pass, we want to find the pixels that were generated for
        // each pixel in the input image the forward pass and add the corresponding
        // coefficient from dy to the gradient (with some interpolation).
        const effectiveXSize = [
            alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
            alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
        ];
        const effectiveYSize = [
            alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
            alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
        ];
        const heightScale = effectiveXSize[0] / effectiveYSize[0];
        const widthScale = effectiveXSize[1] / effectiveYSize[1];
        const invHeightScale = 1 / heightScale;
        const invWidthScale = 1 / widthScale;
        // This defines the size of the window of values around a particular
        // index in dy that we want to search for contributions to dx.
        const winHeight = Math.ceil(invHeightScale) * 2 + 2;
        const winWidth = Math.ceil(invWidthScale) * 2 + 2;
        this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${heightScale});
        const float widthScale = float(${widthScale});

        const float invHeightScale = float(${invHeightScale});
        const float invWidthScale = float(${invWidthScale});

        const int winHeight = int(${winHeight});
        const int winWidth = int(${winWidth});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(startRLerp - float(winHeight / 2));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(startCLerp - float(winWidth / 2));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${yHeight}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${yWidth}) {
              continue;
            }

            float dxR = float(dyR) * heightScale;
            int topDxRIndex = int(floor(dxR));
            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));
            float dxRLerp = dxR - float(topDxRIndex);
            float inverseDxRLerp = 1.0 - dxRLerp;

            float dxC = float(dyC) * widthScale;
            int leftDxCIndex = int(floor(dxC));
            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));
            float dxCLerp = dxC - float(leftDxCIndex);
            float inverseDxCLerp = 1.0 - dxCLerp;

            if (r == topDxRIndex && c == leftDxCIndex) {
              // topLeft
              accumulator +=
                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
            }

            if (r == topDxRIndex && c == rightDxCIndex) {
              // topRight
              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
            }

            if (r == bottomDxRIndex && c == leftDxCIndex) {
              // bottomLeft
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
            }

            if (r == bottomDxRIndex && c == rightDxCIndex) {
              // bottomRight
              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eTSe5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeNearestNeighbor", ()=>resizeNearestNeighbor);
parcelHelpers.export(exports, "resizeNearestNeighborConfig", ()=>resizeNearestNeighborConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _resizeNearestNeighborGpu = require("../resize_nearest_neighbor_gpu");
var _resizeNearestNeighborPackedGpu = require("../resize_nearest_neighbor_packed_gpu");
function resizeNearestNeighbor(args) {
    const { inputs, backend, attrs } = args;
    const { images } = inputs;
    const { alignCorners, halfPixelCenters, size } = attrs;
    const [newHeight, newWidth] = size;
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new (0, _resizeNearestNeighborPackedGpu.ResizeNearestNeighborPackedProgram)(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new (0, _resizeNearestNeighborGpu.ResizeNearestNeighborProgram)(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
    return backend.runWebGLProgram(program, [
        images
    ], images.dtype);
}
const resizeNearestNeighborConfig = {
    kernelName: (0, _tfjsCore.ResizeNearestNeighbor),
    backendName: "webgl",
    kernelFunc: resizeNearestNeighbor
};

},{"@tensorflow/tfjs-core":"21Ftl","../resize_nearest_neighbor_gpu":"ggWuj","../resize_nearest_neighbor_packed_gpu":"92adt","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ggWuj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ResizeNearestNeighborProgram", ()=>ResizeNearestNeighborProgram);
class ResizeNearestNeighborProgram {
    constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters){
        this.variableNames = [
            "A"
        ];
        this.outputShape = [];
        const [batch, oldHeight, oldWidth, depth] = inputShape;
        this.outputShape = [
            batch,
            newHeight,
            newWidth,
            depth
        ];
        const effectiveInSize = [
            alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
            alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
        ];
        const effectiveOutSize = [
            alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
            alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
        ];
        // When align corners is false, we rounds the value with floor.
        const roundBase = alignCorners ? "0.5" : "0.0";
        let sourceFracIndexRC;
        if (halfPixelCenters) sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC` + `, vec2(0.0))`;
        else sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
        this.userCode = `
      const vec2 effectiveInputOverOutputRatioRC = vec2(
          ${effectiveInSize[0] / effectiveOutSize[0]},
          ${effectiveInSize[1] / effectiveOutSize[1]});
      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        ivec2 yRC = coords.yz;

        // Fractional source index.
        vec2 sourceFracIndexRC = ${sourceFracIndexRC};

        // Compute the coordinators of nearest neighbor point.
        ivec2 sourceNearestRC = ivec2(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);

        setOutput(newValue);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"92adt":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ResizeNearestNeighborPackedProgram", ()=>ResizeNearestNeighborPackedProgram);
class ResizeNearestNeighborPackedProgram {
    constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters){
        this.variableNames = [
            "A"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = [];
        const [batch, oldHeight, oldWidth, depth] = inputShape;
        this.outputShape = [
            batch,
            newHeight,
            newWidth,
            depth
        ];
        const effectiveInSize = [
            alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
            alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
        ];
        const effectiveOutSize = [
            alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
            alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
        ];
        // When align corners is false, we rounds the value with floor.
        const roundBase = alignCorners ? "0.5" : "0.0";
        let sourceFracIndexRC;
        if (halfPixelCenters) sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * ` + `effectiveInputOverOutputRatioRC, vec3(0.0))`;
        else sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
        this.userCode = `
      const vec3 effectiveInputOverOutputRatioRC = vec3(
          ${effectiveInSize[0] / effectiveOutSize[0]},
          ${effectiveInSize[1] / effectiveOutSize[1]},
          ${effectiveInSize[1] / effectiveOutSize[1]});
      const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
                                     ${oldWidth}.0);

      float getAValue(int b, int r, int c, int d) {
        return getChannel(getA(b, r, c, d), vec2(c, d));
      }

      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        // Calculate values for next column in yRC.z.
        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);

        // Fractional source index.
        vec3 sourceFracIndexRC = ${sourceFracIndexRC};

        // Compute the coordinators of nearest neighbor point.
        ivec3 sourceNearestRC = ivec3(
          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));

        // Should we calculate next column and row elements in 2x2 packed cell.
        bool hasNextCol = d < ${depth - 1};
        bool hasNextRow = coords.z < ${newWidth - 1};

        vec4 newValue = vec4(
          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
                     : 0.0,
          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
                     : 0.0,
          (hasNextRow && hasNextCol) ?
            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);

        setOutput(newValue);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fZoE2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeNearestNeighborGrad", ()=>resizeNearestNeighborGrad);
parcelHelpers.export(exports, "resizeNearestNeighborGradConfig", ()=>resizeNearestNeighborGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _resizeNearestNeighborBackpropGpu = require("../resize_nearest_neighbor_backprop_gpu");
function resizeNearestNeighborGrad(args) {
    const { inputs, backend, attrs } = args;
    const { images, dy } = inputs;
    const { alignCorners } = attrs;
    const program = new (0, _resizeNearestNeighborBackpropGpu.ResizeNearestNeigborBackpropProgram)(dy.shape, images.shape, alignCorners);
    return backend.runWebGLProgram(program, [
        dy
    ], dy.dtype);
}
const resizeNearestNeighborGradConfig = {
    kernelName: (0, _tfjsCore.ResizeNearestNeighborGrad),
    backendName: "webgl",
    kernelFunc: resizeNearestNeighborGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../resize_nearest_neighbor_backprop_gpu":"9j4W1","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9j4W1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ResizeNearestNeigborBackpropProgram", ()=>ResizeNearestNeigborBackpropProgram);
class ResizeNearestNeigborBackpropProgram {
    constructor(dyShape, inputShape, alignCorners){
        this.variableNames = [
            "dy"
        ];
        this.outputShape = [];
        this.outputShape = inputShape;
        const [, xHeight, xWidth] = inputShape;
        const [, yHeight, yWidth] = dyShape;
        // In the backwards pass, we want to find the pixels that were generated for
        // each pixel in the input image the forward pass and add the corresponding
        // coefficient from dy to the gradient (with some interpolation).
        const effectiveXSize = [
            alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
            alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
        ];
        const effectiveYSize = [
            alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
            alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
        ];
        const heightScale = effectiveXSize[0] / effectiveYSize[0];
        const widthScale = effectiveXSize[1] / effectiveYSize[1];
        const invHeightScale = 1 / heightScale;
        const invWidthScale = 1 / widthScale;
        // This defines the size of the window of values around a particular
        // index in dy that we want to search for contributions to dx.
        const winHeight = Math.ceil(invHeightScale) * 2 + 2;
        const winWidth = Math.ceil(invWidthScale) * 2 + 2;
        this.userCode = `
      void main() {
        ivec4 coords = getOutputCoords();
        int b = coords[0];
        int d = coords[3];
        int r = coords[1];
        int c = coords[2];

        float accumulator = 0.0;

        const float heightScale = float(${heightScale});
        const float widthScale = float(${widthScale});

        const float invHeightScale = float(${invHeightScale});
        const float invWidthScale = float(${invWidthScale});

        const int winHeight = int(${winHeight});
        const int winWidth = int(${winWidth});

        // Compute bounds for where in dy we will look
        float startRLerp = floor(float(r) * invHeightScale);
        int startDyR = int(floor(startRLerp - float(winHeight / 2)));

        float startCLerp = floor(float(c) * invWidthScale);
        int startDyC = int(floor(startCLerp - float(winWidth / 2)));

        // Loop over dy
        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
          int dyR = dyROffset + startDyR;

          // Guard against the window exceeding the bounds of dy
          if (dyR < 0 || dyR >= ${yHeight}) {
            continue;
          }

          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
            int dyC = dyCOffset + startDyC;

            // Guard against the window exceeding the bounds of dy
            if (dyC < 0 || dyC >= ${yWidth}) {
              continue;
            }

            float sourceFracRow =
              float(${effectiveXSize[0]}) *
                (float(dyR) / float(${effectiveYSize[0]}));

            float sourceFracCol =
                float(${effectiveXSize[1]}) *
                  (float(dyC) / float(${effectiveYSize[1]}));

            int sourceNearestRow = int(min(
                float(int(${xHeight}) - 1),
                ${alignCorners} ? float(round(sourceFracRow)) :
                                  float(floor(sourceFracRow))));

            int sourceNearestCol = int(min(
                float(int(${xWidth}) - 1),
                ${alignCorners} ? float(round(sourceFracCol)) :
                                  float(floor(sourceFracCol))));

            if (r == sourceNearestRow && c == sourceNearestCol) {
              accumulator += getDy(b, dyR, dyC, d);
            }
          }
        }
        // End loop over dy

        setOutput(accumulator);
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"81ghw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reverse", ()=>reverse);
parcelHelpers.export(exports, "reverseConfig", ()=>reverseConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reverseGpu = require("../reverse_gpu");
var _reversePackedGpu = require("../reverse_packed_gpu");
var _identity = require("./Identity");
function reverse(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { dims } = attrs;
    const xRank = x.shape.length;
    const $dims = (0, _tfjsCore.util).parseAxisParam(dims, x.shape);
    if (xRank === 0) return (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    const program = (0, _tfjsCore.env)().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new (0, _reversePackedGpu.ReversePackedProgram)(x.shape, $dims) : new (0, _reverseGpu.ReverseProgram)(x.shape, $dims);
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype);
}
const reverseConfig = {
    kernelName: (0, _tfjsCore.Reverse),
    backendName: "webgl",
    kernelFunc: reverse
};

},{"@tensorflow/tfjs-core":"21Ftl","../reverse_gpu":"iDwSz","../reverse_packed_gpu":"jpTar","./Identity":"hY9aT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iDwSz":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ReverseProgram", ()=>ReverseProgram);
var _shaderCompiler = require("./shader_compiler");
class ReverseProgram {
    constructor(xShape, axis){
        this.variableNames = [
            "x"
        ];
        const rank = xShape.length;
        if (rank > 4) throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
        this.outputShape = xShape;
        if (rank === 1) {
            this.userCode = `
        void main() {
          int coord = getOutputCoords();
          setOutput(getX(${xShape[0]} - coord - 1));
        }
      `;
            return;
        }
        const getInCoord = (i)=>{
            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) return `${xShape[i]} - coords[${i}] - 1`;
            return `coords[${i}]`;
        };
        const inCoords = xShape.map((_, i)=>getInCoord(i)).join(",");
        const type = (0, _shaderCompiler.getCoordsDataType)(rank);
        this.userCode = `
      void main() {
        ${type} coords = getOutputCoords();
        setOutput(getX(${inCoords}));
      }
    `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jpTar":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ReversePackedProgram", ()=>ReversePackedProgram);
var _packingUtil = require("./packing_util");
var _shaderCompiler = require("./shader_compiler");
class ReversePackedProgram {
    constructor(xShape, axis){
        this.variableNames = [
            "x"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        const rank = xShape.length;
        if (rank > 4) throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
        this.outputShape = xShape;
        const channels = (0, _packingUtil.getChannels)("rc", rank);
        const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;
        const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;
        const type = (0, _shaderCompiler.getCoordsDataType)(rank);
        if (rank === 1) this.userCode = `
        void main(){
          int rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = getChannel(getX(${xShape[0]} - rc - 1),
            ${xShape[0]} - rc - 1);
          if(${nextColumn}){
              result.g = getChannel(getX(${xShape[0]} - (rc  + 1) - 1),
                ${xShape[0]} - (rc  + 1) - 1);
          }
          setOutput(result);
        }
      `;
        else this.userCode = `
        void main() {
          ${type} rc = getOutputCoords();
          vec4 result = vec4(0.);
          result.r = ${getR(channels.slice())};
          if(${nextColumn}){
            result.g = ${getG(channels.slice())};
          }
          if(${nextRow}) {
            result.b = ${getB(channels.slice())};
            if(${nextColumn}) {
              result.a = ${getA(channels.slice())};
            }
          }
          setOutput(result);
        }
    `;
        function getR(channels) {
            return getChannel(channels);
        }
        function getG(channels) {
            channels[rank - 1] = "(" + channels[rank - 1] + ` + 1)`;
            return getChannel(channels);
        }
        function getB(channels) {
            channels[rank - 2] = "(" + channels[rank - 2] + ` + 1)`;
            return getChannel(channels);
        }
        function getA(channels) {
            channels[rank - 1] = "(" + channels[rank - 1] + ` + 1)`;
            channels[rank - 2] = "(" + channels[rank - 2] + ` + 1)`;
            return getChannel(channels);
        }
        function getChannel(channels) {
            const inCoordsArray = xShape.map((_, i)=>getInCoord(i, channels));
            const inCoords = inCoordsArray.join(",");
            const innerDims = inCoordsArray.slice(-2).join(",");
            return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
        }
        function getInCoord(i, channels1) {
            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) return `${xShape[i]} - ${channels1[i]} - 1`;
            else return `${channels1[i]}`;
        }
    }
}

},{"./packing_util":"5ylpz","./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7nWSD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rotateWithOffsetConfig", ()=>rotateWithOffsetConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _rotateGpu = require("../rotate_gpu");
const rotateWithOffsetConfig = {
    kernelName: (0, _tfjsCore.RotateWithOffset),
    backendName: "webgl",
    kernelFunc: ({ inputs, attrs, backend })=>{
        const { image } = inputs;
        const { radians, fillValue, center } = attrs;
        const webglBackend = backend;
        const program = new (0, _rotateGpu.RotateProgram)(image.shape, fillValue);
        const [centerX, centerY] = (0, _tfjsCore.backend_util).getImageCenter(center, image.shape[1], image.shape[2]);
        const customValues = [
            [
                centerX,
                centerY,
                Math.sin(radians),
                Math.cos(radians)
            ]
        ];
        const output = webglBackend.runWebGLProgram(program, [
            image
        ], image.dtype, customValues);
        return output;
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","../rotate_gpu":"1XVMP","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1XVMP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "RotateProgram", ()=>RotateProgram);
class RotateProgram {
    constructor(imageShape, fillValue){
        this.variableNames = [
            "Image"
        ];
        this.outputShape = [];
        this.customUniforms = [
            {
                name: "params",
                type: "vec4"
            }
        ];
        const imageHeight = imageShape[1];
        const imageWidth = imageShape[2];
        this.outputShape = imageShape;
        let fillSnippet = "";
        if (typeof fillValue === "number") fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;
        else fillSnippet = `
        vec3 fill = vec3(${fillValue.join(",")});
        float outputValue = fill[coords[3]];`;
        this.userCode = `
        void main() {
          ivec4 coords = getOutputCoords();
          int x = coords[2];
          int y = coords[1];
          float coordXFloat = (float(x) - params[0]) * params[3] -
            (float(y) - params[1]) * params[2];
          float coordYFloat = (float(x) - params[0]) * params[2] +
            (float(y) - params[1]) * params[3];
          int coordX = int(round(coordXFloat + params[0]));
          int coordY = int(round(coordYFloat + params[1]));
          ${fillSnippet}
          if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gGWXk":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "round", ()=>round);
parcelHelpers.export(exports, "roundConfig", ()=>roundConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const ROUND = `
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`;
const round = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: ROUND
});
const roundConfig = {
    kernelName: (0, _tfjsCore.Round),
    backendName: "webgl",
    kernelFunc: round
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2xH8K":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rsqrt", ()=>rsqrt);
parcelHelpers.export(exports, "rsqrtConfig", ()=>rsqrtConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const RSQRT = `return inversesqrt(x);`;
const rsqrt = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: RSQRT,
    cpuKernelImpl: (0, _shared.rsqrtImplCPU)
});
const rsqrtConfig = {
    kernelName: (0, _tfjsCore.Rsqrt),
    backendName: "webgl",
    kernelFunc: rsqrt
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cYHjA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "scatterNd", ()=>scatterNd);
parcelHelpers.export(exports, "scatterNdConfig", ()=>scatterNdConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _scatterGpu = require("../scatter_gpu");
var _scatterPackedGpu = require("../scatter_packed_gpu");
var _reshape = require("./Reshape");
function scatterNd(args) {
    const { inputs, backend, attrs } = args;
    const { indices, updates } = inputs;
    const { shape } = attrs;
    const { sliceRank, numUpdates, sliceSize, strides, outputSize } = (0, _tfjsCore.backend_util).calculateShapes(updates, indices, shape);
    const flattenShape = [
        outputSize / sliceSize,
        sliceSize
    ];
    if (outputSize === 0) return backend.makeTensorInfo(shape, indices.dtype);
    const flattenIndices = (0, _reshape.reshape)({
        inputs: {
            x: indices
        },
        backend,
        attrs: {
            shape: [
                numUpdates,
                sliceRank
            ]
        }
    });
    const flattenX = (0, _reshape.reshape)({
        inputs: {
            x: updates
        },
        backend,
        attrs: {
            shape: [
                numUpdates,
                sliceSize
            ]
        }
    });
    const defaultValue = backend.makeTensorInfo([], "float32", new Float32Array([
        0
    ])); // scalar(0)
    let program;
    if ((0, _tfjsCore.env)().getBool("WEBGL_PACK")) program = new (0, _scatterPackedGpu.ScatterPackedProgram)(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);
    else program = new (0, _scatterGpu.ScatterProgram)(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);
    const res = backend.runWebGLProgram(program, [
        flattenX,
        flattenIndices,
        defaultValue
    ], flattenX.dtype);
    const reshaped = (0, _reshape.reshape)({
        inputs: {
            x: res
        },
        backend,
        attrs: {
            shape
        }
    });
    backend.disposeIntermediateTensorInfo(flattenIndices);
    backend.disposeIntermediateTensorInfo(flattenX);
    backend.disposeIntermediateTensorInfo(res);
    backend.disposeIntermediateTensorInfo(defaultValue);
    return reshaped;
}
const scatterNdConfig = {
    kernelName: (0, _tfjsCore.ScatterNd),
    backendName: "webgl",
    kernelFunc: scatterNd
};

},{"@tensorflow/tfjs-core":"21Ftl","../scatter_gpu":"kN25X","../scatter_packed_gpu":"gWrr3","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kN25X":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ScatterProgram", ()=>ScatterProgram);
var _shaderCompiler = require("./shader_compiler");
class ScatterProgram {
    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false){
        this.variableNames = [
            "updates",
            "indices",
            "defaultValue"
        ];
        this.outputShape = shape;
        const stridesType = (0, _shaderCompiler.getCoordsDataType)(strides.length);
        const dtype = (0, _shaderCompiler.getCoordsDataType)(shape.length);
        let indicesString = "";
        if (indicesRank === 1) indicesString = "i";
        else if (indicesRank === 2) indicesString = "i, j";
        const indicesSnippet = `getIndices(${indicesString})`;
        let updatesString = "";
        if (updatesRank === 1) updatesString = "i";
        else if (updatesRank === 2) updatesString = "i, coords[1]";
        const updatesSnippet = `getUpdates(${updatesString})`;
        let defaultValuesString = "";
        if (defaultIsTensor) defaultValuesString = "coords[0], coords[1]";
        const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`;
        const strideString = sliceDim > 1 ? "strides[j]" : "strides";
        this.userCode = `
        ${stridesType} strides = ${stridesType}(${strides});

        void main() {
          ${dtype} coords = getOutputCoords();
          float sum = 0.0;
          bool found = false;
          for (int i = 0; i < ${updateSize}; i++) {
            int flattenedIndex = 0;
            for (int j = 0; j < ${sliceDim}; j++) {
              int index = round(${indicesSnippet});
              flattenedIndex += index * ${strideString};
            }
            if (flattenedIndex == coords[0]) {
              sum += ${updatesSnippet};
              found = true;
            }
          }
          setOutput(mix(${defaultValueSnippet}, sum, float(found)));
        }
      `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gWrr3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ScatterPackedProgram", ()=>ScatterPackedProgram);
var _shaderCompiler = require("./shader_compiler");
class ScatterPackedProgram {
    constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true, defaultIsTensor = false){
        this.variableNames = [
            "updates",
            "indices",
            "defaultValue"
        ];
        this.packedInputs = true;
        this.packedOutput = true;
        this.outputShape = shape;
        const stridesType = (0, _shaderCompiler.getCoordsDataType)(strides.length);
        const dtype = (0, _shaderCompiler.getCoordsDataType)(shape.length);
        let indicesString = "";
        if (indicesRank === 1) indicesString = "i";
        else if (indicesRank === 2) indicesString = "i, j";
        const indicesSnippet = `getIndices(${indicesString})`;
        let updatesString = "";
        if (updatesRank === 1) updatesString = "i";
        else if (updatesRank === 2) updatesString = "i, coords[1]";
        const updatesSnippet = `getUpdates(${updatesString})`;
        let defaultValuesString = "";
        if (defaultIsTensor) defaultValuesString = "coords[0], coords[1]";
        const defaultValueSnippet = `getDefaultValue(${defaultValuesString})`;
        const strideString = sliceDim > 1 ? "strides[j]" : "strides";
        const strideString2 = sliceDim > 1 ? "strides[j + 1]" : "strides";
        this.userCode = `
        ${stridesType} strides = ${stridesType}(${strides});

        void main() {
          ${dtype} coords = getOutputCoords();
          vec4 sum = vec4(0.);
          vec4 found = vec4(0.);
          for (int i = 0; i < ${updateSize}; i+=2) {
            ivec2 flattenedIndex = ivec2(0);
            for (int j = 0; j < ${sliceDim}; j+=2) {
              ivec4 index = round(${indicesSnippet});
              flattenedIndex += index.xz * ${strideString};
              if (j + 1 < ${sliceDim}) {
                flattenedIndex += index.yw * ${strideString2};
              }
            }
            if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||
                flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {
              vec4 updVals = ${updatesSnippet};
              if (flattenedIndex[0] == coords[0]) {
                sum.xy += updVals.xy;
                found.xy = vec2(1.);
              } else if (flattenedIndex[0] == coords[0] + 1) {
                sum.zw += updVals.xy;
                found.zw = vec2(1.);
              }
              if (flattenedIndex[1] == coords[0]) {
                sum.xy += updVals.zw;
                found.xy = vec2(1.);
              } else if (flattenedIndex[1] == coords[0] + 1) {
                sum.zw += updVals.zw;
                found.zw = vec2(1.);
              }
            }
          }
          setOutput(mix(${defaultValueSnippet}, sum, found));
        }
      `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kyGeI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "searchSorted", ()=>searchSorted);
parcelHelpers.export(exports, "searchSortedConfig", ()=>searchSortedConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _searchSortedGpu = require("../search_sorted_gpu");
function searchSorted(args) {
    const { inputs, backend, attrs } = args;
    const { sortedSequence, values } = inputs;
    const { side } = attrs;
    const program = new (0, _searchSortedGpu.SearchSortedProgram)(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
    const customValues = [
        [
            sortedSequence.shape[1]
        ]
    ];
    return backend.runWebGLProgram(program, [
        sortedSequence,
        values
    ], "int32", customValues);
}
const searchSortedConfig = {
    kernelName: (0, _tfjsCore.SearchSorted),
    backendName: "webgl",
    kernelFunc: searchSorted
};

},{"@tensorflow/tfjs-core":"21Ftl","../search_sorted_gpu":"1IKeg","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1IKeg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SearchSortedProgram", ()=>SearchSortedProgram);
var _tfjsCore = require("@tensorflow/tfjs-core");
class SearchSortedProgram {
    constructor(batchSize, numInputs, numValues, side){
        this.variableNames = [
            "sortedSequence",
            "values"
        ];
        this.customUniforms = [
            {
                name: "numInputs",
                type: "int"
            }
        ];
        this.outputShape = [
            batchSize,
            numValues
        ];
        const webGL2LoopHead = "while (left < right) {";
        // WebGL1 doesn't accept non constant loop conditions, so upper bound loop
        // iterations.
        const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;
        const loopHead = (0, _tfjsCore.env)().getNumber("WEBGL_VERSION") === 2 ? webGL2LoopHead : webGL1LoopHead;
        // left corresponds to lower bound and right to upper bound.
        const boundComparator = side === "left" ? "<" : "<=";
        this.userCode = `
       int findBound(int batch, float value) {
         int left = 0;
         int right = numInputs;
         int mid;
         ${loopHead}
           mid = (left + right) / 2;
           if (getSortedSequence(batch, mid) ${boundComparator} value) {
             left = mid + 1;
           } else {
             right = mid;
           }
         }
         return right;
       }

       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int valueIndex = coords[1];

         float value = getValues(batch, valueIndex);

         setOutput(float(findBound(batch, value)));
       }
     `;
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b6rGf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "select", ()=>select);
parcelHelpers.export(exports, "selectConfig", ()=>selectConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _selectGpu = require("../select_gpu");
function select(args) {
    const { inputs, backend } = args;
    const { condition, t, e } = inputs;
    const program = new (0, _selectGpu.SelectProgram)(condition.shape.length, t.shape, t.shape.length);
    return backend.runWebGLProgram(program, [
        condition,
        t,
        e
    ], (0, _tfjsCore.upcastType)(t.dtype, e.dtype));
}
const selectConfig = {
    kernelName: (0, _tfjsCore.Select),
    backendName: "webgl",
    kernelFunc: select
};

},{"@tensorflow/tfjs-core":"21Ftl","../select_gpu":"apyaJ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"apyaJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SelectProgram", ()=>SelectProgram);
var _shaderCompiler = require("./shader_compiler");
class SelectProgram {
    constructor(cRank, shape, rank){
        this.variableNames = [
            "c",
            "a",
            "b"
        ];
        this.outputShape = shape;
        let cCoords;
        let abCoords;
        if (rank > 4) throw Error(`Where for rank ${rank} is not yet supported`);
        if (rank === 1) {
            abCoords = `resRC`;
            cCoords = `resRC`;
        } else {
            const currentCoords = [
                "resRC.x",
                "resRC.y",
                "resRC.z",
                "resRC.w"
            ];
            const cCoordVars = [];
            const abCoordVars = [];
            for(let i = 0; i < shape.length; i++){
                abCoordVars.push(`${currentCoords[i]}`);
                if (i < cRank) cCoordVars.push(`${currentCoords[i]}`);
            }
            cCoords = cCoordVars.join();
            abCoords = abCoordVars.join();
        }
        const dtype = (0, _shaderCompiler.getCoordsDataType)(rank);
        this.userCode = `
      void main() {
        ${dtype} resRC = getOutputCoords();
        float cVal = getC(${cCoords});
        if (cVal >= 1.0) {
          setOutput(getA(${abCoords}));
        } else {
          setOutput(getB(${abCoords}));
        }
      }
    `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1HlBg":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "selu", ()=>selu);
parcelHelpers.export(exports, "seluConfig", ()=>seluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const SELU = `
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${(0, _tfjsCore.backend_util).SELU_SCALEALPHA};
  float scale = ${(0, _tfjsCore.backend_util).SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
const selu = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SELU
});
const seluConfig = {
    kernelName: (0, _tfjsCore.Selu),
    backendName: "webgl",
    kernelFunc: selu
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c3gWE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sigmoid", ()=>sigmoid);
parcelHelpers.export(exports, "sigmoidConfig", ()=>sigmoidConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const SIGMOID = (0, _kernelFuncsUtils.CHECK_NAN_SNIPPET_UNARY) + `
  return 1.0 / (1.0 + exp(-1.0 * x));
`;
const SIGMOID_PACKED = `
  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
  bvec4 isNaN = isnan(x);

  result.r = isNaN.r ? x.r : result.r;
  result.g = isNaN.g ? x.g : result.g;
  result.b = isNaN.b ? x.b : result.b;
  result.a = isNaN.a ? x.a : result.a;

  return result;
`;
const sigmoid = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SIGMOID,
    packedOpSnippet: SIGMOID_PACKED,
    cpuKernelImpl: (0, _shared.sigmoidImplCPU)
});
const sigmoidConfig = {
    kernelName: (0, _tfjsCore.Sigmoid),
    backendName: "webgl",
    kernelFunc: sigmoid
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"83hlB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sign", ()=>sign);
parcelHelpers.export(exports, "signConfig", ()=>signConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
// Sign does not propagate NANs.
const SIGN = `
  if (isnan(x)) { return 0.0; }
  return sign(x);
`;
const sign = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SIGN
});
const signConfig = {
    kernelName: (0, _tfjsCore.Sign),
    backendName: "webgl",
    kernelFunc: sign
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6Fn44":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sin", ()=>sin);
parcelHelpers.export(exports, "sinConfig", ()=>sinConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryopPackedGpu = require("../binaryop_packed_gpu");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const SIN = (0, _kernelFuncsUtils.CHECK_NAN_SNIPPET_UNARY) + `
  return sin(x);
`;
const SIN_PACKED = `
  vec4 result = sin(x);
  bvec4 isNaN = isnan(x);
  ${(0, _binaryopPackedGpu.CHECK_NAN_SNIPPET_PACKED)}
  return result;
`;
const sin = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SIN,
    packedOpSnippet: SIN_PACKED
});
const sinConfig = {
    kernelName: (0, _tfjsCore.Sin),
    backendName: "webgl",
    kernelFunc: sin
};

},{"@tensorflow/tfjs-core":"21Ftl","../binaryop_packed_gpu":"hkzp7","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9tvOm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sinh", ()=>sinh);
parcelHelpers.export(exports, "sinhConfig", ()=>sinhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const SINH = `
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`;
const sinh = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SINH
});
const sinhConfig = {
    kernelName: (0, _tfjsCore.Sinh),
    backendName: "webgl",
    kernelFunc: sinh
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1YmQs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "softplus", ()=>softplus);
parcelHelpers.export(exports, "softplusConfig", ()=>softplusConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const SOFTPLUS = `
  float epsilon = 1.1920928955078125e-7;
  float threshold = log(epsilon) + 2.0;

  bool too_large = x > -threshold;
  bool too_small = x < threshold;

  float result;
  float exp_x = exp(x);

  if (too_large){
    result = x;
  }
  else if (too_small){
    result = exp_x;
  }
  else{
    result = log(exp_x + 1.0);
  }
  return result;
`;
const softplus = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SOFTPLUS
});
const softplusConfig = {
    kernelName: (0, _tfjsCore.Softplus),
    backendName: "webgl",
    kernelFunc: softplus
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"58w9T":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "spaceToBatchND", ()=>spaceToBatchND);
parcelHelpers.export(exports, "spaceToBatchNDConfig", ()=>spaceToBatchNDConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _padV2 = require("./PadV2");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
const spaceToBatchND = (args)=>{
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { blockShape, paddings } = attrs;
    (0, _tfjsCore.util).assert(x.shape.length <= 4, ()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
    const prod = blockShape.reduce((a, b)=>a * b);
    const completePaddings = [
        [
            0,
            0
        ]
    ];
    completePaddings.push(...paddings);
    for(let i = 1 + blockShape.length; i < x.shape.length; ++i)completePaddings.push([
        0,
        0
    ]);
    const toDispose = [];
    const paddedX = (0, _padV2.padV2)({
        inputs: {
            x
        },
        backend,
        attrs: {
            paddings: completePaddings,
            constantValue: 0
        }
    });
    const reshapedPaddedShape = (0, _tfjsCore.backend_util).getReshaped(paddedX.shape, blockShape, prod, false);
    const permutedReshapedPaddedPermutation = (0, _tfjsCore.backend_util).getPermuted(reshapedPaddedShape.length, blockShape.length, false);
    const flattenShape = (0, _tfjsCore.backend_util).getReshapedPermuted(paddedX.shape, blockShape, prod, false);
    const reshapedPaddedX = (0, _reshape.reshape)({
        inputs: {
            x: paddedX
        },
        backend,
        attrs: {
            shape: reshapedPaddedShape
        }
    });
    const paddedXT = (0, _transpose.transpose)({
        inputs: {
            x: reshapedPaddedX
        },
        backend,
        attrs: {
            perm: permutedReshapedPaddedPermutation
        }
    });
    const result = (0, _reshape.reshape)({
        inputs: {
            x: paddedXT
        },
        backend,
        attrs: {
            shape: flattenShape
        }
    });
    toDispose.push(paddedX);
    toDispose.push(reshapedPaddedX);
    toDispose.push(paddedXT);
    toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return result;
};
const spaceToBatchNDConfig = {
    kernelName: (0, _tfjsCore.SpaceToBatchND),
    backendName: "webgl",
    kernelFunc: spaceToBatchND
};

},{"@tensorflow/tfjs-core":"21Ftl","./PadV2":"bOlXa","./Reshape":"fgZen","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iVlEr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseFillEmptyRows", ()=>sparseFillEmptyRows);
parcelHelpers.export(exports, "sparseFillEmptyRowsConfig", ()=>sparseFillEmptyRowsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function sparseFillEmptyRows(args) {
    const { inputs, backend } = args;
    const { indices, values, denseShape, defaultValue } = inputs;
    if (denseShape.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw:
         ${denseShape.shape}`);
    if (indices.shape.length !== 2) throw new Error(`Indices must be a matrix, saw:
         ${indices.shape}`);
    if (values.shape.length !== 1) throw new Error(`Values must be a vector, saw:
         ${values.shape}`);
    if (defaultValue.shape.length !== 0) throw new Error(`Default value must be a scalar, saw:
        ${defaultValue.shape}`);
    const $indices = backend.readSync(indices.dataId);
    const $values = backend.readSync(values.dataId);
    const $denseShape = backend.readSync(denseShape.dataId);
    const $defaultValue = backend.readSync(defaultValue.dataId)[0];
    const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = (0, _shared.sparseFillEmptyRowsImplCPU)($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
    return [
        backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
        backend.makeTensorInfo([
            outputIndicesShape[0]
        ], values.dtype, outputValues),
        backend.makeTensorInfo([
            emptyRowIndicator.length
        ], "bool", new Uint8Array(emptyRowIndicator.map((value)=>Number(value)))),
        backend.makeTensorInfo([
            reverseIndexMap.length
        ], indices.dtype, new Int32Array(reverseIndexMap))
    ];
}
const sparseFillEmptyRowsConfig = {
    kernelName: (0, _tfjsCore.SparseFillEmptyRows),
    backendName: "webgl",
    kernelFunc: sparseFillEmptyRows
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"arBQ7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseReshape", ()=>sparseReshape);
parcelHelpers.export(exports, "sparseReshapeConfig", ()=>sparseReshapeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function sparseReshape(args) {
    const { inputs, backend } = args;
    const { inputIndices, inputShape, newShape } = inputs;
    if (inputIndices.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);
    if (inputShape.shape.length !== 1) throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);
    if (newShape.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
    const $inputShape = Array.from(backend.readSync(inputShape.dataId));
    const $inputIndices = backend.readSync(inputIndices.dataId);
    const targetShape = Array.from(backend.readSync(newShape.dataId));
    const [newIndices, indicesShape, outputShape] = (0, _shared.sparseReshapeImplCPU)($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
    return [
        backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
        backend.makeTensorInfo([
            outputShape.length
        ], newShape.dtype, new Int32Array(outputShape))
    ];
}
const sparseReshapeConfig = {
    kernelName: (0, _tfjsCore.SparseReshape),
    backendName: "webgl",
    kernelFunc: sparseReshape
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gQmRe":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseSegmentMean", ()=>sparseSegmentMean);
parcelHelpers.export(exports, "sparseSegmentMeanConfig", ()=>sparseSegmentMeanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function sparseSegmentMean(args) {
    const { inputs, backend } = args;
    const { data, indices, segmentIds } = inputs;
    if (data.shape.length < 1) throw new Error(`Data should be at least 1 dimensional but received scalar`);
    if (indices.shape.length !== 1) throw new Error(`Indices should be a vector but received shape
              ${indices.shape}`);
    if (segmentIds.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape
              ${segmentIds.shape}`);
    const $data = backend.readSync(data.dataId);
    const $indices = backend.readSync(indices.dataId);
    const $segmentIds = backend.readSync(segmentIds.dataId);
    const [outputData, outputDataShape] = (0, _shared.sparseSegmentReductionImplCPU)($data, data.shape, data.dtype, $indices, $segmentIds, true);
    return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
const sparseSegmentMeanConfig = {
    kernelName: (0, _tfjsCore.SparseSegmentMean),
    backendName: "webgl",
    kernelFunc: sparseSegmentMean
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2zeh8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseSegmentSum", ()=>sparseSegmentSum);
parcelHelpers.export(exports, "sparseSegmentSumConfig", ()=>sparseSegmentSumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function sparseSegmentSum(args) {
    const { inputs, backend } = args;
    const { data, indices, segmentIds } = inputs;
    if (data.shape.length < 1) throw new Error(`Data should be at least 1 dimensional but received scalar`);
    if (indices.shape.length !== 1) throw new Error(`Indices should be a vector but received shape
             ${indices.shape}`);
    if (segmentIds.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape
             ${segmentIds.shape}`);
    const $data = backend.readSync(data.dataId);
    const $indices = backend.readSync(indices.dataId);
    const $segmentIds = backend.readSync(segmentIds.dataId);
    const [outputData, outputDataShape] = (0, _shared.sparseSegmentReductionImplCPU)($data, data.shape, data.dtype, $indices, $segmentIds);
    return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
const sparseSegmentSumConfig = {
    kernelName: (0, _tfjsCore.SparseSegmentSum),
    backendName: "webgl",
    kernelFunc: sparseSegmentSum
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2SjVb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseToDense", ()=>sparseToDense);
parcelHelpers.export(exports, "sparseToDenseConfig", ()=>sparseToDenseConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _scatterGpu = require("../scatter_gpu");
var _reshape = require("./Reshape");
function sparseToDense(args) {
    const { inputs, backend, attrs } = args;
    const { sparseIndices, sparseValues, defaultValue } = inputs;
    const { outputShape } = attrs;
    const { sliceRank, numUpdates, sliceSize, strides, outputSize } = (0, _tfjsCore.backend_util).calculateShapes(sparseValues, sparseIndices, outputShape);
    const sumDupeIndices = false;
    if (sparseValues.dtype === "string") {
        const indicesBuf = backend.bufferSync(sparseIndices);
        const updatesBuf = backend.bufferSync(sparseValues);
        const $defaultValue = (0, _tfjsCore.util).decodeString(backend.readSync(defaultValue.dataId)[0]);
        const outBuf = (0, _shared.scatterImplCPU)(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
        return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
    }
    const program = new (0, _scatterGpu.ScatterProgram)(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [
        outputSize,
        1
    ], sumDupeIndices);
    const res = backend.runWebGLProgram(program, [
        sparseValues,
        sparseIndices,
        defaultValue
    ], sparseValues.dtype);
    const reshaped = (0, _reshape.reshape)({
        inputs: {
            x: res
        },
        backend,
        attrs: {
            shape: outputShape
        }
    });
    backend.disposeIntermediateTensorInfo(res);
    return reshaped;
}
const sparseToDenseConfig = {
    kernelName: (0, _tfjsCore.SparseToDense),
    backendName: "webgl",
    kernelFunc: sparseToDense
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../scatter_gpu":"kN25X","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6cXfl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "splitV", ()=>splitV);
parcelHelpers.export(exports, "splitVConfig", ()=>splitVConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _slice = require("./Slice");
function splitV(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { numOrSizeSplits, axis } = attrs;
    const $axis = (0, _tfjsCore.util).parseAxisParam(axis, x.shape)[0];
    const splitSizes = (0, _tfjsCore.backend_util).prepareSplitSize(x, numOrSizeSplits, $axis);
    const xRank = x.shape.length;
    const begin = new Array(xRank).fill(0);
    const size = x.shape.slice();
    return splitSizes.map((s)=>{
        const sliceSize = [
            ...size
        ];
        sliceSize[$axis] = s;
        const sliceT = (0, _slice.slice)({
            inputs: {
                x
            },
            backend,
            attrs: {
                begin,
                size: sliceSize
            }
        });
        begin[$axis] += s;
        return sliceT;
    });
}
const splitVConfig = {
    kernelName: (0, _tfjsCore.SplitV),
    backendName: "webgl",
    kernelFunc: splitV
};

},{"@tensorflow/tfjs-core":"21Ftl","./Slice":"6jQ29","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4bT4M":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sqrt", ()=>sqrt);
parcelHelpers.export(exports, "sqrtConfig", ()=>sqrtConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
var _shared = require("../kernel_utils/shared");
const SQRT = `return sqrt(x);`;
const sqrt = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SQRT,
    packedOpSnippet: SQRT,
    cpuKernelImpl: (0, _shared.sqrtImplCPU)
});
const sqrtConfig = {
    kernelName: (0, _tfjsCore.Sqrt),
    backendName: "webgl",
    kernelFunc: sqrt
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jCuUm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "square", ()=>square);
parcelHelpers.export(exports, "squareConfig", ()=>squareConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const SQUARE = `return x * x;`;
const square = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: SQUARE
});
const squareConfig = {
    kernelName: (0, _tfjsCore.Square),
    backendName: "webgl",
    kernelFunc: square
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fSbIq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "squaredDifference", ()=>squaredDifference);
parcelHelpers.export(exports, "squaredDifferenceConfig", ()=>squaredDifferenceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const SQUARED_DIFFERENCE = "return (a - b) * (a - b);";
const squaredDifference = (0, _kernelFuncsUtils.binaryKernelFunc)({
    opSnippet: SQUARED_DIFFERENCE,
    packedOpSnippet: SQUARED_DIFFERENCE
});
const squaredDifferenceConfig = {
    kernelName: (0, _tfjsCore.SquaredDifference),
    backendName: "webgl",
    kernelFunc: squaredDifference
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8HWNl":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "staticRegexReplace", ()=>staticRegexReplace);
parcelHelpers.export(exports, "staticRegexReplaceConfig", ()=>staticRegexReplaceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function staticRegexReplace(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    if (x.dtype !== "string") throw new Error("Input must be of datatype string");
    const $x = backend.readSync(x.dataId);
    const stringInput = (0, _tfjsCore.backend_util).fromUint8ToStringArray($x);
    const output = (0, _shared.staticRegexReplaceImplCPU)(stringInput, "string", attrs);
    return backend.makeTensorInfo(x.shape, "string", output);
}
const staticRegexReplaceConfig = {
    kernelName: (0, _tfjsCore.StaticRegexReplace),
    backendName: "webgl",
    kernelFunc: staticRegexReplace
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d3n9V":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "step", ()=>step);
parcelHelpers.export(exports, "stepConfig", ()=>stepConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryopGpu = require("../unaryop_gpu");
function step({ inputs, attrs, backend }) {
    const { x } = inputs;
    const opSnippet = (0, _unaryopGpu.CHECK_NAN_SNIPPET) + `
    return x > 0.0 ? 1.0 : float(${attrs.alpha});
  `;
    const program = new (0, _unaryopGpu.UnaryOpProgram)(x.shape, opSnippet);
    return backend.runWebGLProgram(program, [
        x
    ], x.dtype);
}
const stepConfig = {
    kernelName: (0, _tfjsCore.Step),
    backendName: "webgl",
    kernelFunc: step
};

},{"@tensorflow/tfjs-core":"21Ftl","../unaryop_gpu":"gIMrR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"47wD9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stridedSlice", ()=>stridedSlice);
parcelHelpers.export(exports, "stridedSliceConfig", ()=>stridedSliceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _stridedSliceGpu = require("../strided_slice_gpu");
var _reshape = require("./Reshape");
var _slice = require("./Slice");
function stridedSlice(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
    const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = (0, _tfjsCore.slice_util).sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
    let result;
    if (isIdentity) // Optimization #1, slice is a no-op plus reshape
    result = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: finalShape
        }
    });
    else if (sliceDim0 || isSimpleSlice) {
        // Optimization #2, slice is memory contiguous (only occurs in dim 0)
        (0, _tfjsCore.util).assert(x.shape.length >= 1, ()=>`Input must have rank at least 1, got: ${x.shape.length}`);
        const size = (0, _tfjsCore.slice_util).computeOutShape($begin, $end, $strides);
        // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
        const sliced = (0, _slice.slice)({
            inputs: {
                x
            },
            backend,
            attrs: {
                begin: $begin,
                size
            }
        });
        result = (0, _reshape.reshape)({
            inputs: {
                x: sliced
            },
            backend,
            attrs: {
                shape: finalShape
            }
        });
        backend.disposeIntermediateTensorInfo(sliced);
    } else {
        const shouldExecuteOnCPU = backend.shouldExecuteOnCPU([
            x
        ]);
        if (shouldExecuteOnCPU) {
            // tslint:disable-next-line: no-unnecessary-type-assertion
            const values = backend.readSync(x.dataId);
            // tslint:disable-next-line: no-unnecessary-type-assertion
            const xBuf = (0, _tfjsCore.buffer)(x.shape, x.dtype, values);
            const resultValues = (0, _shared.stridedSliceImplCPU)(finalShapeSparse, xBuf, $strides, $begin);
            result = backend.makeTensorInfo(finalShape, x.dtype, resultValues.values);
        } else {
            const program = new (0, _stridedSliceGpu.StridedSliceProgram)($begin, $strides, finalShapeSparse);
            result = backend.runWebGLProgram(program, [
                x
            ], x.dtype);
        }
    }
    const resultReshaped = (0, _reshape.reshape)({
        inputs: {
            x: result
        },
        backend,
        attrs: {
            shape: finalShape
        }
    });
    backend.disposeIntermediateTensorInfo(result);
    return resultReshaped;
}
const stridedSliceConfig = {
    kernelName: (0, _tfjsCore.StridedSlice),
    backendName: "webgl",
    kernelFunc: stridedSlice
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../strided_slice_gpu":"2eIE7","./Reshape":"fgZen","./Slice":"6jQ29","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2eIE7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "StridedSliceProgram", ()=>StridedSliceProgram);
var _shaderCompiler = require("./shader_compiler");
class StridedSliceProgram {
    constructor(begin, strides, size){
        this.variableNames = [
            "x"
        ];
        this.outputShape = size;
        const rank = size.length;
        const inputDtype = (0, _shaderCompiler.getCoordsDataType)(size.length);
        const dtype = (0, _shaderCompiler.getCoordsDataType)(size.length);
        let newCoords = "";
        if (rank === 1) newCoords = "coords * strides + begin";
        else {
            let outputAxis = 0;
            newCoords = size.map((_, i)=>{
                outputAxis++;
                return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;
            }).join(",");
        }
        this.userCode = `
      ${inputDtype} begin = ${inputDtype}(${begin});
      ${inputDtype} strides = ${inputDtype}(${strides});

      void main() {
        ${dtype} coords = getOutputCoords();
        setOutput(getX(${newCoords}));
      }
    `;
    }
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aUZOK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringNGrams", ()=>stringNGrams);
parcelHelpers.export(exports, "stringNGramsConfig", ()=>stringNGramsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function stringNGrams(args) {
    const { inputs, backend, attrs } = args;
    const { separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences } = attrs;
    const { data, dataSplits } = inputs;
    const $data = backend.readSync(data.dataId);
    const $dataSplits = backend.readSync(dataSplits.dataId);
    const [nGrams, nGramsSplits] = (0, _shared.stringNGramsImplCPU)($data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences);
    return [
        backend.makeTensorInfo([
            nGrams.length
        ], "string", nGrams),
        backend.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
    ];
}
const stringNGramsConfig = {
    kernelName: (0, _tfjsCore.StringNGrams),
    backendName: "webgl",
    kernelFunc: stringNGrams
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ezwh5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringSplit", ()=>stringSplit);
parcelHelpers.export(exports, "stringSplitConfig", ()=>stringSplitConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function stringSplit(args) {
    const { inputs, backend, attrs } = args;
    const { skipEmpty } = attrs;
    const { input, delimiter } = inputs;
    if (input.dtype !== "string") throw new Error("Input must be of datatype string");
    if (input.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${input.shape}`);
    if (delimiter.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
    const $input = backend.readSync(input.dataId);
    const $delimiter = backend.readSync(delimiter.dataId)[0];
    const [indices, values, shape] = (0, _shared.stringSplitImplCPU)($input, $delimiter, skipEmpty);
    const outputSize = values.length;
    return [
        backend.makeTensorInfo([
            outputSize,
            2
        ], "int32", indices),
        backend.makeTensorInfo([
            outputSize
        ], "string", values),
        backend.makeTensorInfo([
            2
        ], "int32", new Int32Array(shape))
    ];
}
const stringSplitConfig = {
    kernelName: (0, _tfjsCore.StringSplit),
    backendName: "webgl",
    kernelFunc: stringSplit
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b6pUo":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringToHashBucketFast", ()=>stringToHashBucketFast);
parcelHelpers.export(exports, "stringToHashBucketFastConfig", ()=>stringToHashBucketFastConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
function stringToHashBucketFast(args) {
    const { inputs, backend, attrs } = args;
    const { numBuckets } = attrs;
    const { input } = inputs;
    if (input.dtype !== "string") throw new Error("Input must be of datatype string");
    if (numBuckets <= 0) throw new Error(`Number of buckets must be at least 1`);
    const $input = backend.readSync(input.dataId);
    const output = (0, _shared.stringToHashBucketFastImplCPU)($input, numBuckets);
    return backend.makeTensorInfo(input.shape, "int32", output);
}
const stringToHashBucketFastConfig = {
    kernelName: (0, _tfjsCore.StringToHashBucketFast),
    backendName: "webgl",
    kernelFunc: stringToHashBucketFast
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2vzWF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tan", ()=>tan);
parcelHelpers.export(exports, "tanConfig", ()=>tanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const TAN = `return tan(x);`;
const tan = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: TAN
});
const tanConfig = {
    kernelName: (0, _tfjsCore.Tan),
    backendName: "webgl",
    kernelFunc: tan
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7BFBf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tanh", ()=>tanh);
parcelHelpers.export(exports, "tanhConfig", ()=>tanhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _kernelFuncsUtils = require("../kernel_utils/kernel_funcs_utils");
const TANH = `
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
const tanh = (0, _kernelFuncsUtils.unaryKernelFunc)({
    opSnippet: TANH
});
const tanhConfig = {
    kernelName: (0, _tfjsCore.Tanh),
    backendName: "webgl",
    kernelFunc: tanh
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/kernel_funcs_utils":"grPtN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2uPVJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tensorScatterUpdate", ()=>tensorScatterUpdate);
parcelHelpers.export(exports, "tensorScatterUpdateConfig", ()=>tensorScatterUpdateConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _scatterGpu = require("../scatter_gpu");
var _reshape = require("./Reshape");
function tensorScatterUpdate(args) {
    const { inputs, backend, attrs } = args;
    const { tensor, indices, updates } = inputs;
    const {} = attrs;
    const { sliceRank, numUpdates, sliceSize, strides, outputSize } = (0, _tfjsCore.backend_util).calculateShapes(updates, indices, tensor.shape);
    const flattenShape = [
        outputSize / sliceSize,
        sliceSize
    ];
    if (outputSize === 0) return backend.makeTensorInfo(tensor.shape, indices.dtype);
    const flattenIndices = (0, _reshape.reshape)({
        inputs: {
            x: indices
        },
        backend,
        attrs: {
            shape: [
                numUpdates,
                sliceRank
            ]
        }
    });
    const flattenX = (0, _reshape.reshape)({
        inputs: {
            x: updates
        },
        backend,
        attrs: {
            shape: [
                numUpdates,
                sliceSize
            ]
        }
    });
    const flattenTensor = (0, _reshape.reshape)({
        inputs: {
            x: tensor
        },
        backend,
        attrs: {
            shape: flattenShape
        }
    });
    const program = new (0, _scatterGpu.ScatterProgram)(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape, false, true);
    const res = backend.runWebGLProgram(program, [
        flattenX,
        flattenIndices,
        flattenTensor
    ], flattenTensor.dtype);
    const reshaped = (0, _reshape.reshape)({
        inputs: {
            x: res
        },
        backend,
        attrs: {
            shape: tensor.shape
        }
    });
    backend.disposeIntermediateTensorInfo(flattenIndices);
    backend.disposeIntermediateTensorInfo(flattenX);
    backend.disposeIntermediateTensorInfo(flattenTensor);
    backend.disposeIntermediateTensorInfo(res);
    return reshaped;
}
const tensorScatterUpdateConfig = {
    kernelName: (0, _tfjsCore.TensorScatterUpdate),
    backendName: "webgl",
    kernelFunc: tensorScatterUpdate
};

},{"@tensorflow/tfjs-core":"21Ftl","../scatter_gpu":"kN25X","./Reshape":"fgZen","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dbDLN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tile", ()=>tile);
parcelHelpers.export(exports, "tileConfig", ()=>tileConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _tileGpu = require("../tile_gpu");
function tile(params) {
    const { inputs, backend, attrs } = params;
    const { x } = inputs;
    const { reps } = attrs;
    // tile gpu program cannot handle rank > 5 case.
    if (x.dtype === "string" || x.shape.length > 5) {
        // Even thought string tensor is always on CPU, just to be consistent on how
        // to access tensor data.
        const data = backend.readSync(x.dataId);
        const value = x.dtype === "string" ? data.map((d)=>(0, _tfjsCore.util).decodeString(d)) : data;
        const buf = (0, _tfjsCore.buffer)(x.shape, x.dtype, value);
        const outBuf = (0, _shared.tileImplCPU)(buf, reps);
        return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
    }
    const program = new (0, _tileGpu.TileProgram)(x.shape, reps);
    const output = backend.runWebGLProgram(program, [
        x
    ], x.dtype);
    return output;
}
const tileConfig = {
    kernelName: (0, _tfjsCore.Tile),
    backendName: "webgl",
    kernelFunc: tile
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../tile_gpu":"kUfPd","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kUfPd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "TileProgram", ()=>TileProgram);
var _shaderCompiler = require("./shader_compiler");
class TileProgram {
    constructor(aShape, reps){
        this.variableNames = [
            "A"
        ];
        const outputShape = new Array(aShape.length);
        for(let i = 0; i < outputShape.length; i++)outputShape[i] = aShape[i] * reps[i];
        this.outputShape = outputShape;
        this.rank = outputShape.length;
        const dtype = (0, _shaderCompiler.getCoordsDataType)(this.rank);
        const sourceCoords = getSourceCoords(aShape);
        this.userCode = `
      void main() {
        ${dtype} resRC = getOutputCoords();
        setOutput(getA(${sourceCoords}));
      }
    `;
    }
}
function getSourceCoords(aShape) {
    const rank = aShape.length;
    if (rank > 5) throw Error(`Tile for rank ${rank} is not yet supported`);
    if (rank === 1) return `imod(resRC, ${aShape[0]})`;
    const currentCoords = [
        "resRC.x",
        "resRC.y",
        "resRC.z",
        "resRC.w",
        "resRC.u"
    ];
    const sourceCoords = [];
    for(let i = 0; i < aShape.length; i++)sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);
    return sourceCoords.join();
}

},{"./shader_compiler":"5uusa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gvorh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
// Based on Algorithm 2 of Bitonic Top K, ref:
// https://anilshanbhag.in/static/papers/gputopk_sigmod18.pdf
parcelHelpers.export(exports, "topK", ()=>topK);
parcelHelpers.export(exports, "topKConfig", ()=>topKConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _topKGpu = require("../top_k_gpu");
var _fill = require("./Fill");
var _gatherV2 = require("./GatherV2");
var _reshape = require("./Reshape");
var _slice = require("./Slice");
function disposeIntermediateTensorInfoOrNull(backend, tensorInfo) {
    if (tensorInfo !== null) backend.disposeIntermediateTensorInfo(tensorInfo);
}
function roundUpToPow2(num) {
    let pow2 = 1;
    while(pow2 < num)pow2 *= 2;
    return pow2;
}
function topK(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { k, sorted } = attrs;
    // Empirically determined constant used to determine last dim threshold for
    // handing off execution to the CPU.
    const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = (0, _tfjsCore.env)().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD");
    // Empirically determined constant used to determine k threshold for handing
    // off execution to the CPU.
    const TOPK_K_CPU_HANDOFF_THRESHOLD = (0, _tfjsCore.env)().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD");
    const xShape = x.shape;
    const lastDim = xShape[xShape.length - 1];
    if (backend.shouldExecuteOnCPU([
        x
    ]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {
        const xVals = backend.readSync(x.dataId);
        const [allTopKVals, allTopKIndices] = (0, _shared.topKImplCPU)(xVals, xShape, x.dtype, k, sorted);
        return [
            backend.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
            backend.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
        ];
    }
    if (k === 0) {
        xShape[xShape.length - 1] = 0;
        return [
            backend.makeTensorInfo(xShape, x.dtype, []),
            backend.makeTensorInfo(xShape, "int32", [])
        ];
    }
    if (lastDim === 1 /* firstPass */ ) return [
        x,
        (0, _fill.fill)({
            attrs: {
                shape: xShape,
                dtype: "int32",
                value: 0
            },
            backend
        })
    ];
    // Eagerly unpack x input since it is passed in to all the shaders which
    // require unpacked inputs.
    const xtexData = backend.texData.get(x.dataId);
    const xIsPacked = xtexData !== null && xtexData.isPacked;
    const xUnPacked = xIsPacked ? backend.unpackTensor(x) : x;
    // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.
    const xSize = (0, _tfjsCore.util).sizeFromShape(xShape);
    const batch = xSize / lastDim;
    const x2D = (0, _reshape.reshape)({
        inputs: {
            x: xUnPacked
        },
        attrs: {
            shape: [
                batch,
                lastDim
            ]
        },
        backend
    });
    if (xIsPacked) disposeIntermediateTensorInfoOrNull(backend, xUnPacked);
    const kPow2 = roundUpToPow2(k);
    const lastDimPow2 = roundUpToPow2(lastDim);
    // Only the indices containing the top K are kept at every step to reduce
    // number of outputs in the GPU algorithms, so once the final set of indices
    // is computed then gather is used to grab the corresponding values
    // from the original input.
    let indices = null;
    // GPU algorithm always takes in an indices input but this input is not used
    // on the first run of a GPU algorithm, therefore if indices is null we simply
    // pass in x2D instead of it but the value will not actually be used
    const getInputs = ()=>indices === null ? [
            x2D,
            x2D
        ] : [
            x2D,
            indices
        ];
    const runSwap = (dir, inc, shape)=>{
        const inputs = getInputs();
        const program = new (0, _topKGpu.SwapProgram)(shape);
        const fistPass = indices === null ? 1 : 0;
        const customValues = [
            [
                lastDim
            ],
            [
                fistPass
            ],
            [
                Number.NEGATIVE_INFINITY
            ],
            [
                dir
            ],
            [
                inc
            ]
        ];
        const prevIndices = indices;
        indices = backend.runWebGLProgram(program, inputs, "int32", customValues);
        disposeIntermediateTensorInfoOrNull(backend, prevIndices);
    };
    // Step 1: local sort
    for(let len = 1; len < kPow2; len *= 2){
        const dir = len * 2;
        for(let inc = len; inc >= 1; inc /= 2)runSwap(dir, inc, [
            batch,
            lastDimPow2
        ]);
    }
    // Step 2: merge
    for(let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2){
        const inputs = getInputs();
        const mergeProgram = new (0, _topKGpu.MergeProgram)([
            batch,
            indicesSize / 2
        ]);
        const firstPass = indices === null ? 1 : 0;
        const customValues = [
            [
                lastDim
            ],
            [
                firstPass
            ],
            [
                kPow2
            ]
        ];
        const prevIndices = indices;
        indices = backend.runWebGLProgram(mergeProgram, inputs, "int32", customValues);
        disposeIntermediateTensorInfoOrNull(backend, prevIndices);
        // Step 3: rebuild
        const len = kPow2 / 2;
        const dir = len * 2;
        for(let inc = len; inc >= 1; inc /= 2)runSwap(dir, inc, indices.shape);
    }
    // Keep only the requested top K results instead of kPow2
    let prevIndices = indices;
    indices = (0, _slice.slice)({
        inputs: {
            x: indices
        },
        backend,
        attrs: {
            begin: 0,
            size: [
                batch,
                k
            ]
        }
    });
    disposeIntermediateTensorInfoOrNull(backend, prevIndices);
    // Gather values on last dimension
    let values = (0, _gatherV2.gatherV2)({
        inputs: {
            x: x2D,
            indices
        },
        backend,
        attrs: {
            axis: 1,
            batchDims: 1
        }
    });
    disposeIntermediateTensorInfoOrNull(backend, x2D);
    // Reshape back to the original input shape, except that the last
    // dimension is k.
    const newShape = xShape.slice(0, -1);
    newShape.push(k);
    prevIndices = indices;
    indices = (0, _reshape.reshape)({
        inputs: {
            x: indices
        },
        attrs: {
            shape: newShape
        },
        backend
    });
    disposeIntermediateTensorInfoOrNull(backend, prevIndices);
    const prevValues = values;
    values = (0, _reshape.reshape)({
        inputs: {
            x: values
        },
        attrs: {
            shape: newShape
        },
        backend
    });
    disposeIntermediateTensorInfoOrNull(backend, prevValues);
    return [
        values,
        indices
    ];
}
const topKConfig = {
    kernelName: (0, _tfjsCore.TopK),
    backendName: "webgl",
    kernelFunc: topK
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../top_k_gpu":"fpm4p","./Fill":"6Rawr","./GatherV2":"cBFXe","./Reshape":"fgZen","./Slice":"6jQ29","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fpm4p":[function(require,module,exports) {
// Based on Algorithm 2 of Bitonic Top K, ref:
// https://anilshanbhag.in/static/papers/gputopk_sigmod18.pdf
// The original algorithm is based on computing the top K only, however
// since for TFJS we require the indices of the top K values as well then the
// algorithm found here is a bit modified. Rather than producing the values
// at each step, the indices containing the top K are generated instead.
// The output values are not generated to reduce the number of outputs in the
// GPU, the values can easily be retrieved from the indices using a gather
// op.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SwapProgram", ()=>SwapProgram);
parcelHelpers.export(exports, "MergeProgram", ()=>MergeProgram);
class SwapProgram {
    /**
     * @param shape desired output shape (can be larger than input shape, output
     *                                    will be padded with -Infinity)
     */ constructor(shape){
        this.variableNames = [
            "x",
            "indices"
        ];
        // |n| Size of the original input of TopK.
        // |firstPass|indicates if this is the first time swap is being used which
        // means no indices input containing the top K is present yet.
        // |inc| Swaps pairs of indices (0, inc), (1, inc + 1), (2, inc + 2) ...
        this.customUniforms = [
            {
                name: "n",
                type: "int"
            },
            {
                name: "firstPass",
                type: "int"
            },
            {
                name: "negativeInf",
                type: "float"
            },
            {
                name: "dir",
                type: "int"
            },
            {
                name: "inc",
                type: "int"
            }
        ];
        this.outputShape = shape;
        this.userCode = `
       void main() {
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // We compare elements pair-wise within a group of size 2 * inc.
         // The comparing rule for each group alternates between ascending
         // and descending. Within each group, we compare each pair at
         // positions i and i+inc. To decide whether an element at position i
         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than
         // inc, it is in the first half of the group, we denote it as x0,
         // otherwise we denote it as x1.
         // For example, as shown in the Bitonic top K paper referenced above,
         // Figure5(a) shows that element[1] is in the
         // second half of the group when group size is 2, but it is in the
         // first half of the group when group size is 4.

         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
         int i = isFirstInPair ? elemIdx : elemIdx - inc;

         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
         float x0 = i0 < n ? getX(batch, i0) : negativeInf;
         float x1 = i1 < n ? getX(batch, i1) : negativeInf;

         // Denotes which direction indices are in (ascending or descending).
         bool reverse = imod(elemIdx, 2 * dir) >= dir;
         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
         if (reverse == isGreater) { // Elements in opposite order of direction
           int iTemp = i0;
           i0 = i1;
           i1 = iTemp;
         }
         if (isFirstInPair) {
            setOutput(float(i0));
         } else {
            setOutput(float(i1));
         }
       }
     `;
    }
}
class MergeProgram {
    /**
     * @param shape desired output shape (must be half of the input size)
     */ constructor(shape){
        this.variableNames = [
            "x",
            "indices"
        ];
        // |n| Size of the original input of TopK
        // |firstPass| indicates if this is the first time swap is being used which
        // means no indices input containing the top K is present yet.
        // |k| Top k elements desired
        this.customUniforms = [
            {
                name: "n",
                type: "int"
            },
            {
                name: "firstPass",
                type: "int"
            },
            {
                name: "k",
                type: "int"
            }
        ];
        this.outputShape = shape;
        this.userCode = `
    void main() {
         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
         ivec2 coords = getOutputCoords();
         int batch = coords[0];
         int elemIdx = coords[1];

         // The output size is half of the previous size.
         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),
         // we only need to output the indices at positions |, the indices at
         // positions _ can be thrown away, see Figure5(b) After Phase 2
         // (Merge phase) in the Bitonic Top K paper referenced above.
         // For example, the paper shows we only need to output the orange bars.
         // The output sequence should look like this | | | | | | | |.
         // Because the sequence is halved, to map the output index back
         // to the previous sequence to find the corresponding value,
         // we need to double the index. When we double the index,
         // we basically interpolate a position, so 2i looks like
         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
         // of each 2k positions by - elemIdx % k. E.g. for output at
         // index 4,5,6,7, we want to get the corresponding element at
         // original index 8,9,10,11, for output at index 8,9,10,11,
         // we want to get the corresponding element at original index
         // 16,17,18,19, so on and so forth.

         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));

         float x0 = getX(batch, i0);
         float x1 = i1 < n ? getX(batch, i1) : x0;

         setOutput(x0 >= x1 ? float(i0) : float(i1));
       }
     `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4cCvF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transform", ()=>transform);
parcelHelpers.export(exports, "transformConfig", ()=>transformConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _transformGpu = require("../transform_gpu");
function transform(args) {
    const { inputs, backend, attrs } = args;
    const { image, transforms } = inputs;
    const { interpolation, fillMode, fillValue, outputShape } = attrs;
    const [batch, imageHeight, imageWidth, numChannels] = image.shape;
    const [outHeight, outWidth] = outputShape != null ? outputShape : [
        imageHeight,
        imageWidth
    ];
    const outShape = [
        batch,
        outHeight,
        outWidth,
        numChannels
    ];
    const program = new (0, _transformGpu.TransformProgram)(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);
    return backend.runWebGLProgram(program, [
        image,
        transforms
    ], "float32");
}
const transformConfig = {
    kernelName: (0, _tfjsCore.Transform),
    backendName: "webgl",
    kernelFunc: transform
};

},{"@tensorflow/tfjs-core":"21Ftl","../transform_gpu":"1WaA8","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1WaA8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "TransformProgram", ()=>TransformProgram);
class TransformProgram {
    constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape){
        this.variableNames = [
            "Image",
            "Transforms"
        ];
        this.outputShape = outShape;
        const interpolationModeId = interpolation === "nearest" ? 1 : 2;
        let fillModeId;
        switch(fillMode){
            case "constant":
                fillModeId = 1;
                break;
            case "reflect":
                fillModeId = 2;
                break;
            case "wrap":
                fillModeId = 3;
                break;
            case "nearest":
                fillModeId = 4;
                break;
            default:
                fillModeId = 1;
                break;
        }
        this.userCode = `
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${fillModeId} == 2) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    if (inCoord < sz2) {
                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +
                      inCoord;
                    }
                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz2 = 2.0 * len;
                    inCoord -= sz2 * float(int(float(inCoord / sz2)));
                    if (inCoord >= len) {
                      inCoord = sz2 - inCoord - 1.0;
                    }
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${fillModeId} == 3) {
                if (inCoord < 0.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
                  }
                } else if (inCoord > len - 1.0) {
                  if (len <= 1.0) {
                    inCoord = 0.0;
                  } else {
                    float sz = len - 1.0;
                    inCoord -= len * float(int(float(inCoord / sz)));
                  }
                }
                return clamp(inCoord, 0.0, len - 1.0);
              } else if (${fillModeId} == 4) {
                return clamp(outCoord, 0.0, len - 1.0);
              } else {
                return outCoord;
              }
            }

            float readWithFillValue(int batch, int coordY, int coordX,
              int channel) {
              float outputValue;
              if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {
                  outputValue = getImage(batch, coordY, coordX, channel);
              } else {
                outputValue = float(${fillValue});
              }
              return outputValue;
            }

            void main() {
              ivec4 coords = getOutputCoords();
              float outputValue;
              int batch = coords[0];
              int x = coords[2];
              int y = coords[1];
              int channel = coords[3];
              float xf = float(x);
              float yf = float(y);
              float a1 = getTransforms(batch, 0);
              float a2 = getTransforms(batch, 1);
              float a3 = getTransforms(batch, 2);
              float b1 = getTransforms(batch, 3);
              float b2 = getTransforms(batch, 4);
              float b3 = getTransforms(batch, 5);
              float c1 = getTransforms(batch, 6);
              float c2 = getTransforms(batch, 7);
              float projection = c1 * xf + c2 * yf + 1.0;
              if (projection == 0.0) {
                outputValue = float(${fillValue});
              } else {
                float inX = (a1 * xf + a2 * yf + a3) / projection;
                float inY = (b1 * xf + b2 * yf + b3) / projection;
                float mapX = mapCoord(inX, float(${imageWidth}));
                float mapY = mapCoord(inY, float(${imageHeight}));

                if (${interpolationModeId} == 1) {
                  int coordY = int(round(mapY));
                  int coordX = int(round(mapX));
                  outputValue = readWithFillValue(batch, coordY, coordX,
                    channel);
                } else {
                  float yFloor = floor(mapY);
                  float xFloor = floor(mapX);
                  float yCeil = yFloor + 1.0;
                  float xCeil = xFloor + 1.0;
                  float valueYFloor = (xCeil - mapX) *
                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);
                  float valueYCeil = (xCeil - mapX) *
                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
                  (mapX - xFloor) *
                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);
                  outputValue = (yCeil - mapY) * valueYFloor +
                  (mapY - yFloor) * valueYCeil;
                }
              }
              setOutput(outputValue);
            }
        `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"e7lqL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unique", ()=>unique);
parcelHelpers.export(exports, "uniqueConfig", ()=>uniqueConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _shared = require("../kernel_utils/shared");
var _webglUtil = require("../webgl_util");
function unique(args) {
    const { inputs, attrs, backend } = args;
    const { axis } = attrs;
    const { x } = inputs;
    (0, _webglUtil.assertNotComplex)(x, "unique");
    // For now, always forward calculation to the CPU backend.
    console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
    const values = backend.readSync(x.dataId);
    const { outputValues, outputShape, indices } = (0, _shared.uniqueImplCPU)(values, axis, x.shape, x.dtype);
    return [
        backend.makeTensorInfo(outputShape, x.dtype, outputValues),
        backend.makeTensorInfo([
            indices.length
        ], "int32", indices)
    ];
}
const uniqueConfig = {
    kernelName: (0, _tfjsCore.Unique),
    backendName: "webgl",
    kernelFunc: unique
};

},{"@tensorflow/tfjs-core":"21Ftl","../kernel_utils/shared":"e4M7v","../webgl_util":"fjf7t","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5jnF3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unpack", ()=>unpack);
parcelHelpers.export(exports, "unpackConfig", ()=>unpackConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reshape = require("./Reshape");
var _slice = require("./Slice");
function unpack(args) {
    const { inputs, backend, attrs } = args;
    const { value } = inputs;
    let { axis } = attrs;
    if (axis < 0) axis += value.shape.length;
    const x = value;
    const xRank = x.shape.length;
    const num = value.shape[axis];
    const outShape = new Array(xRank - 1);
    let outIndex = 0;
    for(let i = 0; i < xRank; i++)if (i !== axis) outShape[outIndex++] = x.shape[i];
    const toDispose = [];
    const begin = new Array(xRank).fill(0);
    const size = x.shape.slice();
    size[axis] = 1;
    const res = new Array(num);
    for(let i = 0; i < res.length; i++){
        begin[axis] = i;
        const sliced = (0, _slice.slice)({
            inputs: {
                x
            },
            backend,
            attrs: {
                begin,
                size
            }
        });
        const reshaped = (0, _reshape.reshape)({
            inputs: {
                x: sliced
            },
            backend,
            attrs: {
                shape: outShape
            }
        });
        res[i] = reshaped;
        toDispose.push(sliced);
    }
    toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return res;
}
const unpackConfig = {
    kernelName: (0, _tfjsCore.Unpack),
    backendName: "webgl",
    kernelFunc: unpack
};

},{"@tensorflow/tfjs-core":"21Ftl","./Reshape":"fgZen","./Slice":"6jQ29","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"vnA1C":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unsortedSegmentSum", ()=>unsortedSegmentSum);
parcelHelpers.export(exports, "unsortedSegmentSumConfig", ()=>unsortedSegmentSumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _segmentGpu = require("../segment_gpu");
var _range = require("./Range");
var _reshape = require("./Reshape");
var _tile = require("./Tile");
var _transpose = require("./Transpose");
function unsortedSegmentSum(args) {
    const { inputs, backend, attrs } = args;
    const { x, segmentIds } = inputs;
    const { numSegments } = attrs;
    const xRank = x.shape.length;
    const toDispose = [];
    let axis = 0;
    const permutation = (0, _tfjsCore.backend_util).getAxesPermutation([
        axis
    ], xRank);
    let permutedX = x;
    if (permutation != null) {
        permutedX = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutation
            }
        });
        toDispose.push(permutedX);
        axis = (0, _tfjsCore.backend_util).getInnerMostAxes(1, xRank)[0];
    }
    const outShape = (0, _tfjsCore.backend_util).segment_util.computeOutShape(permutedX.shape, axis, numSegments);
    const inSize = (0, _tfjsCore.util).sizeFromShape([
        permutedX.shape[axis]
    ]);
    const a2D = (0, _reshape.reshape)({
        inputs: {
            x: permutedX
        },
        backend,
        attrs: {
            shape: [
                -1,
                inSize
            ]
        }
    });
    toDispose.push(a2D);
    const outputDType = (0, _tfjsCore.sumOutType)(x.dtype);
    const segOpCompute = (x, segOpType, segmentIds, dtype, numSegments)=>{
        const batchSize = x.shape[0];
        const inSize = x.shape[1];
        const windowSize = (0, _tfjsCore.backend_util).segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);
        const segOpInfo = {
            windowSize,
            inSize,
            batchSize,
            numSegments
        };
        const program = new (0, _segmentGpu.SegmentOpProgram)(segOpInfo, segOpType);
        const output = backend.compileAndRun(program, [
            x,
            segmentIds
        ], dtype);
        toDispose.push(output);
        // No need to run another GPGPU program.
        if (output.shape[1] === numSegments) return output;
        const rangeInfo = (0, _range.range)({
            backend,
            attrs: {
                start: 0,
                stop: numSegments,
                step: 1,
                dtype: "float32"
            }
        });
        const tileInfo = (0, _tile.tile)({
            inputs: {
                x: rangeInfo
            },
            backend,
            attrs: {
                reps: [
                    inSize / windowSize
                ]
            }
        });
        toDispose.push(rangeInfo);
        toDispose.push(tileInfo);
        const result = segOpCompute(output, segOpType, tileInfo, dtype, numSegments);
        return result;
    };
    const segOpResult = segOpCompute(a2D, "unsortedSegmentSum", segmentIds, outputDType, numSegments);
    const reshaped = (0, _reshape.reshape)({
        inputs: {
            x: segOpResult
        },
        backend,
        attrs: {
            shape: outShape
        }
    });
    let result = reshaped;
    if (permutation != null) {
        toDispose.push(reshaped);
        const perm = (0, _tfjsCore.backend_util).getUndoAxesPermutation(permutation);
        result = (0, _transpose.transpose)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                perm
            }
        });
    }
    toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return result;
}
const unsortedSegmentSumConfig = {
    kernelName: (0, _tfjsCore.UnsortedSegmentSum),
    backendName: "webgl",
    kernelFunc: unsortedSegmentSum
};

},{"@tensorflow/tfjs-core":"21Ftl","../segment_gpu":"57Kv1","./Range":"4ulNZ","./Reshape":"fgZen","./Tile":"dbDLN","./Transpose":"cKJAC","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"57Kv1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SegmentOpProgram", ()=>SegmentOpProgram);
class SegmentOpProgram {
    constructor(segOpInfo, segOpType){
        this.variableNames = [
            "x",
            "segmentIds"
        ];
        const windowSize = segOpInfo.windowSize;
        const batchSize = segOpInfo.batchSize;
        const inSize = segOpInfo.inSize;
        const numSegments = segOpInfo.numSegments;
        const outSize = numSegments * Math.ceil(inSize / windowSize);
        this.outputShape = [
            batchSize,
            outSize
        ];
        const initializationValue = "0.0";
        const returnValue = `sumValue`;
        const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
        const windowSizeVec4Remainder = windowSize % 4;
        const updateSnippet = `
        sumValue += dot(values, segFilter);
    `;
        let checkValueOutOfBounds = "";
        if (inSize % windowSize > 0) checkValueOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return initializationValue;
        }
      `;
        let checkSegmentIdOutOfBounds = "";
        if (inSize % windowSize > 0) checkSegmentIdOutOfBounds = `
        if (inIdx < 0 || inIdx >= ${inSize}) {
          return -1.0;
        }
      `;
        this.userCode = `
      const float initializationValue = ${initializationValue};

      float getValue(int batch, int inIdx) {
        ${checkValueOutOfBounds}
        return getX(batch, inIdx);
      }

      float getSegmentIdAtIndex(int inIdx) {
        ${checkSegmentIdOutOfBounds}
        return getSegmentIds(inIdx);
      }

      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = int(floor(float(outIdx) / float(
          ${numSegments})) * float(${windowSize}));
        int currentSeg = int(mod(float(outIdx), float(${numSegments})));

        float sumValue = 0.0;

        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
          int inIdx = inOffset + i;
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            getValue(batch, inIdx + 3)
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
          );

          ${updateSnippet}
        }

        int inIdx = inOffset + ${windowSizeNearestVec4};
        if (${windowSizeVec4Remainder === 1}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            initializationValue,
            initializationValue,
            initializationValue
          );

          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            0,
            0,
            0
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 2}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            initializationValue,
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
              0,
              0
          );

          ${updateSnippet}
        } else if (${windowSizeVec4Remainder === 3}) {
          vec4 values = vec4(
            getValue(batch, inIdx),
            getValue(batch, inIdx + 1),
            getValue(batch, inIdx + 2),
            initializationValue
          );

          vec4 segFilter = vec4(
            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
            0
          );

          ${updateSnippet}
        }
        setOutput(${returnValue});
      }
    `;
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5k877":[function(require,module,exports) {
/**
    * @license
    * Copyright 2023 Google LLC. All Rights Reserved.
    * Licensed under the Apache License, Version 2.0 (the "License");
    * you may not use this file except in compliance with the License.
    * You may obtain a copy of the License at
    *
    * http://www.apache.org/licenses/LICENSE-2.0
    *
    * Unless required by applicable law or agreed to in writing, software
    * distributed under the License is distributed on an "AS IS" BASIS,
    * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    * See the License for the specific language governing permissions and
    * limitations under the License.
    * =============================================================================
    */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "SupportedModels", ()=>Se);
parcelHelpers.export(exports, "blurBodyPart", ()=>$e);
parcelHelpers.export(exports, "bodyPixMaskValueToRainbowColor", ()=>U);
parcelHelpers.export(exports, "createSegmenter", ()=>ke);
parcelHelpers.export(exports, "drawBokehEffect", ()=>Ye);
parcelHelpers.export(exports, "drawMask", ()=>Ne);
parcelHelpers.export(exports, "drawPixelatedMask", ()=>Qe);
parcelHelpers.export(exports, "toBinaryMask", ()=>Ue);
parcelHelpers.export(exports, "toColoredMask", ()=>qe);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _tfjsConverter = require("@tensorflow/tfjs-converter");
var _selfieSegmentation = require("@mediapipe/selfie_segmentation");
var R = function(t, e) {
    return R = Object.setPrototypeOf || ({
        __proto__: []
    }) instanceof Array && function(t, e) {
        t.__proto__ = e;
    } || function(t, e) {
        for(var n in e)e.hasOwnProperty(n) && (t[n] = e[n]);
    }, R(t, e);
};
function F(t, e) {
    function n() {
        this.constructor = t;
    }
    R(t, e), t.prototype = null === e ? Object.create(e) : (n.prototype = e.prototype, new n);
}
var C = function() {
    return C = Object.assign || function(t) {
        for(var e, n = 1, r = arguments.length; n < r; n++)for(var i in e = arguments[n])Object.prototype.hasOwnProperty.call(e, i) && (t[i] = e[i]);
        return t;
    }, C.apply(this, arguments);
};
function D(t, e, n, r) {
    return new (n || (n = Promise))(function(i, o) {
        function a(t) {
            try {
                u(r.next(t));
            } catch (t) {
                o(t);
            }
        }
        function s(t) {
            try {
                u(r.throw(t));
            } catch (t) {
                o(t);
            }
        }
        function u(t) {
            var e;
            t.done ? i(t.value) : (e = t.value, e instanceof n ? e : new n(function(t) {
                t(e);
            })).then(a, s);
        }
        u((r = r.apply(t, e || [])).next());
    });
}
function B(t, e) {
    var n, r, i, o, a = {
        label: 0,
        sent: function() {
            if (1 & i[0]) throw i[1];
            return i[1];
        },
        trys: [],
        ops: []
    };
    return o = {
        next: s(0),
        throw: s(1),
        return: s(2)
    }, "function" == typeof Symbol && (o[Symbol.iterator] = function() {
        return this;
    }), o;
    function s(o) {
        return function(s) {
            return function(o) {
                if (n) throw new TypeError("Generator is already executing.");
                for(; a;)try {
                    if (n = 1, r && (i = 2 & o[0] ? r.return : o[0] ? r.throw || ((i = r.return) && i.call(r), 0) : r.next) && !(i = i.call(r, o[1])).done) return i;
                    switch(r = 0, i && (o = [
                        2 & o[0],
                        i.value
                    ]), o[0]){
                        case 0:
                        case 1:
                            i = o;
                            break;
                        case 4:
                            return a.label++, {
                                value: o[1],
                                done: !1
                            };
                        case 5:
                            a.label++, r = o[1], o = [
                                0
                            ];
                            continue;
                        case 7:
                            o = a.ops.pop(), a.trys.pop();
                            continue;
                        default:
                            if (!(i = a.trys, (i = i.length > 0 && i[i.length - 1]) || 6 !== o[0] && 2 !== o[0])) {
                                a = 0;
                                continue;
                            }
                            if (3 === o[0] && (!i || o[1] > i[0] && o[1] < i[3])) {
                                a.label = o[1];
                                break;
                            }
                            if (6 === o[0] && a.label < i[1]) {
                                a.label = i[1], i = o;
                                break;
                            }
                            if (i && a.label < i[2]) {
                                a.label = i[2], a.ops.push(o);
                                break;
                            }
                            i[2] && a.ops.pop(), a.trys.pop();
                            continue;
                    }
                    o = e.call(t, a);
                } catch (t) {
                    o = [
                        6,
                        t
                    ], r = 0;
                } finally{
                    n = i = 0;
                }
                if (5 & o[0]) throw o[1];
                return {
                    value: o[0] ? o[1] : void 0,
                    done: !0
                };
            }([
                o,
                s
            ]);
        };
    }
}
function L(t) {
    return t instanceof SVGAnimatedLength ? t.baseVal.value : t;
}
function z(t) {
    return D(this, void 0, void 0, function() {
        var r, i;
        return B(this, function(o) {
            switch(o.label){
                case 0:
                    return r = document.createElement("canvas"), t instanceof (0, _tfjsCore.Tensor) ? [
                        4,
                        (0, _tfjsCore.browser).toPixels(t, r)
                    ] : [
                        3,
                        2
                    ];
                case 1:
                    return o.sent(), [
                        3,
                        3
                    ];
                case 2:
                    r.width = L(t.width), r.height = L(t.height), i = r.getContext("2d"), t instanceof ImageData ? i.putImageData(t, 0, 0) : i.drawImage(t, 0, 0), o.label = 3;
                case 3:
                    return [
                        2,
                        r
                    ];
            }
        });
    });
}
function W(t) {
    return D(this, void 0, void 0, function() {
        var r, i, o, a, s, u;
        return B(this, function(f) {
            switch(f.label){
                case 0:
                    return t instanceof (0, _tfjsCore.Tensor) ? (r = t.shape.slice(0, 2), i = r[0], o = r[1], a = ImageData.bind, [
                        4,
                        (0, _tfjsCore.browser).toPixels(t)
                    ]) : [
                        3,
                        2
                    ];
                case 1:
                    return [
                        2,
                        new (a.apply(ImageData, [
                            void 0,
                            f.sent(),
                            o,
                            i
                        ]))
                    ];
                case 2:
                    return s = document.createElement("canvas"), u = s.getContext("2d"), s.width = L(t.width), s.height = L(t.height), u.drawImage(t, 0, 0), [
                        2,
                        u.getImageData(0, 0, s.width, s.height)
                    ];
            }
        });
    });
}
function j(t) {
    return D(this, void 0, void 0, function() {
        var e, r;
        return B(this, function(i) {
            switch(i.label){
                case 0:
                    return t instanceof SVGImageElement || t instanceof OffscreenCanvas ? [
                        4,
                        z(t)
                    ] : [
                        3,
                        2
                    ];
                case 1:
                    return r = i.sent(), [
                        3,
                        3
                    ];
                case 2:
                    r = t, i.label = 3;
                case 3:
                    return e = r, [
                        2,
                        (0, _tfjsCore.browser).fromPixels(e, 4)
                    ];
            }
        });
    });
}
function V(t) {
    if (t < 0 || t >= 256) throw new Error("Mask value must be in range [0, 255] but got " + t);
    if (!Number.isInteger(t)) throw new Error("Mask value must be an integer but got " + t);
}
var K = [
    [
        110,
        64,
        170
    ],
    [
        143,
        61,
        178
    ],
    [
        178,
        60,
        178
    ],
    [
        210,
        62,
        167
    ],
    [
        238,
        67,
        149
    ],
    [
        255,
        78,
        125
    ],
    [
        255,
        94,
        99
    ],
    [
        255,
        115,
        75
    ],
    [
        255,
        140,
        56
    ],
    [
        239,
        167,
        47
    ],
    [
        217,
        194,
        49
    ],
    [
        194,
        219,
        64
    ],
    [
        175,
        240,
        91
    ],
    [
        135,
        245,
        87
    ],
    [
        96,
        247,
        96
    ],
    [
        64,
        243,
        115
    ],
    [
        40,
        234,
        141
    ],
    [
        28,
        219,
        169
    ],
    [
        26,
        199,
        194
    ],
    [
        33,
        176,
        213
    ],
    [
        47,
        150,
        224
    ],
    [
        65,
        125,
        224
    ],
    [
        84,
        101,
        214
    ],
    [
        99,
        81,
        195
    ]
];
function U(t) {
    if (V(t), t < K.length) {
        var e = K[t];
        return {
            r: e[0],
            g: e[1],
            b: e[2],
            a: 255
        };
    }
    throw new Error("Mask value must be in range [0, " + K.length + ")");
}
function q(t) {
    var e = t.shape[2], n = (0, _tfjsCore.argMax)(t, 2), r = (0, _tfjsCore.reshape)(n, [
        -1
    ]);
    return (0, _tfjsCore.oneHot)(r, e);
}
function N(t, e) {
    return (0, _tfjsCore.tidy)(function() {
        return (0, _tfjsCore.cast)((0, _tfjsCore.greater)(t, (0, _tfjsCore.scalar)(e)), "int32");
    });
}
function Q(t, e) {
    var n = e.shape, o = n[0], s = n[1], m = n[2];
    return (0, _tfjsCore.tidy)(function() {
        var n = q(e), r = (0, _tfjsCore.expandDims)((0, _tfjsCore.range)(0, m, 1, "int32"), 1), g = (0, _tfjsCore.cast)((0, _tfjsCore.matMul)(n, r), "int32"), v = (0, _tfjsCore.reshape)(g, [
            o,
            s
        ]), w = (0, _tfjsCore.add)(v, (0, _tfjsCore.scalar)(1, "int32"));
        return (0, _tfjsCore.sub)(function(t, e) {
            return (0, _tfjsCore.mul)(t, e);
        }(w, t), (0, _tfjsCore.scalar)(1, "int32"));
    });
}
var X = function() {
    function t(t, e) {
        this.model = t, this.outputStride = e;
        var n = this.model.inputs[0].shape;
        (0, _tfjsCore.util).assert(-1 === n[1] && -1 === n[2], function() {
            return "Input shape [" + n[1] + ", " + n[2] + "] must both be equal to or -1";
        });
    }
    return t.prototype.predict = function(t) {
        var e = this;
        return (0, _tfjsCore.tidy)(function() {
            var n = e.preprocessInput((0, _tfjsCore.cast)(t, "float32")), r = (0, _tfjsCore.expandDims)(n, 0), o = e.model.predict(r).map(function(t) {
                return (0, _tfjsCore.squeeze)(t, [
                    0
                ]);
            }), a = e.nameOutputResults(o);
            return {
                heatmapScores: (0, _tfjsCore.sigmoid)(a.heatmap),
                offsets: a.offsets,
                displacementFwd: a.displacementFwd,
                displacementBwd: a.displacementBwd,
                segmentation: a.segmentation,
                partHeatmaps: a.partHeatmaps,
                longOffsets: a.longOffsets,
                partOffsets: a.partOffsets
            };
        });
    }, t.prototype.dispose = function() {
        this.model.dispose();
    }, t;
}(), Y = function(t) {
    function e() {
        return null !== t && t.apply(this, arguments) || this;
    }
    return F(e, t), e.prototype.preprocessInput = function(t) {
        return (0, _tfjsCore.tidy)(function() {
            return (0, _tfjsCore.sub)((0, _tfjsCore.div)(t, 127.5), 1);
        });
    }, e.prototype.nameOutputResults = function(t) {
        return {
            offsets: t[0],
            segmentation: t[1],
            partHeatmaps: t[2],
            longOffsets: t[3],
            heatmap: t[4],
            displacementFwd: t[5],
            displacementBwd: t[6],
            partOffsets: t[7]
        };
    }, e;
}(X), G = [
    "nose",
    "leftEye",
    "rightEye",
    "leftEar",
    "rightEar",
    "leftShoulder",
    "rightShoulder",
    "leftElbow",
    "rightElbow",
    "leftWrist",
    "rightWrist",
    "leftHip",
    "rightHip",
    "leftKnee",
    "rightKnee",
    "leftAnkle",
    "rightAnkle"
], $ = G.length, J = G.reduce(function(t, e, n) {
    return t[e] = n, t;
}, {});
[
    [
        "leftHip",
        "leftShoulder"
    ],
    [
        "leftElbow",
        "leftShoulder"
    ],
    [
        "leftElbow",
        "leftWrist"
    ],
    [
        "leftHip",
        "leftKnee"
    ],
    [
        "leftKnee",
        "leftAnkle"
    ],
    [
        "rightHip",
        "rightShoulder"
    ],
    [
        "rightElbow",
        "rightShoulder"
    ],
    [
        "rightElbow",
        "rightWrist"
    ],
    [
        "rightHip",
        "rightKnee"
    ],
    [
        "rightKnee",
        "rightAnkle"
    ],
    [
        "leftShoulder",
        "rightShoulder"
    ],
    [
        "leftHip",
        "rightHip"
    ]
].map(function(t) {
    var e = t[0], n = t[1];
    return [
        J[e],
        J[n]
    ];
});
function Z(t, e, n) {
    var r = t[0], i = t[1], o = e[0], a = e[1], s = n.top, u = n.bottom;
    return [
        a / (n.left + n.right + i),
        o / (s + u + r)
    ];
}
function tt(t, e, n, r) {
    return {
        y: r.get(t, e, n),
        x: r.get(t, e, n + $)
    };
}
function et(t, e, n) {
    var r = tt(t.heatmapY, t.heatmapX, t.id, n), i = r.y, o = r.x;
    return {
        x: t.heatmapX * e + o,
        y: t.heatmapY * e + i
    };
}
function nt(t, e, n) {
    return t < e ? e : t > n ? n : t;
}
function rt(t, e) {
    return {
        x: t.x + e.x,
        y: t.y + e.y
    };
}
function it(t, e, n) {
    void 0 === n && (n = .3);
    for(var r = 0, i = 0, o = 0; o < t.length; o++)e.keypoints[o].score > n && (i += 1, r += Math.pow(t[o].x - e.keypoints[o].position.x, 2) + Math.pow(t[o].y - e.keypoints[o].position.y, 2));
    return 0 === i ? r = 1 / 0 : r /= i, r;
}
function ot(t, e, n, r, i, o, a) {
    for(var s = a[0], u = a[1], f = n(t), l = f.y * r + f.x, c = i[$ * (2 * l) + e], d = i[$ * (2 * l + 1) + e], h = t.y + c, p = t.x + d, m = 0; m < o; m++){
        h = Math.min(h, s - 1);
        var g = n({
            x: p = Math.min(p, u - 1),
            y: h
        }), v = g.y * r + g.x;
        h += c = i[$ * (2 * v) + e], p += d = i[$ * (2 * v + 1) + e];
    }
    return {
        x: p,
        y: h
    };
}
function at(t, e, n, r, i, o, a, s, u, f) {
    for(var l = i[0], c = i[1], d = o[0], h = o[1], p = s[0], m = s[1], g = [], v = function(t) {
        return function(t, e, n, r) {
            var i = e[0], o = e[1], a = n[0], s = n[1], u = Math.round(((i + t.y + 1) * s - 1) / r);
            return {
                x: Math.round(((o + t.x + 1) * a - 1) / r),
                y: u
            };
        }(t, [
            l,
            c
        ], [
            d,
            h
        ], u);
    }, w = 0; w < r; w++){
        var y = ot(t, w, v, a, e, f, [
            p,
            m
        ]);
        g.push(y);
    }
    for(var b = -1, S = 1 / 0, x = 0; x < n.length; x++){
        var M = it(g, n[x]);
        M < S && (b = x, S = M);
    }
    return b;
}
function st(t, e) {
    var n = t[0], r = t[1];
    return [
        Math.round((r - 1) / e + 1),
        Math.round((n - 1) / e + 1)
    ];
}
function ut(t, e, n, r, i, o, a, s, u, f, l) {
    for(var d = a[0], h = a[1], p = t.shape, m = p[0], g = p[1], v = e.shape.slice(0, 2), w = v[0], y = v[1], x = (0, _tfjsCore.reshape)(e, [
        w,
        y,
        2,
        $
    ]), M = new Float32Array(l * $ * 3).fill(0), k = 0; k < n.length; k++)for(var E = k * $ * 3, T = n[k], P = 0; P < $; P++){
        var I = T.keypoints[P], O = E + 3 * P;
        M[O] = I.score, M[O + 1] = I.position.y, M[O + 2] = I.position.x;
    }
    var _ = Z([
        r,
        i
    ], [
        d,
        h
    ], s), H = _[0], A = _[1], R = (0, _tfjsCore.tensor)(M, [
        l,
        $,
        3
    ]), F = s.top, C = s.left, D = {
        variableNames: [
            "segmentation",
            "longOffsets",
            "poses"
        ],
        outputShape: [
            m,
            g
        ],
        userCode: "\n    int convertToPositionInOutput(int pos, int pad, float scale, int stride) {\n      return round(((float(pos + pad) + 1.0) * scale - 1.0) / float(stride));\n    }\n\n    float convertToPositionInOutputFloat(\n        int pos, int pad, float scale, int stride) {\n      return ((float(pos + pad) + 1.0) * scale - 1.0) / float(stride);\n    }\n\n    float dist(float x1, float y1, float x2, float y2) {\n      return pow(x1 - x2, 2.0) + pow(y1 - y2, 2.0);\n    }\n\n    float sampleLongOffsets(float h, float w, int d, int k) {\n      float fh = fract(h);\n      float fw = fract(w);\n      int clH = int(ceil(h));\n      int clW = int(ceil(w));\n      int flH = int(floor(h));\n      int flW = int(floor(w));\n      float o11 = getLongOffsets(flH, flW, d, k);\n      float o12 = getLongOffsets(flH, clW, d, k);\n      float o21 = getLongOffsets(clH, flW, d, k);\n      float o22 = getLongOffsets(clH, clW, d, k);\n      float o1 = mix(o11, o12, fw);\n      float o2 = mix(o21, o22, fw);\n      return mix(o1, o2, fh);\n    }\n\n    int findNearestPose(int h, int w) {\n      float prob = getSegmentation(h, w);\n      if (prob < 1.0) {\n        return -1;\n      }\n\n      // Done(Tyler): convert from output space h/w to strided space.\n      float stridedH = convertToPositionInOutputFloat(\n        h, " + F + ", " + A + ", " + o + ");\n      float stridedW = convertToPositionInOutputFloat(\n        w, " + C + ", " + H + ", " + o + ");\n\n      float minDist = 1000000.0;\n      int iMin = -1;\n      for (int i = 0; i < " + l + "; i++) {\n        float curDistSum = 0.0;\n        int numKpt = 0;\n        for (int k = 0; k < " + $ + "; k++) {\n          float dy = sampleLongOffsets(stridedH, stridedW, 0, k);\n          float dx = sampleLongOffsets(stridedH, stridedW, 1, k);\n\n          float y = float(h) + dy;\n          float x = float(w) + dx;\n\n          for (int s = 0; s < " + u + "; s++) {\n            int yRounded = round(min(y, float(" + (r - 1) + ")));\n            int xRounded = round(min(x, float(" + (i - 1) + ")));\n\n            float yStrided = convertToPositionInOutputFloat(\n              yRounded, " + F + ", " + A + ", " + o + ");\n            float xStrided = convertToPositionInOutputFloat(\n              xRounded, " + C + ", " + H + ", " + o + ");\n\n            float dy = sampleLongOffsets(yStrided, xStrided, 0, k);\n            float dx = sampleLongOffsets(yStrided, xStrided, 1, k);\n\n            y = y + dy;\n            x = x + dx;\n          }\n\n          float poseScore = getPoses(i, k, 0);\n          float poseY = getPoses(i, k, 1);\n          float poseX = getPoses(i, k, 2);\n          if (poseScore > " + f + ") {\n            numKpt = numKpt + 1;\n            curDistSum = curDistSum + dist(x, y, poseX, poseY);\n          }\n        }\n        if (numKpt > 0 && curDistSum / float(numKpt) < minDist) {\n          minDist = curDistSum / float(numKpt);\n          iMin = i;\n        }\n      }\n      return iMin;\n    }\n\n    void main() {\n        ivec2 coords = getOutputCoords();\n        int nearestPose = findNearestPose(coords[0], coords[1]);\n        setOutput(float(nearestPose));\n      }\n  "
    };
    return (0, _tfjsCore.backend)().compileAndRun(D, [
        t,
        x,
        R
    ]);
}
function ft() {
    return "webgl" === (0, _tfjsCore.getBackend)();
}
function lt(t, e, n, o, s, u, f, l, c, d, h, p) {
    var m = f[0], g = f[1];
    return void 0 === c && (c = .2), void 0 === d && (d = 8), void 0 === h && (h = .3), void 0 === p && (p = 10), D(this, void 0, void 0, function() {
        var f, v, w, y, b;
        return B(this, function(S) {
            switch(S.label){
                case 0:
                    return f = n.filter(function(t) {
                        return t.score >= c;
                    }), ft() ? (w = (0, _tfjsCore.tidy)(function() {
                        var n = ut(t, e, f, o, s, u, [
                            m,
                            g
                        ], l, d, h, p), c = (0, _tfjsCore.engine)().makeTensorFromDataId(n.dataId, n.shape, n.dtype);
                        return f.map(function(t, e) {
                            return function(t, e) {
                                return (0, _tfjsCore.tidy)(function() {
                                    return (0, _tfjsCore.cast)((0, _tfjsCore.equal)(t, (0, _tfjsCore.scalar)(e)), "int32");
                                });
                            }(c, e);
                        });
                    }), [
                        4,
                        Promise.all(w.map(function(t) {
                            return t.data();
                        }))
                    ]) : [
                        3,
                        2
                    ];
                case 1:
                    return v = S.sent(), w.forEach(function(t) {
                        return t.dispose();
                    }), [
                        3,
                        5
                    ];
                case 2:
                    return [
                        4,
                        t.data()
                    ];
                case 3:
                    return y = S.sent(), [
                        4,
                        e.data()
                    ];
                case 4:
                    b = S.sent(), v = function(t, e, n, r, i, o, a, s, u, f) {
                        var l = a[0], c = a[1];
                        void 0 === f && (f = 5);
                        for(var d = n.map(function(t) {
                            return new Uint8Array(r * i).fill(0);
                        }), h = s.top, p = s.left, m = Z([
                            r,
                            i
                        ], [
                            l,
                            c
                        ], s), g = m[0], v = m[1], w = st([
                            l,
                            c
                        ], o)[0], y = 0; y < r; y += 1)for(var b = 0; b < i; b += 1){
                            var S = y * i + b;
                            if (1 === t[S]) {
                                var x = at({
                                    x: b,
                                    y: y
                                }, e, n, f, [
                                    h,
                                    p
                                ], [
                                    g,
                                    v
                                ], w, [
                                    r,
                                    i
                                ], o, u);
                                x >= 0 && (d[x][S] = 1);
                            }
                        }
                        return d;
                    }(y, b, f, o, s, u, [
                        m,
                        g
                    ], l, d), S.label = 5;
                case 5:
                    return [
                        2,
                        v.map(function(t, e) {
                            return {
                                data: t,
                                pose: f[e],
                                width: s,
                                height: o
                            };
                        })
                    ];
            }
        });
    });
}
function ct(t, e, n, o, s, u, f, l, c, m, g, v, w) {
    var y = l[0], b = l[1];
    return void 0 === m && (m = .2), void 0 === g && (g = 8), void 0 === v && (v = .3), void 0 === w && (w = 10), D(this, void 0, void 0, function() {
        var l, S, x, E, T, P;
        return B(this, function(I) {
            switch(I.label){
                case 0:
                    return l = o.filter(function(t) {
                        return t.score >= m;
                    }), ft() ? (x = (0, _tfjsCore.tidy)(function() {
                        var o = ut(t, e, l, s, u, f, [
                            y,
                            b
                        ], c, g, v, w), m = (0, _tfjsCore.engine)().makeTensorFromDataId(o.dataId, o.shape, o.dtype);
                        return l.map(function(t, e) {
                            return function(t, e, n) {
                                return (0, _tfjsCore.tidy)(function() {
                                    return (0, _tfjsCore.sub)((0, _tfjsCore.mul)((0, _tfjsCore.cast)((0, _tfjsCore.equal)(t, (0, _tfjsCore.scalar)(n)), "int32"), (0, _tfjsCore.add)(e, 1)), 1);
                                });
                            }(m, n, e);
                        });
                    }), [
                        4,
                        Promise.all(x.map(function(t) {
                            return t.data();
                        }))
                    ]) : [
                        3,
                        2
                    ];
                case 1:
                    return S = I.sent(), x.forEach(function(t) {
                        return t.dispose();
                    }), [
                        3,
                        6
                    ];
                case 2:
                    return [
                        4,
                        t.data()
                    ];
                case 3:
                    return E = I.sent(), [
                        4,
                        e.data()
                    ];
                case 4:
                    return T = I.sent(), [
                        4,
                        n.data()
                    ];
                case 5:
                    P = I.sent(), S = function(t, e, n, r, i, o, a, s, u, f, l) {
                        var c = s[0], d = s[1];
                        void 0 === l && (l = 5);
                        for(var h = r.map(function(t) {
                            return new Int32Array(i * o).fill(-1);
                        }), p = u.top, m = u.left, g = Z([
                            i,
                            o
                        ], [
                            c,
                            d
                        ], u), v = g[0], w = g[1], y = st([
                            c,
                            d
                        ], a)[0], b = 0; b < i; b += 1)for(var S = 0; S < o; S += 1){
                            var x = b * o + S;
                            if (1 === t[x]) {
                                var M = at({
                                    x: S,
                                    y: b
                                }, e, r, l, [
                                    p,
                                    m
                                ], [
                                    v,
                                    w
                                ], y, [
                                    i,
                                    o
                                ], a, f);
                                M >= 0 && (h[M][x] = n[x]);
                            }
                        }
                        return h;
                    }(E, T, P, l, s, u, f, [
                        y,
                        b
                    ], c, g), I.label = 6;
                case 6:
                    return [
                        2,
                        S.map(function(t, e) {
                            return {
                                pose: l[e],
                                data: t,
                                height: s,
                                width: u
                            };
                        })
                    ];
            }
        });
    });
}
function dt(t) {
    return Math.floor(t / 2);
}
var ht = function() {
    function t(t, e) {
        this.priorityQueue = new Array(t), this.numberOfElements = -1, this.getElementValue = e;
    }
    return t.prototype.enqueue = function(t) {
        this.priorityQueue[++this.numberOfElements] = t, this.swim(this.numberOfElements);
    }, t.prototype.dequeue = function() {
        var t = this.priorityQueue[0];
        return this.exchange(0, this.numberOfElements--), this.sink(0), this.priorityQueue[this.numberOfElements + 1] = null, t;
    }, t.prototype.empty = function() {
        return -1 === this.numberOfElements;
    }, t.prototype.size = function() {
        return this.numberOfElements + 1;
    }, t.prototype.all = function() {
        return this.priorityQueue.slice(0, this.numberOfElements + 1);
    }, t.prototype.max = function() {
        return this.priorityQueue[0];
    }, t.prototype.swim = function(t) {
        for(; t > 0 && this.less(dt(t), t);)this.exchange(t, dt(t)), t = dt(t);
    }, t.prototype.sink = function(t) {
        for(; 2 * t <= this.numberOfElements;){
            var e = 2 * t;
            if (e < this.numberOfElements && this.less(e, e + 1) && e++, !this.less(t, e)) break;
            this.exchange(t, e), t = e;
        }
    }, t.prototype.getValueAt = function(t) {
        return this.getElementValue(this.priorityQueue[t]);
    }, t.prototype.less = function(t, e) {
        return this.getValueAt(t) < this.getValueAt(e);
    }, t.prototype.exchange = function(t, e) {
        var n = this.priorityQueue[t];
        this.priorityQueue[t] = this.priorityQueue[e], this.priorityQueue[e] = n;
    }, t;
}();
function pt(t, e, n, r, i, o) {
    for(var a = o.shape, s = a[0], u = a[1], f = !0, l = Math.max(n - i, 0), c = Math.min(n + i + 1, s), d = l; d < c; ++d){
        for(var h = Math.max(r - i, 0), p = Math.min(r + i + 1, u), m = h; m < p; ++m)if (o.get(d, m, t) > e) {
            f = !1;
            break;
        }
        if (!f) break;
    }
    return f;
}
var mt = [
    [
        "nose",
        "leftEye"
    ],
    [
        "leftEye",
        "leftEar"
    ],
    [
        "nose",
        "rightEye"
    ],
    [
        "rightEye",
        "rightEar"
    ],
    [
        "nose",
        "leftShoulder"
    ],
    [
        "leftShoulder",
        "leftElbow"
    ],
    [
        "leftElbow",
        "leftWrist"
    ],
    [
        "leftShoulder",
        "leftHip"
    ],
    [
        "leftHip",
        "leftKnee"
    ],
    [
        "leftKnee",
        "leftAnkle"
    ],
    [
        "nose",
        "rightShoulder"
    ],
    [
        "rightShoulder",
        "rightElbow"
    ],
    [
        "rightElbow",
        "rightWrist"
    ],
    [
        "rightShoulder",
        "rightHip"
    ],
    [
        "rightHip",
        "rightKnee"
    ],
    [
        "rightKnee",
        "rightAnkle"
    ]
].map(function(t) {
    var e = t[0], n = t[1];
    return [
        J[e],
        J[n]
    ];
}), gt = mt.map(function(t) {
    return t[1];
}), vt = mt.map(function(t) {
    return t[0];
});
function wt(t, e, n, r) {
    return {
        y: nt(Math.round(t.y / e), 0, n - 1),
        x: nt(Math.round(t.x / e), 0, r - 1)
    };
}
function yt(t, e, n, r, i, o, a, s) {
    void 0 === s && (s = 2);
    for(var u = r.shape, f = u[0], l = u[1], c = function(t, e, n) {
        var r = n.shape[2] / 2;
        return {
            y: n.get(e.y, e.x, t),
            x: n.get(e.y, e.x, r + t)
        };
    }(t, wt(e.position, o, f, l), a), d = rt(e.position, c), h = 0; h < s; h++){
        var p = wt(d, o, f, l), m = tt(p.y, p.x, n, i);
        d = rt({
            x: p.x * o,
            y: p.y * o
        }, {
            x: m.x,
            y: m.y
        });
    }
    var g = wt(d, o, f, l), v = r.get(g.y, g.x, n);
    return {
        position: d,
        part: G[n],
        score: v
    };
}
function bt(t, e, n, r, i, o) {
    var a = e.shape[2], s = gt.length, u = new Array(a), f = t.part, l = t.score, c = et(f, r, n);
    u[f.id] = {
        score: l,
        part: G[f.id],
        position: c
    };
    for(var d = s - 1; d >= 0; --d){
        var h = gt[d], p = vt[d];
        u[h] && !u[p] && (u[p] = yt(d, u[h], p, e, n, r, o));
    }
    for(d = 0; d < s; ++d){
        h = vt[d], p = gt[d];
        u[h] && !u[p] && (u[p] = yt(d, u[h], p, e, n, r, i));
    }
    return u;
}
function St(t, e, n, r) {
    var i = n.x, o = n.y;
    return t.some(function(t) {
        var n, a, s, u, f, l, c = t.keypoints[r].position;
        return n = o, a = i, s = c.y, u = c.x, (f = s - n) * f + (l = u - a) * l <= e;
    });
}
function xt(t, e, n) {
    var r = n.reduce(function(n, r, i) {
        var o = r.position, a = r.score;
        return St(t, e, o, i) || (n += a), n;
    }, 0);
    return r / n.length;
}
function Mt(t, e, n, r, i, o, a, s) {
    void 0 === a && (a = .5), void 0 === s && (s = 20);
    for(var u = [], f = function(t, e, n) {
        for(var r = n.shape, i = r[0], o = r[1], a = r[2], s = new ht(i * o * a, function(t) {
            return t.score;
        }), u = 0; u < i; ++u)for(var f = 0; f < o; ++f)for(var l = 0; l < a; ++l){
            var c = n.get(u, f, l);
            c < t || pt(l, c, u, f, e, n) && s.enqueue({
                score: c,
                part: {
                    heatmapY: u,
                    heatmapX: f,
                    id: l
                }
            });
        }
        return s;
    }(a, 1, t), l = s * s; u.length < o && !f.empty();){
        var c = f.dequeue();
        if (!St(u, l, et(c.part, i, e), c.part.id)) {
            var d = bt(c, t, e, i, n, r), h = xt(u, l, d);
            u.push({
                keypoints: d,
                score: h
            });
        }
    }
    return u;
}
var kt, Et = [
    -123.15,
    -115.9,
    -103.06
], Tt = function(t) {
    function e() {
        return null !== t && t.apply(this, arguments) || this;
    }
    return F(e, t), e.prototype.preprocessInput = function(t) {
        return (0, _tfjsCore.add)(t, Et);
    }, e.prototype.nameOutputResults = function(t) {
        var e = t[0], n = t[1], r = t[2], i = t[3], o = t[4], a = t[5];
        return {
            offsets: o,
            segmentation: t[6],
            partHeatmaps: a,
            longOffsets: i,
            heatmap: r,
            displacementFwd: n,
            displacementBwd: e,
            partOffsets: t[7]
        };
    }, e;
}(X), Pt = "https://storage.googleapis.com/tfjs-models/savedmodel/bodypix/resnet50/", It = "https://storage.googleapis.com/tfjs-models/savedmodel/bodypix/mobilenet/";
function Ot(t) {
    if ("undefined" != typeof HTMLCanvasElement && t instanceof HTMLCanvasElement || "undefined" != typeof OffscreenCanvas && t instanceof OffscreenCanvas || "undefined" != typeof HTMLImageElement && t instanceof HTMLImageElement) return function(t) {
        if ("offsetHeight" in t && 0 !== t.offsetHeight && "offsetWidth" in t && 0 !== t.offsetWidth) return [
            t.offsetHeight,
            t.offsetWidth
        ];
        if (null != t.height && null != t.width) return [
            t.height,
            t.width
        ];
        throw new Error("HTMLImageElement must have height and width attributes set.");
    }(t);
    if ("undefined" != typeof ImageData && t instanceof ImageData) return [
        t.height,
        t.width
    ];
    if ("undefined" != typeof HTMLVideoElement && t instanceof HTMLVideoElement) return function(t) {
        return t.hasAttribute("height") && t.hasAttribute("width") ? [
            t.height,
            t.width
        ] : [
            t.videoHeight,
            t.videoWidth
        ];
    }(t);
    if (t instanceof (0, _tfjsCore.Tensor)) return [
        t.shape[0],
        t.shape[1]
    ];
    throw new Error("error: Unknown input type: " + t + ".");
}
function _t(t, e) {
    return function(t, e) {
        return (t - 1) % e == 0;
    }(t, e) ? t : Math.floor(t / e) * e + 1;
}
var Ht = {
    low: "low",
    medium: "medium",
    high: "high",
    full: "full"
}, At = ((kt = {})[Ht.low] = .25, kt[Ht.medium] = .5, kt[Ht.high] = .75, kt[Ht.full] = 1, kt);
function Rt(t, e, n) {
    var r = n[0], i = n[1], o = function(t) {
        if ("string" == typeof t) {
            var e = At[t];
            return (0, _tfjsCore.util).assert("number" == typeof e, function() {
                return "string value of inputResolution must be one of " + Object.values(Ht).join(",") + " but was " + t + ".";
            }), e;
        }
        return (0, _tfjsCore.util).assert("number" == typeof t && t <= 2 && t >= .1, function() {
            return "inputResolution must be a string or number between 0.1 and 2, but was " + t;
        }), t;
    }(t);
    return [
        _t(r * o, e),
        _t(i * o, e)
    ];
}
function Ft(t, e, n, i, o) {
    var a = e[0], s = e[1], f = n[0], l = n[1], c = i[0], d = c[0], h = c[1], p = i[1], m = p[0], w = p[1];
    return void 0 === o && (o = !1), (0, _tfjsCore.tidy)(function() {
        var e = (0, _tfjsCore.image).resizeBilinear(t, [
            f,
            l
        ], !0);
        return o && (e = (0, _tfjsCore.sigmoid)(e)), function(t, e, n) {
            var i = e[0], o = e[1], a = n[0], s = a[0], f = a[1], l = n[1], c = l[0], d = l[1];
            return (0, _tfjsCore.tidy)(function() {
                var e = (0, _tfjsCore.expandDims)(t);
                return (0, _tfjsCore.squeeze)((0, _tfjsCore.image).cropAndResize(e, [
                    [
                        s / (i + s + f - 1),
                        c / (o + c + d - 1),
                        (s + i - 1) / (i + s + f - 1),
                        (c + o - 1) / (o + c + d - 1)
                    ]
                ], [
                    0
                ], [
                    i,
                    o
                ]), [
                    0
                ]);
            });
        }(e, [
            a,
            s
        ], [
            [
                d,
                h
            ],
            [
                m,
                w
            ]
        ]);
    });
}
function Ct(t, i) {
    var o = i[0], a = i[1], s = Ot(t), u = s[0], f = s[1], l = a / o, c = [
        0,
        0,
        0,
        0
    ], d = c[0], h = c[1], p = c[2], m = c[3];
    f / u < l ? (d = 0, h = 0, p = Math.round(.5 * (l * u - f)), m = Math.round(.5 * (l * u - f))) : (d = Math.round(.5 * (1 / l * f - u)), h = Math.round(.5 * (1 / l * f - u)), p = 0, m = 0);
    var g = (0, _tfjsCore.tidy)(function() {
        var r = function(t) {
            return t instanceof (0, _tfjsCore.Tensor) ? t : (0, _tfjsCore.browser).fromPixels(t);
        }(t);
        return r = (0, _tfjsCore.pad3d)(r, [
            [
                d,
                h
            ],
            [
                p,
                m
            ],
            [
                0,
                0
            ]
        ]), (0, _tfjsCore.image).resizeBilinear(r, [
            o,
            a
        ]);
    });
    return {
        resized: g,
        padding: {
            top: d,
            left: p,
            right: m,
            bottom: h
        }
    };
}
function Dt(t) {
    return D(this, void 0, void 0, function() {
        return B(this, function(e) {
            return [
                2,
                Promise.all(t.map(function(t) {
                    return t.buffer();
                }))
            ];
        });
    });
}
function Bt(t, e, n, r, i) {
    var o = e[0], a = e[1], s = n[0], u = n[1], f = function(t, e, n, r, i) {
        return void 0 === r && (r = 0), void 0 === i && (i = 0), 1 === n && 1 === e && 0 === r && 0 === i ? t : t.map(function(t) {
            return function(t, e, n, r, i) {
                return void 0 === r && (r = 0), void 0 === i && (i = 0), {
                    score: t.score,
                    keypoints: t.keypoints.map(function(t) {
                        var o = t.score, a = t.part, s = t.position;
                        return {
                            score: o,
                            part: a,
                            position: {
                                x: s.x * n + i,
                                y: s.y * e + r
                            }
                        };
                    })
                };
            }(t, e, n, r, i);
        });
    }(t, (o + r.top + r.bottom) / s, (a + r.left + r.right) / u, -r.top, -r.left);
    return i ? function(t, e) {
        return e <= 0 ? t : t.map(function(t) {
            return function(t, e) {
                return {
                    score: t.score,
                    keypoints: t.keypoints.map(function(t) {
                        var n = t.score, r = t.part, i = t.position;
                        return {
                            score: n,
                            part: r,
                            position: {
                                x: e - 1 - i.x,
                                y: i.y
                            }
                        };
                    })
                };
            }(t, e);
        });
    }(f, a) : f;
}
var Lt = {
    architecture: "MobileNetV1",
    outputStride: 16,
    quantBytes: 4,
    multiplier: .75
}, zt = [
    "MobileNetV1",
    "ResNet50"
], Wt = {
    MobileNetV1: [
        8,
        16,
        32
    ],
    ResNet50: [
        32,
        16
    ]
}, jt = {
    MobileNetV1: [
        .5,
        .75,
        1
    ],
    ResNet50: [
        1
    ]
}, Vt = [
    1,
    2,
    4
];
var Kt = {
    flipHorizontal: !1,
    internalResolution: "medium",
    segmentationThreshold: .7,
    maxDetections: 10,
    scoreThreshold: .4,
    nmsRadius: 20
}, Ut = {
    flipHorizontal: !1,
    internalResolution: "medium",
    segmentationThreshold: .7,
    maxDetections: 10,
    scoreThreshold: .4,
    nmsRadius: 20,
    minKeypointScore: .3,
    refineSteps: 10
};
function qt(t) {
    var e = t.segmentationThreshold, n = t.maxDetections, r = t.scoreThreshold, i = t.nmsRadius;
    if (e < 0 || e > 1) throw new Error("segmentationThreshold " + e + ". Should be in range [0.0, 1.0]");
    if (n <= 0) throw new Error("Invalid maxDetections " + n + ". Should be > 0");
    if (r < 0 || r > 1) throw new Error("Invalid scoreThreshold " + r + ". Should be in range [0.0, 1.0]");
    if (i <= 0) throw new Error("Invalid nmsRadius " + i + ".");
}
function Nt(t) {
    var e = t.segmentationThreshold, n = t.maxDetections, r = t.scoreThreshold, i = t.nmsRadius, o = t.minKeypointScore, a = t.refineSteps;
    if (e < 0 || e > 1) throw new Error("segmentationThreshold " + e + ". Should be in range [0.0, 1.0]");
    if (n <= 0) throw new Error("Invalid maxDetections " + n + ". Should be > 0");
    if (r < 0 || r > 1) throw new Error("Invalid scoreThreshold " + r + ". Should be in range [0.0, 1.0]");
    if (i <= 0) throw new Error("Invalid nmsRadius " + i + ".");
    if (o < 0 || o > 1) throw new Error("Invalid minKeypointScore " + o + ".Should be in range [0.0, 1.0]");
    if (a <= 0 || a > 20) throw new Error("Invalid refineSteps " + a + ".Should be in range [1, 20]");
}
var Qt = function() {
    function t(t) {
        this.baseModel = t;
    }
    return t.prototype.predictForPersonSegmentation = function(t) {
        var e = this.baseModel.predict(t);
        return {
            segmentLogits: e.segmentation,
            heatmapScores: e.heatmapScores,
            offsets: e.offsets,
            displacementFwd: e.displacementFwd,
            displacementBwd: e.displacementBwd
        };
    }, t.prototype.predictForPersonSegmentationAndPart = function(t) {
        var e = this.baseModel.predict(t);
        return {
            segmentLogits: e.segmentation,
            partHeatmapLogits: e.partHeatmaps,
            heatmapScores: e.heatmapScores,
            offsets: e.offsets,
            displacementFwd: e.displacementFwd,
            displacementBwd: e.displacementBwd
        };
    }, t.prototype.predictForMultiPersonInstanceSegmentationAndPart = function(t) {
        var e = this.baseModel.predict(t);
        return {
            segmentLogits: e.segmentation,
            longOffsets: e.longOffsets,
            heatmapScores: e.heatmapScores,
            offsets: e.offsets,
            displacementFwd: e.displacementFwd,
            displacementBwd: e.displacementBwd,
            partHeatmaps: e.partHeatmaps
        };
    }, t.prototype.segmentPersonActivation = function(t, e, n) {
        var i = this;
        void 0 === n && (n = .5);
        var o = Ot(t), a = o[0], s = o[1], u = Rt(e, this.baseModel.outputStride, [
            a,
            s
        ]), f = Ct(t, u), l = f.resized, c = f.padding, d = (0, _tfjsCore.tidy)(function() {
            var t = i.predictForPersonSegmentation(l), e = t.segmentLogits, r = t.heatmapScores, o = t.offsets, u = t.displacementFwd, f = t.displacementBwd, d = l.shape, h = d[0], p = d[1], m = Ft(e, [
                a,
                s
            ], [
                h,
                p
            ], [
                [
                    c.top,
                    c.bottom
                ],
                [
                    c.left,
                    c.right
                ]
            ], true);
            return {
                segmentation: N((0, _tfjsCore.squeeze)(m), n),
                heatmapScores: r,
                offsets: o,
                displacementFwd: u,
                displacementBwd: f
            };
        }), h = d.segmentation, p = d.heatmapScores, m = d.offsets, v = d.displacementFwd, w = d.displacementBwd;
        return l.dispose(), {
            segmentation: h,
            heatmapScores: p,
            offsets: m,
            displacementFwd: v,
            displacementBwd: w,
            padding: c,
            internalResolutionHeightAndWidth: u
        };
    }, t.prototype.segmentPerson = function(t, e) {
        return void 0 === e && (e = Kt), D(this, void 0, void 0, function() {
            var n, r, i, o, a, s, u, f, l, c, d, h, p, m, g, v, w, y;
            return B(this, function(b) {
                switch(b.label){
                    case 0:
                        return qt(e = C(C({}, Kt), e)), n = this.segmentPersonActivation(t, e.internalResolution, e.segmentationThreshold), r = n.segmentation, i = n.heatmapScores, o = n.offsets, a = n.displacementFwd, s = n.displacementBwd, u = n.padding, f = n.internalResolutionHeightAndWidth, l = r.shape, c = l[0], d = l[1], [
                            4,
                            r.data()
                        ];
                    case 1:
                        return h = b.sent(), r.dispose(), [
                            4,
                            Dt([
                                i,
                                o,
                                a,
                                s
                            ])
                        ];
                    case 2:
                        return p = b.sent(), m = p[0], g = p[1], v = p[2], w = p[3], y = Bt(y = Mt(m, g, v, w, this.baseModel.outputStride, e.maxDetections, e.scoreThreshold, e.nmsRadius), [
                            c,
                            d
                        ], f, u, false), i.dispose(), o.dispose(), a.dispose(), s.dispose(), [
                            2,
                            {
                                height: c,
                                width: d,
                                data: h,
                                allPoses: y
                            }
                        ];
                }
            });
        });
    }, t.prototype.segmentMultiPerson = function(t, e) {
        return void 0 === e && (e = Ut), D(this, void 0, void 0, function() {
            var n, i, o, a, s, u, f, l, c, d, h, p, m, v, w, y, b, S, x, M, k, E = this;
            return B(this, function(T) {
                switch(T.label){
                    case 0:
                        return Nt(e = C(C({}, Ut), e)), n = Ot(t), i = n[0], o = n[1], a = Rt(e.internalResolution, this.baseModel.outputStride, [
                            i,
                            o
                        ]), s = Ct(t, a), u = s.resized, f = s.padding, l = (0, _tfjsCore.tidy)(function() {
                            var t, n = E.predictForMultiPersonInstanceSegmentationAndPart(u), r = n.segmentLogits, s = n.longOffsets, l = n.heatmapScores, c = n.offsets, d = n.displacementFwd, h = n.displacementBwd, p = Ft(r, [
                                i,
                                o
                            ], a, [
                                [
                                    f.top,
                                    f.bottom
                                ],
                                [
                                    f.left,
                                    f.right
                                ]
                            ], true);
                            return t = s, {
                                segmentation: N((0, _tfjsCore.squeeze)(p), e.segmentationThreshold),
                                longOffsets: t,
                                heatmapScoresRaw: l,
                                offsetsRaw: c,
                                displacementFwdRaw: d,
                                displacementBwdRaw: h
                            };
                        }), c = l.segmentation, d = l.longOffsets, h = l.heatmapScoresRaw, p = l.offsetsRaw, m = l.displacementFwdRaw, v = l.displacementBwdRaw, [
                            4,
                            Dt([
                                h,
                                p,
                                m,
                                v
                            ])
                        ];
                    case 1:
                        return w = T.sent(), y = w[0], b = w[1], S = w[2], x = w[3], M = Bt(M = Mt(y, b, S, x, this.baseModel.outputStride, e.maxDetections, e.scoreThreshold, e.nmsRadius), [
                            i,
                            o
                        ], a, f, false), [
                            4,
                            lt(c, d, M, i, o, this.baseModel.outputStride, a, f, e.scoreThreshold, e.refineSteps, e.minKeypointScore, e.maxDetections)
                        ];
                    case 2:
                        return k = T.sent(), u.dispose(), c.dispose(), d.dispose(), h.dispose(), p.dispose(), m.dispose(), v.dispose(), [
                            2,
                            k
                        ];
                }
            });
        });
    }, t.prototype.segmentPersonPartsActivation = function(t, e, n) {
        var i = this;
        void 0 === n && (n = .5);
        var o = Ot(t), a = o[0], s = o[1], u = Rt(e, this.baseModel.outputStride, [
            a,
            s
        ]), f = Ct(t, u), l = f.resized, c = f.padding, d = (0, _tfjsCore.tidy)(function() {
            var t = i.predictForPersonSegmentationAndPart(l), e = t.segmentLogits, r = t.partHeatmapLogits, o = t.heatmapScores, u = t.offsets, f = t.displacementFwd, d = t.displacementBwd, h = l.shape, p = h[0], m = h[1], v = Ft(e, [
                a,
                s
            ], [
                p,
                m
            ], [
                [
                    c.top,
                    c.bottom
                ],
                [
                    c.left,
                    c.right
                ]
            ], true), w = Ft(r, [
                a,
                s
            ], [
                p,
                m
            ], [
                [
                    c.top,
                    c.bottom
                ],
                [
                    c.left,
                    c.right
                ]
            ], true);
            return {
                partSegmentation: Q(N((0, _tfjsCore.squeeze)(v), n), w),
                heatmapScores: o,
                offsets: u,
                displacementFwd: f,
                displacementBwd: d
            };
        }), h = d.partSegmentation, p = d.heatmapScores, m = d.offsets, v = d.displacementFwd, w = d.displacementBwd;
        return l.dispose(), {
            partSegmentation: h,
            heatmapScores: p,
            offsets: m,
            displacementFwd: v,
            displacementBwd: w,
            padding: c,
            internalResolutionHeightAndWidth: u
        };
    }, t.prototype.segmentPersonParts = function(t, e) {
        return void 0 === e && (e = Kt), D(this, void 0, void 0, function() {
            var n, r, i, o, a, s, u, f, l, c, d, h, p, m, g, v, w, y;
            return B(this, function(b) {
                switch(b.label){
                    case 0:
                        return qt(e = C(C({}, Kt), e)), n = this.segmentPersonPartsActivation(t, e.internalResolution, e.segmentationThreshold), r = n.partSegmentation, i = n.heatmapScores, o = n.offsets, a = n.displacementFwd, s = n.displacementBwd, u = n.padding, f = n.internalResolutionHeightAndWidth, l = r.shape, c = l[0], d = l[1], [
                            4,
                            r.data()
                        ];
                    case 1:
                        return h = b.sent(), r.dispose(), [
                            4,
                            Dt([
                                i,
                                o,
                                a,
                                s
                            ])
                        ];
                    case 2:
                        return p = b.sent(), m = p[0], g = p[1], v = p[2], w = p[3], y = Bt(y = Mt(m, g, v, w, this.baseModel.outputStride, e.maxDetections, e.scoreThreshold, e.nmsRadius), [
                            c,
                            d
                        ], f, u, false), i.dispose(), o.dispose(), a.dispose(), s.dispose(), [
                            2,
                            {
                                height: c,
                                width: d,
                                data: h,
                                allPoses: y
                            }
                        ];
                }
            });
        });
    }, t.prototype.segmentMultiPersonParts = function(t, e) {
        return void 0 === e && (e = Ut), D(this, void 0, void 0, function() {
            var n, o, a, s, d, h, p, m, v, w, y, b, S, x, M, k, E, T, P, I, O, _, H = this;
            return B(this, function(A) {
                switch(A.label){
                    case 0:
                        return Nt(e = C(C({}, Ut), e)), n = Ot(t), o = n[0], a = n[1], s = Rt(e.internalResolution, this.baseModel.outputStride, [
                            o,
                            a
                        ]), d = Ct(t, s), h = d.resized, p = d.padding, m = (0, _tfjsCore.tidy)(function() {
                            var t = H.predictForMultiPersonInstanceSegmentationAndPart(h), n = t.segmentLogits, d = t.longOffsets, m = t.heatmapScores, v = t.offsets, w = t.displacementFwd, y = t.displacementBwd, b = t.partHeatmaps, S = Ft(n, [
                                o,
                                a
                            ], s, [
                                [
                                    p.top,
                                    p.bottom
                                ],
                                [
                                    p.left,
                                    p.right
                                ]
                            ], true), x = Ft(b, [
                                o,
                                a
                            ], s, [
                                [
                                    p.top,
                                    p.bottom
                                ],
                                [
                                    p.left,
                                    p.right
                                ]
                            ], true), M = d, k = N((0, _tfjsCore.squeeze)(S), e.segmentationThreshold), E = function(t) {
                                var e = t.shape, n = e[0], o = e[1], a = e[2];
                                return (0, _tfjsCore.tidy)(function() {
                                    var e = q(t), r = (0, _tfjsCore.expandDims)((0, _tfjsCore.range)(0, a, 1, "int32"), 1), s = (0, _tfjsCore.cast)((0, _tfjsCore.matMul)(e, r), "int32");
                                    return (0, _tfjsCore.reshape)(s, [
                                        n,
                                        o
                                    ]);
                                });
                            }(x);
                            return {
                                segmentation: k,
                                longOffsets: M,
                                heatmapScoresRaw: m,
                                offsetsRaw: v,
                                displacementFwdRaw: w,
                                displacementBwdRaw: y,
                                partSegmentation: E
                            };
                        }), v = m.segmentation, w = m.longOffsets, y = m.heatmapScoresRaw, b = m.offsetsRaw, S = m.displacementFwdRaw, x = m.displacementBwdRaw, M = m.partSegmentation, [
                            4,
                            Dt([
                                y,
                                b,
                                S,
                                x
                            ])
                        ];
                    case 1:
                        return k = A.sent(), E = k[0], T = k[1], P = k[2], I = k[3], O = Bt(O = Mt(E, T, P, I, this.baseModel.outputStride, e.maxDetections, e.scoreThreshold, e.nmsRadius), [
                            o,
                            a
                        ], s, p, false), [
                            4,
                            ct(v, w, M, O, o, a, this.baseModel.outputStride, s, p, e.scoreThreshold, e.refineSteps, e.minKeypointScore, e.maxDetections)
                        ];
                    case 2:
                        return _ = A.sent(), h.dispose(), v.dispose(), w.dispose(), y.dispose(), b.dispose(), S.dispose(), x.dispose(), M.dispose(), [
                            2,
                            _
                        ];
                }
            });
        });
    }, t.prototype.dispose = function() {
        this.baseModel.dispose();
    }, t;
}();
function Xt(e) {
    return D(this, void 0, void 0, function() {
        var n, r, i, o, a, s;
        return B(this, function(u) {
            switch(u.label){
                case 0:
                    if (n = e.outputStride, r = e.quantBytes, i = e.multiplier, null == _tfjsCore) throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this\n        model.");
                    return o = function(t, e, n) {
                        var r = {
                            1: "100",
                            .75: "075",
                            .5: "050"
                        }, i = "model-stride" + t + ".json";
                        return 4 === n ? It + "float/" + r[e] + "/" + i : It + "quant" + n + "/" + r[e] + "/" + i;
                    }(n, i, r), [
                        4,
                        (0, _tfjsConverter.loadGraphModel)(e.modelUrl || o)
                    ];
                case 1:
                    return a = u.sent(), s = new Y(a, n), [
                        2,
                        new Qt(s)
                    ];
            }
        });
    });
}
function Yt(e) {
    return D(this, void 0, void 0, function() {
        var n, r, i, o, a;
        return B(this, function(s) {
            switch(s.label){
                case 0:
                    if (n = e.outputStride, r = e.quantBytes, null == _tfjsCore) throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this\n        model.");
                    return i = function(t, e) {
                        var n = "model-stride" + t + ".json";
                        return 4 === e ? Pt + "float/" + n : Pt + "quant" + e + "/" + n;
                    }(n, r), [
                        4,
                        (0, _tfjsConverter.loadGraphModel)(e.modelUrl || i)
                    ];
                case 1:
                    return o = s.sent(), a = new Tt(o, n), [
                        2,
                        new Qt(a)
                    ];
            }
        });
    });
}
function Gt(t) {
    return void 0 === t && (t = Lt), D(this, void 0, void 0, function() {
        return B(this, function(e) {
            return "ResNet50" === (t = function(t) {
                if (null == (t = t || Lt).architecture && (t.architecture = "MobileNetV1"), zt.indexOf(t.architecture) < 0) throw new Error("Invalid architecture " + t.architecture + ". Should be one of " + zt);
                if (null == t.outputStride && (t.outputStride = 16), Wt[t.architecture].indexOf(t.outputStride) < 0) throw new Error("Invalid outputStride " + t.outputStride + ". Should be one of " + Wt[t.architecture] + " for architecture " + t.architecture + ".");
                if (null == t.multiplier && (t.multiplier = 1), jt[t.architecture].indexOf(t.multiplier) < 0) throw new Error("Invalid multiplier " + t.multiplier + ". Should be one of " + jt[t.architecture] + " for architecture " + t.architecture + ".");
                if (null == t.quantBytes && (t.quantBytes = 4), Vt.indexOf(t.quantBytes) < 0) throw new Error("Invalid quantBytes " + t.quantBytes + ". Should be one of " + Vt + " for architecture " + t.architecture + ".");
                return t;
            }(t)).architecture ? [
                2,
                Yt(t)
            ] : "MobileNetV1" === t.architecture ? [
                2,
                Xt(t)
            ] : [
                2,
                null
            ];
        });
    });
}
var $t = [
    "left_face",
    "right_face",
    "left_upper_arm_front",
    "left_upper_arm_back",
    "right_upper_arm_front",
    "right_upper_arm_back",
    "left_lower_arm_front",
    "left_lower_arm_back",
    "right_lower_arm_front",
    "right_lower_arm_back",
    "left_hand",
    "right_hand",
    "torso_front",
    "torso_back",
    "left_upper_leg_front",
    "left_upper_leg_back",
    "right_upper_leg_front",
    "right_upper_leg_back",
    "left_lower_leg_front",
    "left_lower_leg_back",
    "right_lower_leg_front",
    "right_lower_leg_back",
    "left_feet",
    "right_feet"
], Jt = function() {
    function t(t) {
        this.mask = t;
    }
    return t.prototype.toCanvasImageSource = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    z(this.mask)
                ];
            });
        });
    }, t.prototype.toImageData = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    this.mask
                ];
            });
        });
    }, t.prototype.toTensor = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    j(this.mask)
                ];
            });
        });
    }, t.prototype.getUnderlyingType = function() {
        return "imagedata";
    }, t;
}();
function Zt(t) {
    if (V(t), 255 !== t) throw new Error("Foreground id must be 255 but got " + t);
    return "person";
}
function te(t) {
    if (V(t), t >= $t.length) throw new Error("Invalid body part value " + t);
    return $t[t];
}
var ee = function() {
    function t(t) {
        this.bodyPixModel = t;
    }
    return t.prototype.segmentPeople = function(t, e) {
        return D(this, void 0, void 0, function() {
            var n, r, i, o;
            return B(this, function(a) {
                switch(a.label){
                    case 0:
                        return t instanceof ImageBitmap && ((n = document.createElement("canvas")).getContext("2d").drawImage(t, 0, 0), t = n), e.segmentBodyParts ? e.multiSegmentation ? [
                            4,
                            this.bodyPixModel.segmentMultiPersonParts(t, e)
                        ] : [
                            3,
                            2
                        ] : [
                            3,
                            5
                        ];
                    case 1:
                        return i = a.sent(), [
                            3,
                            4
                        ];
                    case 2:
                        return [
                            4,
                            this.bodyPixModel.segmentPersonParts(t, e)
                        ];
                    case 3:
                        i = [
                            a.sent()
                        ], a.label = 4;
                    case 4:
                        return r = i.map(function(t) {
                            var e = t.data, n = t.width, r = t.height, i = new Uint8ClampedArray(n * r * 4).fill(0);
                            return e.forEach(function(t, e) {
                                -1 === t ? (i[4 * e] = $t.length, i[4 * e + 3] = 0) : (i[4 * e] = t, i[4 * e + 3] = 255);
                            }), {
                                maskValueToLabel: te,
                                mask: new Jt(new ImageData(i, n, r))
                            };
                        }), [
                            3,
                            10
                        ];
                    case 5:
                        return e.multiSegmentation ? [
                            4,
                            this.bodyPixModel.segmentMultiPerson(t, e)
                        ] : [
                            3,
                            7
                        ];
                    case 6:
                        return o = a.sent(), [
                            3,
                            9
                        ];
                    case 7:
                        return [
                            4,
                            this.bodyPixModel.segmentPerson(t, e)
                        ];
                    case 8:
                        o = [
                            a.sent()
                        ], a.label = 9;
                    case 9:
                        r = o.map(function(t) {
                            var e = t.data, n = t.width, r = t.height, i = new Uint8ClampedArray(n * r * 4).fill(0);
                            return e.forEach(function(t, e) {
                                0 === t ? (i[4 * e] = 0, i[4 * e + 3] = 0) : (i[4 * e] = 255, i[4 * e + 3] = 255);
                            }), {
                                maskValueToLabel: Zt,
                                mask: new Jt(new ImageData(i, n, r))
                            };
                        }), a.label = 10;
                    case 10:
                        return [
                            2,
                            r
                        ];
                }
            });
        });
    }, t.prototype.dispose = function() {
        this.bodyPixModel.dispose();
    }, t.prototype.reset = function() {}, t;
}();
function ne(t) {
    return D(this, void 0, void 0, function() {
        return B(this, function(e) {
            return [
                2,
                Gt(t).then(function(t) {
                    return new ee(t);
                })
            ];
        });
    });
}
var re = {
    runtime: "mediapipe",
    modelType: "general"
};
var ie = function() {
    function t(t) {
        this.mask = t;
    }
    return t.prototype.toCanvasImageSource = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    this.mask
                ];
            });
        });
    }, t.prototype.toImageData = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    W(this.mask)
                ];
            });
        });
    }, t.prototype.toTensor = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    j(this.mask)
                ];
            });
        });
    }, t.prototype.getUnderlyingType = function() {
        return "canvasimagesource";
    }, t;
}();
function oe(t) {
    return V(t), "person";
}
var ae = function() {
    function t(t) {
        var e, n = this;
        this.selfieMode = !1;
        var r;
        if (this.selfieSegmentationSolution = new (0, _selfieSegmentation.SelfieSegmentation)({
            locateFile: null !== (e = t.locateFile) && void 0 !== e ? e : function(e, n) {
                return t.solutionPath ? t.solutionPath.replace(/\/+$/, "") + "/" + e : n + "/" + e;
            }
        }), "landscape" === t.modelType) r = 1;
        else r = 0;
        this.selfieSegmentationSolution.setOptions({
            modelSelection: r,
            selfieMode: this.selfieMode
        }), this.selfieSegmentationSolution.onResults(function(t) {
            n.segmentation = [
                {
                    maskValueToLabel: oe,
                    mask: new ie(t.segmentationMask)
                }
            ];
        });
    }
    return t.prototype.segmentPeople = function(t, r) {
        return D(this, void 0, void 0, function() {
            var i, o;
            return B(this, function(a) {
                switch(a.label){
                    case 0:
                        return r && r.flipHorizontal && r.flipHorizontal !== this.selfieMode && (this.selfieMode = r.flipHorizontal, this.selfieSegmentationSolution.setOptions({
                            selfieMode: this.selfieMode
                        })), t instanceof (0, _tfjsCore.Tensor) ? (o = ImageData.bind, [
                            4,
                            (0, _tfjsCore.browser).toPixels(t)
                        ]) : [
                            3,
                            2
                        ];
                    case 1:
                        return i = new (o.apply(ImageData, [
                            void 0,
                            a.sent(),
                            t.shape[1],
                            t.shape[0]
                        ])), [
                            3,
                            3
                        ];
                    case 2:
                        i = t, a.label = 3;
                    case 3:
                        return t = i, [
                            4,
                            this.selfieSegmentationSolution.send({
                                image: t
                            })
                        ];
                    case 4:
                        return a.sent(), [
                            2,
                            this.segmentation
                        ];
                }
            });
        });
    }, t.prototype.dispose = function() {
        this.selfieSegmentationSolution.close();
    }, t.prototype.reset = function() {
        this.selfieSegmentationSolution.reset(), this.segmentation = null, this.selfieMode = !1;
    }, t.prototype.initialize = function() {
        return this.selfieSegmentationSolution.initialize();
    }, t;
}();
function se(t) {
    return D(this, void 0, void 0, function() {
        var e, n;
        return B(this, function(r) {
            switch(r.label){
                case 0:
                    return e = function(t) {
                        if (null == t) return C({}, re);
                        var e = C({}, t);
                        return e.runtime = "mediapipe", null == e.modelType && (e.modelType = re.modelType), e;
                    }(t), [
                        4,
                        (n = new ae(e)).initialize()
                    ];
                case 1:
                    return r.sent(), [
                        2,
                        n
                    ];
            }
        });
    });
}
function ue(t, e, n, r) {
    var i = t.width, o = t.height, a = r ? -1 : 1, s = Math.cos(t.rotation), u = Math.sin(t.rotation), f = t.xCenter, l = t.yCenter, c = 1 / e, d = 1 / n, h = new Array(16);
    return h[0] = i * s * a * c, h[1] = -o * u * c, h[2] = 0, h[3] = (-0.5 * i * s * a + .5 * o * u + f) * c, h[4] = i * u * a * d, h[5] = o * s * d, h[6] = 0, h[7] = (-0.5 * o * s - .5 * i * u * a + l) * d, h[8] = 0, h[9] = 0, h[10] = i * c, h[11] = 0, h[12] = 0, h[13] = 0, h[14] = 0, h[15] = 1, function(t) {
        if (16 !== t.length) throw new Error("Array length must be 16 but got " + t.length);
        return [
            [
                t[0],
                t[1],
                t[2],
                t[3]
            ],
            [
                t[4],
                t[5],
                t[6],
                t[7]
            ],
            [
                t[8],
                t[9],
                t[10],
                t[11]
            ],
            [
                t[12],
                t[13],
                t[14],
                t[15]
            ]
        ];
    }(h);
}
function fe(t) {
    return t instanceof (0, _tfjsCore.Tensor) ? {
        height: t.shape[0],
        width: t.shape[1]
    } : {
        height: t.height,
        width: t.width
    };
}
function le(t, e) {
    (0, _tfjsCore.util).assert(0 !== t.width, function() {
        return e + " width cannot be 0.";
    }), (0, _tfjsCore.util).assert(0 !== t.height, function() {
        return e + " height cannot be 0.";
    });
}
function ce(t, e) {
    var n = function(t, e, n, r) {
        var i = e - t, o = r - n;
        if (0 === i) throw new Error("Original min and max are both " + t + ", range cannot be 0.");
        var a = o / i;
        return {
            scale: a,
            offset: n - t * a
        };
    }(0, 255, e[0], e[1]);
    return (0, _tfjsCore.tidy)(function() {
        return (0, _tfjsCore.add)((0, _tfjsCore.mul)(t, n.scale), n.offset);
    });
}
function de(t, o, a) {
    var s = o.outputTensorSize, f = o.keepAspectRatio, l = o.borderMode, c = o.outputTensorFloatRange, d = fe(t), h = function(t, e) {
        return e ? {
            xCenter: e.xCenter * t.width,
            yCenter: e.yCenter * t.height,
            width: e.width * t.width,
            height: e.height * t.height,
            rotation: e.rotation
        } : {
            xCenter: .5 * t.width,
            yCenter: .5 * t.height,
            width: t.width,
            height: t.height,
            rotation: 0
        };
    }(d, a), p = function(t, e, n) {
        if (void 0 === n && (n = !1), !n) return {
            top: 0,
            left: 0,
            right: 0,
            bottom: 0
        };
        var r = e.height, i = e.width;
        le(e, "targetSize"), le(t, "roi");
        var o, a, s = r / i, u = t.height / t.width, f = 0, l = 0;
        return s > u ? (o = t.width, a = t.width * s, l = (1 - u / s) / 2) : (o = t.height / s, a = t.height, f = (1 - s / u) / 2), t.width = o, t.height = a, {
            top: l,
            left: f,
            right: f,
            bottom: l
        };
    }(h, s, f), m = ue(h, d.width, d.height, !1), g = (0, _tfjsCore.tidy)(function() {
        var r, o = (r = t) instanceof (0, _tfjsCore.Tensor) ? r : (0, _tfjsCore.browser).fromPixels(r), a = (0, _tfjsCore.tensor2d)(function(t, e, n) {
            return le(n, "inputResolution"), [
                1 / n.width * t[0][0] * e.width,
                1 / n.height * t[0][1] * e.width,
                t[0][3] * e.width,
                1 / n.width * t[1][0] * e.height,
                1 / n.height * t[1][1] * e.height,
                t[1][3] * e.height,
                0,
                0
            ];
        }(m, d, s), [
            1,
            8
        ]), f = "zero" === l ? "constant" : "nearest", h = (0, _tfjsCore.image).transform((0, _tfjsCore.expandDims)((0, _tfjsCore.cast)(o, "float32")), a, "bilinear", f, 0, [
            s.height,
            s.width
        ]);
        return null != c ? ce(h, c) : h;
    });
    return {
        imageTensor: g,
        padding: p,
        transformationMatrix: m
    };
}
function he(t, e, n) {
    return (0, _tfjsCore.tidy)(function() {
        var r = (0, _tfjsCore.squeeze)(t, [
            0
        ]), i = r.shape[2];
        if (1 === i) {
            var o = r;
            switch(e.activation){
                case "none":
                    break;
                case "sigmoid":
                    o = (0, _tfjsCore.sigmoid)(o);
                    break;
                case "softmax":
                    throw new Error("Softmax activation requires two channels.");
                default:
                    throw new Error("Activation not supported (" + e.activation + ")");
            }
            var a = n ? (0, _tfjsCore.image).resizeBilinear(o, [
                n.height,
                n.width
            ]) : o;
            return (0, _tfjsCore.squeeze)(a, [
                2
            ]);
        }
        throw new Error("Unsupported number of tensor channels " + i);
    });
}
var pe = {
    runtime: "tfjs",
    modelType: "general",
    modelUrl: "https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/general/1"
}, me = {
    flipHorizontal: !1
}, ge = {
    outputTensorSize: {
        width: 256,
        height: 256
    },
    keepAspectRatio: !1,
    borderMode: "zero",
    outputTensorFloatRange: [
        0,
        1
    ]
}, ve = {
    outputTensorSize: {
        width: 256,
        height: 144
    },
    keepAspectRatio: !1,
    borderMode: "zero",
    outputTensorFloatRange: [
        0,
        1
    ]
}, we = {
    activation: "none"
};
var ye = function() {
    function t(t) {
        this.mask = t;
    }
    return t.prototype.toCanvasImageSource = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    z(this.mask)
                ];
            });
        });
    }, t.prototype.toImageData = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    W(this.mask)
                ];
            });
        });
    }, t.prototype.toTensor = function() {
        return D(this, void 0, void 0, function() {
            return B(this, function(t) {
                return [
                    2,
                    this.mask
                ];
            });
        });
    }, t.prototype.getUnderlyingType = function() {
        return "tensor";
    }, t;
}();
function be(t) {
    return V(t), "person";
}
var Se, xe = function() {
    function t(t, e) {
        this.modelType = t, this.model = e;
    }
    return t.prototype.segmentPeople = function(t, e) {
        return D(this, void 0, void 0, function() {
            var n, i = this;
            return B(this, function(o) {
                return e = function(t) {
                    if (null == t) return C({}, me);
                    var e = C({}, t);
                    return null == e.flipHorizontal && (e.flipHorizontal = me.flipHorizontal), e;
                }(e), null == t ? (this.reset(), [
                    2,
                    []
                ]) : (n = (0, _tfjsCore.tidy)(function() {
                    var e = de(t, "general" === i.modelType ? ge : ve).imageTensor, n = (0, _tfjsCore.slice)(i.model.predict(e), [
                        0,
                        0,
                        0,
                        1
                    ], -1), r = fe(t), o = he(n, we, r), a = (0, _tfjsCore.expandDims)(o, 2), s = (0, _tfjsCore.pad)(a, [
                        [
                            0,
                            0
                        ],
                        [
                            0,
                            0
                        ],
                        [
                            0,
                            1
                        ]
                    ]);
                    return (0, _tfjsCore.mirrorPad)(s, [
                        [
                            0,
                            0
                        ],
                        [
                            0,
                            0
                        ],
                        [
                            0,
                            2
                        ]
                    ], "symmetric");
                }), [
                    2,
                    [
                        {
                            maskValueToLabel: be,
                            mask: new ye(n)
                        }
                    ]
                ]);
            });
        });
    }, t.prototype.dispose = function() {
        this.model.dispose();
    }, t.prototype.reset = function() {}, t;
}();
function Me(t) {
    return D(this, void 0, void 0, function() {
        var e, n, r;
        return B(this, function(i) {
            switch(i.label){
                case 0:
                    return e = function(t) {
                        if (null == t) return C({}, pe);
                        var e = C({}, t);
                        if (e.runtime = "tfjs", null == e.modelType && (e.modelType = pe.modelType), "general" !== e.modelType && "landscape" !== e.modelType) throw new Error("Model type must be one of general or landscape, but got " + e.modelType);
                        null == e.modelUrl && ("general" === e.modelType ? e.modelUrl = "https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/general/1" : e.modelUrl = "https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/landscape/1");
                        return e;
                    }(t), n = "string" == typeof e.modelUrl && e.modelUrl.indexOf("https://tfhub.dev") > -1, [
                        4,
                        (0, _tfjsConverter.loadGraphModel)(e.modelUrl, {
                            fromTFHub: n
                        })
                    ];
                case 1:
                    return r = i.sent(), [
                        2,
                        new xe(e.modelType, r)
                    ];
            }
        });
    });
}
function ke(t, e) {
    return D(this, void 0, void 0, function() {
        var n, r;
        return B(this, function(i) {
            switch(t){
                case Se.MediaPipeSelfieSegmentation:
                    if (n = void 0, null != (r = e)) {
                        if ("tfjs" === r.runtime) return [
                            2,
                            Me(r)
                        ];
                        if ("mediapipe" === r.runtime) return [
                            2,
                            se(r)
                        ];
                        n = r.runtime;
                    }
                    throw new Error("Expect modelConfig.runtime to be either 'tfjs' or 'mediapipe', but got " + n);
                case Se.BodyPix:
                    return [
                        2,
                        ne(r = e)
                    ];
                default:
                    throw new Error(t + " is not a supported model name.");
            }
        });
    });
}
!function(t) {
    t.BodyPix = "BodyPix", t.MediaPipeSelfieSegmentation = "MediaPipeSelfieSegmentation";
}(Se || (Se = {}));
var Ee = "blurred", Te = "blurred-mask", Pe = "mask", Ie = "lowres-part-mask", Oe = "draw-image", _e = {};
function He(t, e, n, r) {
    var i = t.width, o = t.height, a = e.width, s = e.height;
    if (i !== a || o !== s) throw new Error("error: dimensions must match. " + n + " has dimensions " + i + "x" + o + ", " + r + " has dimensions " + a + "x" + s);
}
function Ae(t) {
    if ("undefined" != typeof HTMLCanvasElement && t instanceof HTMLCanvasElement || "undefined" != typeof OffscreenCanvas && t instanceof OffscreenCanvas || "undefined" != typeof HTMLImageElement && t instanceof HTMLImageElement) return function(t) {
        if ("offsetHeight" in t && 0 !== t.offsetHeight && "offsetWidth" in t && 0 !== t.offsetWidth) return [
            t.offsetHeight,
            t.offsetWidth
        ];
        if (null != t.height && null != t.width) return [
            t.height,
            t.width
        ];
        throw new Error("HTMLImageElement must have height and width attributes set.");
    }(t);
    if ("undefined" != typeof ImageData && t instanceof ImageData) return [
        t.height,
        t.width
    ];
    if ("undefined" != typeof HTMLVideoElement && t instanceof HTMLVideoElement) return function(t) {
        return t.hasAttribute("height") && t.hasAttribute("width") ? [
            t.height,
            t.width
        ] : [
            t.videoHeight,
            t.videoWidth
        ];
    }(t);
    if (t instanceof (0, _tfjsCore.Tensor)) return [
        t.shape[0],
        t.shape[1]
    ];
    throw new Error("error: Unknown input type: " + t + ".");
}
function Re(t) {
    return _e[t] || (_e[t] = function() {
        if ("undefined" != typeof document) return document.createElement("canvas");
        if ("undefined" != typeof OffscreenCanvas) return new OffscreenCanvas(0, 0);
        throw new Error("Cannot create a canvas in this context");
    }()), _e[t];
}
function Fe(t, e) {
    var n = Re(e);
    return function(t, e) {
        e.width = t.width, e.height = t.height, e.getContext("2d").putImageData(t, 0, 0);
    }(t, n), n;
}
function Ce(t, r, i, o, a, s) {
    return D(this, void 0, void 0, function() {
        var u, f, l, c;
        return B(this, function(d) {
            switch(d.label){
                case 0:
                    return r instanceof (0, _tfjsCore.Tensor) ? [
                        4,
                        (0, _tfjsCore.browser).toPixels(r)
                    ] : [
                        3,
                        2
                    ];
                case 1:
                    u = d.sent(), f = Ae(r), l = f[0], c = f[1], r = new ImageData(u, c, l), d.label = 2;
                case 2:
                    return r instanceof ImageData && (r = Fe(r, Oe)), null == a || null == s ? t.drawImage(r, i, o) : t.drawImage(r, i, o, a, s), [
                        2
                    ];
            }
        });
    });
}
function De(t, e) {
    return D(this, void 0, void 0, function() {
        var n, r, i;
        return B(this, function(o) {
            switch(o.label){
                case 0:
                    return n = Ae(t), r = n[0], i = n[1], e.width = i, e.height = r, [
                        4,
                        Ce(e.getContext("2d"), t, 0, 0, i, r)
                    ];
                case 1:
                    return o.sent(), [
                        2
                    ];
            }
        });
    });
}
function Be(t) {
    var e = t.getContext("2d");
    e.scale(-1, 1), e.translate(-t.width, 0);
}
function Le(t, e, n) {
    return D(this, void 0, void 0, function() {
        return B(this, function(r) {
            switch(r.label){
                case 0:
                    return t.globalCompositeOperation = n, [
                        4,
                        Ce(t, e, 0, 0)
                    ];
                case 1:
                    return r.sent(), [
                        2
                    ];
            }
        });
    });
}
function ze(t, e, n) {
    return D(this, void 0, void 0, function() {
        var r, i, o, a, s, u, f, l;
        return B(this, function(c) {
            switch(c.label){
                case 0:
                    for(r = t.getContext("2d"), i = 0, o = 5, a = 1 / (2 * Math.PI * o * o), s = n < 3 ? 1 : 2, f = -n; f <= n; f += s)for(l = -n; l <= n; l += s)u = a * Math.exp(-(l * l + f * f) / (2 * o * o)), i += u;
                    f = -n, c.label = 1;
                case 1:
                    if (!(f <= n)) return [
                        3,
                        6
                    ];
                    l = -n, c.label = 2;
                case 2:
                    return l <= n ? (r.globalAlpha = a * Math.exp(-(l * l + f * f) / (2 * o * o)) / i * n, [
                        4,
                        Ce(r, e, l, f)
                    ]) : [
                        3,
                        5
                    ];
                case 3:
                    c.sent(), c.label = 4;
                case 4:
                    return l += s, [
                        3,
                        2
                    ];
                case 5:
                    return f += s, [
                        3,
                        1
                    ];
                case 6:
                    return r.globalAlpha = 1, [
                        2
                    ];
            }
        });
    });
}
function We(t, e, n) {
    return D(this, void 0, void 0, function() {
        var r, i, o, a;
        return B(this, function(s) {
            switch(s.label){
                case 0:
                    return r = Ae(t), i = r[0], o = r[1], a = n.getContext("2d"), n.width = o, n.height = i, a.clearRect(0, 0, o, i), a.save(), /^((?!chrome|android).)*safari/i.test(navigator.userAgent) ? [
                        4,
                        ze(n, t, e)
                    ] : [
                        3,
                        2
                    ];
                case 1:
                    return s.sent(), [
                        3,
                        4
                    ];
                case 2:
                    return a.filter = "blur(" + e + "px)", [
                        4,
                        Ce(a, t, 0, 0, o, i)
                    ];
                case 3:
                    s.sent(), s.label = 4;
                case 4:
                    return a.restore(), [
                        2
                    ];
            }
        });
    });
}
function je(t, e, n) {
    return D(this, void 0, void 0, function() {
        var r;
        return B(this, function(i) {
            switch(i.label){
                case 0:
                    return r = Re(n), 0 !== e ? [
                        3,
                        2
                    ] : [
                        4,
                        De(t, r)
                    ];
                case 1:
                    return i.sent(), [
                        3,
                        4
                    ];
                case 2:
                    return [
                        4,
                        We(t, e, r)
                    ];
                case 3:
                    i.sent(), i.label = 4;
                case 4:
                    return [
                        2,
                        r
                    ];
            }
        });
    });
}
function Ve(t, e, n, r, i, o) {
    void 0 === o && (o = {
        r: 0,
        g: 255,
        b: 255,
        a: 255
    });
    for(var a = -i; a <= i; a++)for(var s = -i; s <= i; s++)if (0 !== a && 0 !== s) {
        var u = (e + a) * r + (n + s);
        t[4 * u + 0] = o.r, t[4 * u + 1] = o.g, t[4 * u + 2] = o.b, t[4 * u + 3] = o.a;
    }
}
function Ke(t, e, n, r, i, o, a) {
    void 0 === a && (a = 1);
    for(var s = 0, u = -a; u <= a; u++)for(var f = -a; f <= a; f++)if (0 !== u && 0 !== f) {
        var l = (e + u) * r + (n + f);
        (!i[t[4 * l]] || t[4 * l + 3] < o) && (s += 1);
    }
    return s > 0;
}
function Ue(t, e, n, r, i, o) {
    return void 0 === e && (e = {
        r: 0,
        g: 0,
        b: 0,
        a: 0
    }), void 0 === n && (n = {
        r: 0,
        g: 0,
        b: 0,
        a: 255
    }), void 0 === r && (r = !1), void 0 === i && (i = .5), void 0 === o && (o = Array.from(Array(256).keys())), D(this, void 0, void 0, function() {
        var a, s, u, f, l, c, d, h, p, m, g, v, w, y;
        return B(this, function(b) {
            switch(b.label){
                case 0:
                    return 0 === (a = Array.isArray(t) ? t : [
                        t
                    ]).length ? [
                        2,
                        null
                    ] : [
                        4,
                        Promise.all(a.map(function(t) {
                            return t.mask.toImageData();
                        }))
                    ];
                case 1:
                    for(s = b.sent(), u = s[0], f = u.width, l = u.height, c = new Uint8ClampedArray(f * l * 4), d = Math.round(255 * i), h = new Array(256).fill(!1), o.forEach(function(t) {
                        return h[t] = !0;
                    }), p = 0; p < l; p++)for(m = 0; m < f; m++)for(c[4 * (g = p * f + m) + 0] = n.r, c[4 * g + 1] = n.g, c[4 * g + 2] = n.b, c[4 * g + 3] = n.a, v = 0, w = s; v < w.length; v++)y = w[v], h[y.data[4 * g]] && y.data[4 * g + 3] >= d && (c[4 * g] = e.r, c[4 * g + 1] = e.g, c[4 * g + 2] = e.b, c[4 * g + 3] = e.a, r && p - 1 >= 0 && p + 1 < l && m - 1 >= 0 && m + 1 < f && Ke(y.data, p, m, f, h, d) && Ve(c, p, m, f, 1));
                    return [
                        2,
                        new ImageData(c, f, l)
                    ];
            }
        });
    });
}
function qe(t, e, n, r) {
    return void 0 === n && (n = {
        r: 0,
        g: 0,
        b: 0,
        a: 255
    }), void 0 === r && (r = .5), D(this, void 0, void 0, function() {
        var i, o, a, s, u, f, l, c, d, h, p, m, g, v;
        return B(this, function(w) {
            switch(w.label){
                case 0:
                    return 0 === (i = Array.isArray(t) ? t : [
                        t
                    ]).length ? [
                        2,
                        null
                    ] : [
                        4,
                        Promise.all(i.map(function(t) {
                            return t.mask.toImageData();
                        }))
                    ];
                case 1:
                    for(o = w.sent(), a = o[0], s = a.width, u = a.height, f = new Uint8ClampedArray(s * u * 4), l = Math.round(255 * r), c = 0; c < u * s; ++c)for(f[(d = 4 * c) + 0] = n.r, f[d + 1] = n.g, f[d + 2] = n.b, f[d + 3] = n.a, h = 0, p = o; h < p.length; h++)(m = p[h]).data[d + 3] >= l && (g = m.data[d], v = e(g), f[d + 0] = v.r, f[d + 1] = v.g, f[d + 2] = v.b, f[d + 3] = v.a);
                    return [
                        2,
                        new ImageData(f, s, u)
                    ];
            }
        });
    });
}
function Ne(t, e, n, r, i, o) {
    return void 0 === r && (r = .7), void 0 === i && (i = 0), void 0 === o && (o = !1), D(this, void 0, void 0, function() {
        var a, s, u, f, l;
        return B(this, function(c) {
            switch(c.label){
                case 0:
                    return a = Ae(e), s = a[0], u = a[1], t.width = u, t.height = s, (f = t.getContext("2d")).save(), o && Be(t), [
                        4,
                        Ce(f, e, 0, 0)
                    ];
                case 1:
                    return c.sent(), f.globalAlpha = r, n ? (He({
                        width: u,
                        height: s
                    }, n, "image", "mask"), [
                        4,
                        je(Fe(n, Pe), i, Te)
                    ]) : [
                        3,
                        3
                    ];
                case 2:
                    l = c.sent(), f.drawImage(l, 0, 0, u, s), c.label = 3;
                case 3:
                    return f.restore(), [
                        2
                    ];
            }
        });
    });
}
function Qe(t, e, n, r, i, o, a) {
    return void 0 === r && (r = .7), void 0 === i && (i = 0), void 0 === o && (o = !1), void 0 === a && (a = 10), D(this, void 0, void 0, function() {
        var s, u, f, l, c, d, h;
        return B(this, function(p) {
            switch(p.label){
                case 0:
                    return s = Ae(e), u = s[0], He({
                        width: s[1],
                        height: u
                    }, n, "image", "mask"), [
                        4,
                        je(Fe(n, Pe), i, Te)
                    ];
                case 1:
                    for(f = p.sent(), t.width = f.width, t.height = f.height, (l = t.getContext("2d")).save(), o && Be(t), c = Re(Ie), d = c.getContext("2d"), c.width = f.width * (1 / a), c.height = f.height * (1 / a), d.drawImage(f, 0, 0, f.width, f.height, 0, 0, c.width, c.height), l.imageSmoothingEnabled = !1, l.drawImage(c, 0, 0, c.width, c.height, 0, 0, t.width, t.height), h = 0; h < c.width; h++)l.beginPath(), l.strokeStyle = "#ffffff", l.moveTo(a * h, 0), l.lineTo(a * h, t.height), l.stroke();
                    for(h = 0; h < c.height; h++)l.beginPath(), l.strokeStyle = "#ffffff", l.moveTo(0, a * h), l.lineTo(t.width, a * h), l.stroke();
                    return l.globalAlpha = 1 - r, [
                        4,
                        Ce(l, e, 0, 0, f.width, f.height)
                    ];
                case 2:
                    return p.sent(), l.restore(), [
                        2
                    ];
            }
        });
    });
}
function Xe(t, e, n) {
    return D(this, void 0, void 0, function() {
        var r, i;
        return B(this, function(o) {
            switch(o.label){
                case 0:
                    return [
                        4,
                        Ue(t, {
                            r: 0,
                            g: 0,
                            b: 0,
                            a: 255
                        }, {
                            r: 0,
                            g: 0,
                            b: 0,
                            a: 0
                        }, !1, e)
                    ];
                case 1:
                    return r = o.sent(), i = Fe(r, Pe), 0 === n ? [
                        2,
                        i
                    ] : [
                        2,
                        je(i, n, Te)
                    ];
            }
        });
    });
}
function Ye(t, e, n, r, i, o, a) {
    return void 0 === r && (r = .5), void 0 === i && (i = 3), void 0 === o && (o = 3), void 0 === a && (a = !1), D(this, void 0, void 0, function() {
        var s, u, f, l, c, d;
        return B(this, function(h) {
            switch(h.label){
                case 0:
                    return [
                        4,
                        je(e, i, Ee)
                    ];
                case 1:
                    return s = h.sent(), t.width = s.width, t.height = s.height, u = t.getContext("2d"), Array.isArray(n) && 0 === n.length ? (u.drawImage(s, 0, 0), [
                        2
                    ]) : [
                        4,
                        Xe(n, r, o)
                    ];
                case 2:
                    return f = h.sent(), u.save(), a && Be(t), l = Ae(e), c = l[0], d = l[1], [
                        4,
                        Ce(u, e, 0, 0, d, c)
                    ];
                case 3:
                    return h.sent(), [
                        4,
                        Le(u, f, "destination-in")
                    ];
                case 4:
                    return h.sent(), [
                        4,
                        Le(u, s, "destination-over")
                    ];
                case 5:
                    return h.sent(), u.restore(), [
                        2
                    ];
            }
        });
    });
}
function Ge(t, e, n, r) {
    return D(this, void 0, void 0, function() {
        var i, o;
        return B(this, function(a) {
            switch(a.label){
                case 0:
                    return [
                        4,
                        Ue(t, {
                            r: 0,
                            g: 0,
                            b: 0,
                            a: 0
                        }, {
                            r: 0,
                            g: 0,
                            b: 0,
                            a: 255
                        }, !0, n, e)
                    ];
                case 1:
                    return i = a.sent(), o = Fe(i, Pe), 0 === r ? [
                        2,
                        o
                    ] : [
                        2,
                        je(o, r, Te)
                    ];
            }
        });
    });
}
function $e(t, e, n, r, i, o, a, s) {
    return void 0 === i && (i = .5), void 0 === o && (o = 3), void 0 === a && (a = 3), void 0 === s && (s = !1), D(this, void 0, void 0, function() {
        var u, f, l, c, d, h;
        return B(this, function(p) {
            switch(p.label){
                case 0:
                    return [
                        4,
                        je(e, o, Ee)
                    ];
                case 1:
                    return u = p.sent(), t.width = u.width, t.height = u.height, f = t.getContext("2d"), Array.isArray(n) && 0 === n.length ? (f.drawImage(u, 0, 0), [
                        2
                    ]) : [
                        4,
                        Ge(n, r, i, a)
                    ];
                case 2:
                    return l = p.sent(), f.save(), s && Be(t), c = Ae(e), d = c[0], h = c[1], [
                        4,
                        Ce(f, e, 0, 0, h, d)
                    ];
                case 3:
                    return p.sent(), [
                        4,
                        Le(f, l, "destination-in")
                    ];
                case 4:
                    return p.sent(), [
                        4,
                        Le(f, u, "destination-over")
                    ];
                case 5:
                    return p.sent(), f.restore(), [
                        2
                    ];
            }
        });
    });
}

},{"@tensorflow/tfjs-core":"21Ftl","@tensorflow/tfjs-converter":"g4re1","@mediapipe/selfie_segmentation":"j6iw2","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g4re1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "GraphModel", ()=>(0, _graphModel.GraphModel));
parcelHelpers.export(exports, "loadGraphModel", ()=>(0, _graphModel.loadGraphModel));
parcelHelpers.export(exports, "loadGraphModelSync", ()=>(0, _graphModel.loadGraphModelSync));
parcelHelpers.export(exports, "deregisterOp", ()=>(0, _register.deregisterOp));
parcelHelpers.export(exports, "registerOp", ()=>(0, _register.registerOp));
parcelHelpers.export(exports, "version_converter", ()=>(0, _version.version));
var _flags = require("./flags");
var _graphModel = require("./executor/graph_model");
var _register = require("./operations/custom_op/register");
var _version = require("./version");

},{"./flags":"angKf","./executor/graph_model":"e4ep5","./operations/custom_op/register":"fBdpJ","./version":"by8XT","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"angKf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var _tfjsCore = require("@tensorflow/tfjs-core");
const ENV = (0, _tfjsCore.env)();
/** Whether to keep intermediate tensors. */ ENV.registerFlag("KEEP_INTERMEDIATE_TENSORS", ()=>false, (debugValue)=>{
    if (debugValue) console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
});

},{"@tensorflow/tfjs-core":"21Ftl"}],"e4ep5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "TFHUB_SEARCH_PARAM", ()=>TFHUB_SEARCH_PARAM);
parcelHelpers.export(exports, "DEFAULT_MODEL_NAME", ()=>DEFAULT_MODEL_NAME);
/**
 * A `tf.GraphModel` is a directed, acyclic graph built from a
 * SavedModel GraphDef and allows inference execution.
 *
 * A `tf.GraphModel` can only be created by loading from a model converted from
 * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
 * the command line converter tool and loaded via `tf.loadGraphModel`.
 *
 * @doc {heading: 'Models', subheading: 'Classes'}
 */ parcelHelpers.export(exports, "GraphModel", ()=>GraphModel);
/**
 * Load a graph model given a URL to the model definition.
 *
 * Example of loading MobileNetV2 from a URL and making a prediction with a
 * zeros input:
 *
 * ```js
 * const modelUrl =
 *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
 * const model = await tf.loadGraphModel(modelUrl);
 * const zeros = tf.zeros([1, 224, 224, 3]);
 * model.predict(zeros).print();
 * ```
 *
 * Example of loading MobileNetV2 from a TF Hub URL and making a prediction
 * with a zeros input:
 *
 * ```js
 * const modelUrl =
 *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
 * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
 * const zeros = tf.zeros([1, 224, 224, 3]);
 * model.predict(zeros).print();
 * ```
 * @param modelUrl The url or an `io.IOHandler` that loads the model.
 * @param options Options for the HTTP request, which allows to send
 *     credentials
 *    and custom headers.
 *
 * @doc {heading: 'Models', subheading: 'Loading'}
 */ parcelHelpers.export(exports, "loadGraphModel", ()=>loadGraphModel);
/**
 * Load a graph model given a synchronous IO handler with a 'load' method.
 *
 * @param modelSource The `io.IOHandlerSync` that loads the model, or the
 *     `io.ModelArtifacts` that encode the model, or a tuple of
 *     `[io.ModelJSON, ArrayBuffer]` of which the first element encodes the
 *      model and the second contains the weights.
 *
 * @doc {heading: 'Models', subheading: 'Loading'}
 */ parcelHelpers.export(exports, "loadGraphModelSync", ()=>loadGraphModelSync);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _operationMapper = require("../operations/operation_mapper");
var _graphExecutor = require("./graph_executor");
var _resourceManager = require("./resource_manager");
// tslint:disable-next-line: no-imports-from-dist
var _ioUtils = require("@tensorflow/tfjs-core/dist/io/io_utils");
const TFHUB_SEARCH_PARAM = "?tfjs-format=file";
const DEFAULT_MODEL_NAME = "model.json";
class GraphModel {
    // Returns the version information for the tensorflow model GraphDef.
    get modelVersion() {
        return this.version;
    }
    get inputNodes() {
        return this.executor.inputNodes;
    }
    get outputNodes() {
        return this.executor.outputNodes;
    }
    get inputs() {
        return this.executor.inputs;
    }
    get outputs() {
        return this.executor.outputs;
    }
    get weights() {
        return this.executor.weightMap;
    }
    get metadata() {
        return this.artifacts.userDefinedMetadata;
    }
    get modelSignature() {
        return this.signature;
    }
    get modelStructuredOutputKeys() {
        return this.structuredOutputKeys;
    }
    /**
     * @param modelUrl url for the model, or an `io.IOHandler`.
     * @param weightManifestUrl url for the weight file generated by
     * scripts/convert.py script.
     * @param requestOption options for Request, which allows to send credentials
     * and custom headers.
     * @param onProgress Optional, progress callback function, fired periodically
     * before the load is completed.
     */ constructor(modelUrl, loadOptions = {}, tfio = (0, _tfjsCore.io)){
        this.modelUrl = modelUrl;
        this.loadOptions = loadOptions;
        this.version = "n/a";
        this.io = tfio;
        if (loadOptions == null) this.loadOptions = {};
        this.resourceManager = new (0, _resourceManager.ResourceManager)();
    }
    findIOHandler() {
        const path = this.modelUrl;
        if (path.load != null) // Path is an IO Handler.
        this.handler = path;
        else if (this.loadOptions.requestInit != null) this.handler = this.io.browserHTTPRequest(path, this.loadOptions);
        else {
            const handlers = this.io.getLoadHandlers(path, this.loadOptions);
            if (handlers.length === 0) // For backward compatibility: if no load handler can be found,
            // assume it is a relative http path.
            handlers.push(this.io.browserHTTPRequest(path, this.loadOptions));
            else if (handlers.length > 1) throw new Error(`Found more than one (${handlers.length}) load handlers for ` + `URL '${[
                path
            ]}'`);
            this.handler = handlers[0];
        }
    }
    /**
     * Loads the model and weight files, construct the in memory weight map and
     * compile the inference graph.
     */ load() {
        this.findIOHandler();
        if (this.handler.load == null) throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
        const loadResult = this.handler.load();
        if ((0, _tfjsCore.util).isPromise(loadResult)) return loadResult.then((artifacts)=>{
            if (artifacts.getWeightStream == null) return this.loadSync(artifacts);
            return this.loadStreaming(artifacts);
        });
        return this.loadSync(loadResult);
    }
    /**
     * Synchronously construct the in memory weight map and
     * compile the inference graph.
     *
     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
     */ loadSync(artifacts) {
        const weightMap = this.io.decodeWeights(artifacts.weightData, artifacts.weightSpecs);
        return this.loadWithWeightMap(artifacts, weightMap);
    }
    async loadStreaming(artifacts) {
        if (artifacts.getWeightStream == null) throw new Error("Model artifacts missing streamWeights function");
        const weightMap = await (0, _ioUtils.decodeWeightsStream)(artifacts.getWeightStream(), artifacts.weightSpecs);
        return this.loadWithWeightMap(artifacts, weightMap);
    }
    loadWithWeightMap(artifacts, weightMap) {
        this.artifacts = artifacts;
        const graph = this.artifacts.modelTopology;
        let signature = this.artifacts.signature;
        if (this.artifacts.userDefinedMetadata != null) {
            const metadata = this.artifacts.userDefinedMetadata;
            if (metadata.signature != null) signature = metadata.signature;
            if (metadata.structuredOutputKeys != null) this.structuredOutputKeys = metadata.structuredOutputKeys;
        }
        this.signature = signature;
        this.version = `${graph.versions.producer}.${graph.versions.minConsumer}`;
        this.executor = new (0, _graphExecutor.GraphExecutor)((0, _operationMapper.OperationMapper).Instance.transformGraph(graph, this.signature));
        this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap);
        // Attach a model-level resourceManager to each executor to share resources,
        // such as `HashTable`.
        this.executor.resourceManager = this.resourceManager;
        if (artifacts.modelInitializer != null && artifacts.modelInitializer.node != null) {
            const initializer = (0, _operationMapper.OperationMapper).Instance.transformGraph(artifacts.modelInitializer);
            this.initializer = new (0, _graphExecutor.GraphExecutor)(initializer);
            this.initializer.weightMap = this.executor.weightMap;
            // Attach a model-level resourceManager to the initializer, the
            // hashTables created from when executing the initializer will be stored
            // in the resourceManager.
            this.initializer.resourceManager = this.resourceManager;
            this.initializerSignature = artifacts.initializerSignature;
        }
        return true;
    }
    /**
     * Save the configuration and/or weights of the GraphModel.
     *
     * An `IOHandler` is an object that has a `save` method of the proper
     * signature defined. The `save` method manages the storing or
     * transmission of serialized data ("artifacts") that represent the
     * model's topology and weights onto or via a specific medium, such as
     * file downloads, local storage, IndexedDB in the web browser and HTTP
     * requests to a server. TensorFlow.js provides `IOHandler`
     * implementations for a number of frequently used saving mediums, such as
     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
     * for more details.
     *
     * This method also allows you to refer to certain types of `IOHandler`s
     * as URL-like string shortcuts, such as 'localstorage://' and
     * 'indexeddb://'.
     *
     * Example 1: Save `model`'s topology and weights to browser [local
     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
     * then load it back.
     *
     * ```js
     * const modelUrl =
     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
     * const model = await tf.loadGraphModel(modelUrl);
     * const zeros = tf.zeros([1, 224, 224, 3]);
     * model.predict(zeros).print();
     *
     * const saveResults = await model.save('localstorage://my-model-1');
     *
     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
     * console.log('Prediction from loaded model:');
     * model.predict(zeros).print();
     * ```
     *
     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
     * scheme-based string shortcut for `IOHandler`.
     * @param config Options for saving the model.
     * @returns A `Promise` of `SaveResult`, which summarizes the result of
     * the saving, such as byte sizes of the saved artifacts for the model's
     *   topology and weight values.
     *
     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
     */ async save(handlerOrURL, config) {
        if (typeof handlerOrURL === "string") {
            const handlers = this.io.getSaveHandlers(handlerOrURL);
            if (handlers.length === 0) throw new Error(`Cannot find any save handlers for URL '${handlerOrURL}'`);
            else if (handlers.length > 1) throw new Error(`Found more than one (${handlers.length}) save handlers for ` + `URL '${handlerOrURL}'`);
            handlerOrURL = handlers[0];
        }
        if (handlerOrURL.save == null) throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
        return handlerOrURL.save(this.artifacts);
    }
    addStructuredOutputNames(outputTensors) {
        if (this.structuredOutputKeys) {
            const outputTensorsArray = outputTensors instanceof (0, _tfjsCore.Tensor) ? [
                outputTensors
            ] : outputTensors;
            const outputTensorMap = {};
            outputTensorsArray.forEach((outputTensor, i)=>outputTensorMap[this.structuredOutputKeys[i]] = outputTensor);
            return outputTensorMap;
        }
        return outputTensors;
    }
    /**
     * Execute the inference for the input tensors.
     *
     * @param input The input tensors, when there is single input for the model,
     * inputs param should be a `tf.Tensor`. For models with multiple inputs,
     * inputs params should be in either `tf.Tensor`[] if the input order is
     * fixed, or otherwise NamedTensorMap format.
     *
     * For model with multiple inputs, we recommend you use NamedTensorMap as the
     * input type, if you use `tf.Tensor`[], the order of the array needs to
     * follow the
     * order of inputNodes array. @see {@link GraphModel.inputNodes}
     *
     * You can also feed any intermediate nodes using the NamedTensorMap as the
     * input type. For example, given the graph
     *    InputNode => Intermediate => OutputNode,
     * you can execute the subgraph Intermediate => OutputNode by calling
     *    model.execute('IntermediateNode' : tf.tensor(...));
     *
     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
     * state needs to be fed manually.
     *
     * For batch inference execution, the tensors for each input need to be
     * concatenated together. For example with mobilenet, the required input shape
     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
     * If we are provide a batched data of 100 images, the input tensor should be
     * in the shape of [100, 244, 244, 3].
     *
     * @param config Prediction configuration for specifying the batch size.
     * Currently the batch size option is ignored for graph model.
     *
     * @returns Inference result tensors. If the model is converted and it
     * originally had structured_outputs in tensorflow, then a NamedTensorMap
     * will be returned matching the structured_outputs. If no structured_outputs
     * are present, the output will be single `tf.Tensor` if the model has single
     * output node, otherwise Tensor[].
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */ predict(inputs, config) {
        const outputTensors = this.execute(inputs, this.outputNodes);
        return this.addStructuredOutputNames(outputTensors);
    }
    /**
     * Execute the inference for the input tensors in async fashion, use this
     * method when your model contains control flow ops.
     *
     * @param input The input tensors, when there is single input for the model,
     * inputs param should be a `tf.Tensor`. For models with mutliple inputs,
     * inputs params should be in either `tf.Tensor`[] if the input order is
     * fixed, or otherwise NamedTensorMap format.
     *
     * For model with multiple inputs, we recommend you use NamedTensorMap as the
     * input type, if you use `tf.Tensor`[], the order of the array needs to
     * follow the
     * order of inputNodes array. @see {@link GraphModel.inputNodes}
     *
     * You can also feed any intermediate nodes using the NamedTensorMap as the
     * input type. For example, given the graph
     *    InputNode => Intermediate => OutputNode,
     * you can execute the subgraph Intermediate => OutputNode by calling
     *    model.execute('IntermediateNode' : tf.tensor(...));
     *
     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
     * state needs to be fed manually.
     *
     * For batch inference execution, the tensors for each input need to be
     * concatenated together. For example with mobilenet, the required input shape
     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
     * If we are provide a batched data of 100 images, the input tensor should be
     * in the shape of [100, 244, 244, 3].
     *
     * @param config Prediction configuration for specifying the batch size.
     * Currently the batch size option is ignored for graph model.
     *
     * @returns A Promise of inference result tensors. If the model is converted
     * and it originally had structured_outputs in tensorflow, then a
     * NamedTensorMap will be returned matching the structured_outputs. If no
     * structured_outputs are present, the output will be single `tf.Tensor` if
     * the model has single output node, otherwise Tensor[].
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */ async predictAsync(inputs, config) {
        const outputTensors = await this.executeAsync(inputs, this.outputNodes);
        return this.addStructuredOutputNames(outputTensors);
    }
    normalizeInputs(inputs) {
        var _a;
        if (!(inputs instanceof (0, _tfjsCore.Tensor)) && !Array.isArray(inputs)) {
            // The input is already a NamedTensorMap.
            const signatureInputs = (_a = this.signature) === null || _a === void 0 ? void 0 : _a.inputs;
            if (signatureInputs != null) for(const input in signatureInputs){
                const tensor = signatureInputs[input];
                if (tensor.resourceId != null) inputs[input] = this.resourceIdToCapturedInput[tensor.resourceId];
            }
            return inputs;
        }
        inputs = Array.isArray(inputs) ? inputs : [
            inputs
        ];
        const numCapturedInputs = Object.keys(this.resourceIdToCapturedInput).length;
        if (inputs.length + numCapturedInputs !== this.inputNodes.length) throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length - numCapturedInputs} non-resource placeholders, while there are ${inputs.length} input tensors provided.`);
        let inputIndex = 0;
        return this.inputNodes.reduce((map, inputName)=>{
            var _a, _b, _c;
            const resourceId = (_c = (_b = (_a = this.signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName]) === null || _c === void 0 ? void 0 : _c.resourceId;
            if (resourceId != null) map[inputName] = this.resourceIdToCapturedInput[resourceId];
            else map[inputName] = inputs[inputIndex++];
            return map;
        }, {});
    }
    normalizeOutputs(outputs) {
        outputs = outputs || this.outputNodes;
        return !Array.isArray(outputs) ? [
            outputs
        ] : outputs;
    }
    executeInitializerGraph() {
        if (this.initializer == null) return [];
        if (this.initializerSignature == null) return this.initializer.execute({}, []);
        else return this.initializer.execute({}, Object.keys(this.initializerSignature.outputs));
    }
    async executeInitializerGraphAsync() {
        if (this.initializer == null) return [];
        if (this.initializerSignature == null) return this.initializer.executeAsync({}, []);
        else return this.initializer.executeAsync({}, Object.keys(this.initializerSignature.outputs));
    }
    setResourceIdToCapturedInput(outputs) {
        this.resourceIdToCapturedInput = {};
        if (this.initializerSignature) {
            const signatureOutputs = this.initializerSignature.outputs;
            const outputNames = Object.keys(signatureOutputs);
            for(let i = 0; i < outputNames.length; i++){
                const outputName = outputNames[i];
                const tensorInfo = signatureOutputs[outputName];
                this.resourceIdToCapturedInput[tensorInfo.resourceId] = outputs[i];
            }
        }
    }
    /**
     * Executes inference for the model for given input tensors.
     * @param inputs tensor, tensor array or tensor map of the inputs for the
     * model, keyed by the input node names.
     * @param outputs output node name from the TensorFlow model, if no
     * outputs are specified, the default outputs of the model would be used.
     * You can inspect intermediate nodes of the model by adding them to the
     * outputs array.
     *
     * @returns A single tensor if provided with a single output or no outputs
     * are provided and there is only one default output, otherwise return a
     * tensor array. The order of the tensor array is the same as the outputs
     * if provided, otherwise the order of outputNodes attribute of the model.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */ execute(inputs, outputs) {
        if (this.resourceIdToCapturedInput == null) this.setResourceIdToCapturedInput(this.executeInitializerGraph());
        inputs = this.normalizeInputs(inputs);
        outputs = this.normalizeOutputs(outputs);
        const result = this.executor.execute(inputs, outputs);
        return result.length > 1 ? result : result[0];
    }
    /**
     * Executes inference for the model for given input tensors in async
     * fashion, use this method when your model contains control flow ops.
     * @param inputs tensor, tensor array or tensor map of the inputs for the
     * model, keyed by the input node names.
     * @param outputs output node name from the TensorFlow model, if no outputs
     * are specified, the default outputs of the model would be used. You can
     * inspect intermediate nodes of the model by adding them to the outputs
     * array.
     *
     * @returns A Promise of single tensor if provided with a single output or
     * no outputs are provided and there is only one default output, otherwise
     * return a tensor map.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */ async executeAsync(inputs, outputs) {
        if (this.resourceIdToCapturedInput == null) this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync());
        inputs = this.normalizeInputs(inputs);
        outputs = this.normalizeOutputs(outputs);
        const result = await this.executor.executeAsync(inputs, outputs);
        return result.length > 1 ? result : result[0];
    }
    /**
     * Get intermediate tensors for model debugging mode (flag
     * KEEP_INTERMEDIATE_TENSORS is true).
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */ getIntermediateTensors() {
        return this.executor.getIntermediateTensors();
    }
    /**
     * Dispose intermediate tensors for model debugging mode (flag
     * KEEP_INTERMEDIATE_TENSORS is true).
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */ disposeIntermediateTensors() {
        this.executor.disposeIntermediateTensors();
    }
    convertTensorMapToTensorsMap(map) {
        return Object.keys(map).reduce((newMap, key)=>{
            newMap[key] = [
                map[key]
            ];
            return newMap;
        }, {});
    }
    /**
     * Releases the memory used by the weight tensors and resourceManager.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */ dispose() {
        this.executor.dispose();
        if (this.initializer) {
            this.initializer.dispose();
            if (this.resourceIdToCapturedInput) (0, _tfjsCore.dispose)(this.resourceIdToCapturedInput);
        }
        this.resourceManager.dispose();
    }
}
async function loadGraphModel(modelUrl, options = {}, tfio = (0, _tfjsCore.io)) {
    if (modelUrl == null) throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
    if (options == null) options = {};
    if (options.fromTFHub && typeof modelUrl === "string") modelUrl = getTFHubUrl(modelUrl);
    const model = new GraphModel(modelUrl, options, tfio);
    await model.load();
    return model;
}
function loadGraphModelSync(modelSource) {
    if (modelSource == null) throw new Error("modelUrl in loadGraphModelSync() cannot be null. Please provide model artifacts or an IOHandler that loads the model");
    let ioHandler;
    if (modelSource instanceof Array) {
        const [modelJSON, weights] = modelSource;
        if (!modelJSON) throw new Error("modelJSON must be the first element of the array");
        if (!weights || !(weights instanceof ArrayBuffer)) throw new Error("An ArrayBuffer of weights must be the second element of the array");
        if (!("modelTopology" in modelJSON)) throw new Error("Model JSON is missing 'modelTopology'");
        if (!("weightsManifest" in modelJSON)) throw new Error("Model JSON is missing 'weightsManifest'");
        const weightSpecs = (0, _tfjsCore.io).getWeightSpecs(modelJSON.weightsManifest);
        const modelArtifacts = (0, _tfjsCore.io).getModelArtifactsForJSONSync(modelJSON, weightSpecs, weights);
        ioHandler = (0, _tfjsCore.io).fromMemorySync(modelArtifacts);
    } else if ("load" in modelSource) // Then modelSource is already an IOHandlerSync.
    ioHandler = modelSource;
    else if ("modelTopology" in modelSource && "weightSpecs" in modelSource && "weightData" in modelSource) // modelSource is of type ModelArtifacts.
    ioHandler = (0, _tfjsCore.io).fromMemorySync(modelSource);
    else throw new Error("Unknown model format");
    const model = new GraphModel(ioHandler);
    model.load();
    return model;
}
function getTFHubUrl(modelUrl) {
    if (!modelUrl.endsWith("/")) modelUrl = modelUrl + "/";
    return `${modelUrl}${DEFAULT_MODEL_NAME}${TFHUB_SEARCH_PARAM}`;
}

},{"@tensorflow/tfjs-core":"21Ftl","../operations/operation_mapper":"ewPe6","./graph_executor":"iu0wq","./resource_manager":"9Yk8c","@tensorflow/tfjs-core/dist/io/io_utils":"hW5iQ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ewPe6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "OperationMapper", ()=>OperationMapper);
parcelHelpers.export(exports, "decodeBase64", ()=>decodeBase64);
parcelHelpers.export(exports, "parseStringParam", ()=>parseStringParam);
parcelHelpers.export(exports, "getStringParam", ()=>getStringParam);
parcelHelpers.export(exports, "getBoolParam", ()=>getBoolParam);
parcelHelpers.export(exports, "getNumberParam", ()=>getNumberParam);
parcelHelpers.export(exports, "parseDtypeParam", ()=>parseDtypeParam);
parcelHelpers.export(exports, "getFuncParam", ()=>getFuncParam);
parcelHelpers.export(exports, "getDtypeParam", ()=>getDtypeParam);
parcelHelpers.export(exports, "getDtypeArrayParam", ()=>getDtypeArrayParam);
parcelHelpers.export(exports, "parseTensorShapeParam", ()=>parseTensorShapeParam);
parcelHelpers.export(exports, "getTensorShapeParam", ()=>getTensorShapeParam);
parcelHelpers.export(exports, "getNumericArrayParam", ()=>getNumericArrayParam);
parcelHelpers.export(exports, "getStringArrayParam", ()=>getStringArrayParam);
parcelHelpers.export(exports, "getTensorShapeArrayParam", ()=>getTensorShapeArrayParam);
parcelHelpers.export(exports, "getBoolArrayParam", ()=>getBoolArrayParam);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _compiledApi = require("../data/compiled_api");
var _register = require("./custom_op/register");
var _utils = require("./executors/utils");
var _arithmetic = require("./op_list/arithmetic");
var _basicMath = require("./op_list/basic_math");
var _control = require("./op_list/control");
var _convolution = require("./op_list/convolution");
var _creation = require("./op_list/creation");
var _dynamic = require("./op_list/dynamic");
var _evaluation = require("./op_list/evaluation");
var _graph = require("./op_list/graph");
var _hashTable = require("./op_list/hash_table");
var _image = require("./op_list/image");
var _logical = require("./op_list/logical");
var _matrices = require("./op_list/matrices");
var _normalization = require("./op_list/normalization");
var _reduction = require("./op_list/reduction");
var _sliceJoin = require("./op_list/slice_join");
var _sparse = require("./op_list/sparse");
var _spectral = require("./op_list/spectral");
var _string = require("./op_list/string");
var _transformation = require("./op_list/transformation");
var Buffer = require("509fe566240bdc8b").Buffer;
class OperationMapper {
    // Singleton instance for the mapper
    static get Instance() {
        return this._instance || (this._instance = new this());
    }
    // Loads the op mapping from the JSON file.
    constructor(){
        const ops = [
            _arithmetic,
            _basicMath,
            _control,
            _convolution,
            _creation,
            _dynamic,
            _evaluation,
            _graph,
            _hashTable,
            _image,
            _logical,
            _matrices,
            _normalization,
            _reduction,
            _sliceJoin,
            _sparse,
            _spectral,
            _string,
            _transformation
        ];
        const mappersJson = [].concat(...ops.map((op)=>op.json));
        this.opMappers = mappersJson.reduce((map, mapper)=>{
            map[mapper.tfOpName] = mapper;
            return map;
        }, {});
    }
    // Converts the model inference graph from Tensorflow GraphDef to local
    // representation for TensorFlow.js API
    transformGraph(graph, signature = {}) {
        const tfNodes = graph.node;
        const placeholders = [];
        const weights = [];
        const initNodes = [];
        const nodes = tfNodes.reduce((map, node)=>{
            map[node.name] = this.mapNode(node);
            if (node.op.startsWith("Placeholder")) placeholders.push(map[node.name]);
            else if (node.op === "Const") weights.push(map[node.name]);
            else if (node.input == null || node.input.length === 0) initNodes.push(map[node.name]);
            return map;
        }, {});
        let inputs = [];
        const outputs = [];
        let inputNodeNameToKey = {};
        let outputNodeNameToKey = {};
        if (signature != null) {
            inputNodeNameToKey = this.mapSignatureEntries(signature.inputs);
            outputNodeNameToKey = this.mapSignatureEntries(signature.outputs);
        }
        const allNodes = Object.keys(nodes);
        allNodes.forEach((key)=>{
            const node = nodes[key];
            node.inputNames.forEach((name, index)=>{
                const [nodeName, , outputName] = (0, _utils.getNodeNameAndIndex)(name);
                const inputNode = nodes[nodeName];
                if (inputNode.outputs != null) {
                    const outputIndex = inputNode.outputs.indexOf(outputName);
                    if (outputIndex !== -1) {
                        const inputName = `${nodeName}:${outputIndex}`;
                        // update the input name to use the mapped output index directly.
                        node.inputNames[index] = inputName;
                    }
                }
                node.inputs.push(inputNode);
                inputNode.children.push(node);
            });
        });
        // if signature has not outputs set, add any node that does not have
        // outputs.
        if (Object.keys(outputNodeNameToKey).length === 0) allNodes.forEach((key)=>{
            const node = nodes[key];
            if (node.children.length === 0) outputs.push(node);
        });
        else Object.keys(outputNodeNameToKey).forEach((name)=>{
            const [nodeName] = (0, _utils.getNodeNameAndIndex)(name);
            const node = nodes[nodeName];
            if (node != null) {
                node.signatureKey = outputNodeNameToKey[name];
                outputs.push(node);
            }
        });
        if (Object.keys(inputNodeNameToKey).length > 0) Object.keys(inputNodeNameToKey).forEach((name)=>{
            const [nodeName] = (0, _utils.getNodeNameAndIndex)(name);
            const node = nodes[nodeName];
            if (node) {
                node.signatureKey = inputNodeNameToKey[name];
                inputs.push(node);
            }
        });
        else inputs = placeholders;
        let functions = {};
        if (graph.library != null && graph.library.function != null) functions = graph.library.function.reduce((functions, func)=>{
            functions[func.signature.name] = this.mapFunction(func);
            return functions;
        }, {});
        const result = {
            nodes,
            inputs,
            outputs,
            weights,
            placeholders,
            signature,
            functions
        };
        if (initNodes.length > 0) result.initNodes = initNodes;
        return result;
    }
    mapSignatureEntries(entries) {
        return Object.keys(entries || {}).reduce((prev, curr)=>{
            prev[entries[curr].name] = curr;
            return prev;
        }, {});
    }
    mapNode(node) {
        // Unsupported ops will cause an error at run-time (not parse time), since
        // they may not be used by the actual execution subgraph.
        const mapper = (0, _register.getRegisteredOp)(node.op) || this.opMappers[node.op] || {};
        if (node.attr == null) node.attr = {};
        const newNode = {
            name: node.name,
            op: node.op,
            category: mapper.category,
            inputNames: (node.input || []).map((input)=>input.startsWith("^") ? input.slice(1) : input),
            inputs: [],
            children: [],
            inputParams: {},
            attrParams: {},
            rawAttrs: node.attr,
            outputs: mapper.outputs
        };
        if (mapper.inputs != null) newNode.inputParams = mapper.inputs.reduce((map, param)=>{
            map[param.name] = {
                type: param.type,
                inputIndexStart: param.start,
                inputIndexEnd: param.end
            };
            return map;
        }, {});
        if (mapper.attrs != null) newNode.attrParams = mapper.attrs.reduce((map, param)=>{
            const type = param.type;
            let value = undefined;
            switch(param.type){
                case "string":
                    value = getStringParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "string[]":
                    value = getStringArrayParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "number":
                    value = getNumberParam(node.attr, param.tfName, param.defaultValue || 0);
                    if (value === undefined && !!param.tfDeprecatedName) value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "number[]":
                    value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "bool":
                    value = getBoolParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "bool[]":
                    value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "shape":
                    value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "shape[]":
                    value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "dtype":
                    value = getDtypeParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "dtype[]":
                    value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "func":
                    value = getFuncParam(node.attr, param.tfName, param.defaultValue);
                    if (value === undefined && !!param.tfDeprecatedName) value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue);
                    break;
                case "tensor":
                case "tensors":
                    break;
                default:
                    throw new Error(`Unsupported param type: ${param.type} for op: ${node.op}`);
            }
            map[param.name] = {
                value,
                type
            };
            return map;
        }, {});
        return newNode;
    }
    // map the TFunctionDef to TFJS graph object
    mapFunction(functionDef) {
        const tfNodes = functionDef.nodeDef;
        const placeholders = [];
        const weights = [];
        let nodes = {};
        if (tfNodes != null) nodes = tfNodes.reduce((map, node)=>{
            map[node.name] = this.mapNode(node);
            if (node.op === "Const") weights.push(map[node.name]);
            return map;
        }, {});
        const inputs = [];
        const outputs = [];
        functionDef.signature.inputArg.forEach((arg)=>{
            const [nodeName] = (0, _utils.getNodeNameAndIndex)(arg.name);
            const node = {
                name: nodeName,
                op: "Placeholder",
                inputs: [],
                inputNames: [],
                category: "graph",
                inputParams: {},
                attrParams: {
                    dtype: {
                        value: parseDtypeParam(arg.type),
                        type: "dtype"
                    }
                },
                children: []
            };
            node.signatureKey = arg.name;
            inputs.push(node);
            nodes[nodeName] = node;
        });
        const allNodes = Object.keys(nodes);
        allNodes.forEach((key)=>{
            const node = nodes[key];
            node.inputNames.forEach((name, index)=>{
                const [nodeName, , outputName] = (0, _utils.getNodeNameAndIndex)(name);
                const inputNode = nodes[nodeName];
                if (inputNode.outputs != null) {
                    const outputIndex = inputNode.outputs.indexOf(outputName);
                    if (outputIndex !== -1) {
                        const inputName = `${nodeName}:${outputIndex}`;
                        // update the input name to use the mapped output index directly.
                        node.inputNames[index] = inputName;
                    }
                }
                node.inputs.push(inputNode);
                inputNode.children.push(node);
            });
        });
        const returnNodeMap = functionDef.ret;
        functionDef.signature.outputArg.forEach((output)=>{
            const [nodeName, index] = (0, _utils.getNodeNameAndIndex)(returnNodeMap[output.name]);
            const node = nodes[nodeName];
            if (node != null) {
                node.defaultOutput = index;
                outputs.push(node);
            }
        });
        const signature = this.mapArgsToSignature(functionDef);
        return {
            nodes,
            inputs,
            outputs,
            weights,
            placeholders,
            signature
        };
    }
    mapArgsToSignature(functionDef) {
        return {
            methodName: functionDef.signature.name,
            inputs: functionDef.signature.inputArg.reduce((map, arg)=>{
                map[arg.name] = this.mapArgToTensorInfo(arg);
                return map;
            }, {}),
            outputs: functionDef.signature.outputArg.reduce((map, arg)=>{
                map[arg.name] = this.mapArgToTensorInfo(arg, functionDef.ret);
                return map;
            }, {})
        };
    }
    mapArgToTensorInfo(arg, nameMap) {
        let name = arg.name;
        if (nameMap != null) name = nameMap[name];
        return {
            name,
            dtype: arg.type
        };
    }
}
function decodeBase64(text) {
    const global = (0, _tfjsCore.env)().global;
    if (typeof global.atob !== "undefined") return global.atob(text);
    else if (typeof Buffer !== "undefined") return new Buffer(text, "base64").toString();
    else throw new Error("Unable to decode base64 in this environment. Missing built-in atob() or Buffer()");
}
function parseStringParam(s, keepCase) {
    const value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s);
    return keepCase ? value : value.toLowerCase();
}
function getStringParam(attrs, name, def, keepCase = false) {
    const param = attrs[name];
    if (param != null) return parseStringParam(param.s, keepCase);
    return def;
}
function getBoolParam(attrs, name, def) {
    const param = attrs[name];
    return param ? param.b : def;
}
function getNumberParam(attrs, name, def) {
    const param = attrs[name] || {};
    const value = param["i"] != null ? param["i"] : param["f"] != null ? param["f"] : def;
    return typeof value === "number" ? value : parseInt(value, 10);
}
function parseDtypeParam(value) {
    if (typeof value === "string") // tslint:disable-next-line:no-any
    value = _compiledApi.DataType[value];
    switch(value){
        case _compiledApi.DataType.DT_FLOAT:
        case _compiledApi.DataType.DT_HALF:
            return "float32";
        case _compiledApi.DataType.DT_INT32:
        case _compiledApi.DataType.DT_INT64:
        case _compiledApi.DataType.DT_INT8:
        case _compiledApi.DataType.DT_UINT8:
            return "int32";
        case _compiledApi.DataType.DT_BOOL:
            return "bool";
        case _compiledApi.DataType.DT_DOUBLE:
            return "float32";
        case _compiledApi.DataType.DT_STRING:
            return "string";
        case _compiledApi.DataType.DT_COMPLEX64:
        case _compiledApi.DataType.DT_COMPLEX128:
            return "complex64";
        default:
            // Unknown dtype error will happen at runtime (instead of parse time),
            // since these nodes might not be used by the actual subgraph execution.
            return null;
    }
}
function getFuncParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.func) return param.func.name;
    return def;
}
function getDtypeParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.type) return parseDtypeParam(param.type);
    return def;
}
function getDtypeArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.list && param.list.type) return param.list.type.map((v)=>parseDtypeParam(v));
    return def;
}
function parseTensorShapeParam(shape) {
    if (shape.unknownRank) return undefined;
    if (shape.dim != null) return shape.dim.map((dim)=>typeof dim.size === "number" ? dim.size : parseInt(dim.size, 10));
    return [];
}
function getTensorShapeParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.shape) return parseTensorShapeParam(param.shape);
    return def;
}
function getNumericArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param) return ((param.list.f && param.list.f.length ? param.list.f : param.list.i) || []).map((v)=>typeof v === "number" ? v : parseInt(v, 10));
    return def;
}
function getStringArrayParam(attrs, name, def, keepCase = false) {
    const param = attrs[name];
    if (param && param.list && param.list.s) return param.list.s.map((v)=>{
        return parseStringParam(v, keepCase);
    });
    return def;
}
function getTensorShapeArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.list && param.list.shape) return param.list.shape.map((v)=>{
        return parseTensorShapeParam(v);
    });
    return def;
}
function getBoolArrayParam(attrs, name, def) {
    const param = attrs[name];
    if (param && param.list && param.list.b) return param.list.b;
    return def;
}

},{"509fe566240bdc8b":"kLBSi","@tensorflow/tfjs-core":"21Ftl","../data/compiled_api":"9JlGI","./custom_op/register":"fBdpJ","./executors/utils":"lA8AN","./op_list/arithmetic":"ji9iB","./op_list/basic_math":"jvEs5","./op_list/control":"lLjRP","./op_list/convolution":"6d1E7","./op_list/creation":"bqRRS","./op_list/dynamic":"dk8bW","./op_list/evaluation":"2nQsa","./op_list/graph":"5sTUq","./op_list/hash_table":"aL6VI","./op_list/image":"20Hqa","./op_list/logical":"lRZZd","./op_list/matrices":"fzvw9","./op_list/normalization":"5PcxC","./op_list/reduction":"aLLnY","./op_list/slice_join":"byP8Z","./op_list/sparse":"1gqiT","./op_list/spectral":"dJaL2","./op_list/string":"j8hv4","./op_list/transformation":"eSckc","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9JlGI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *
 * =============================================================================
 */ /** DataType enum. */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "DataType", ()=>DataType);
parcelHelpers.export(exports, "SaverDef", ()=>SaverDef);
var DataType;
(function(DataType) {
    // These properties must be quoted since they are used by parseDtypeParam
    // in tfjs-converter/src/operations/operation_mapper.ts to look up dtypes
    // by string name. If they are not quoted, Closure will mangle their names.
    // Not a legal value for DataType.  Used to indicate a DataType field
    // has not been set.
    DataType[DataType["DT_INVALID"] = 0] = "DT_INVALID";
    // Data types that all computation devices are expected to be
    // capable to support.
    DataType[DataType["DT_FLOAT"] = 1] = "DT_FLOAT";
    DataType[DataType["DT_DOUBLE"] = 2] = "DT_DOUBLE";
    DataType[DataType["DT_INT32"] = 3] = "DT_INT32";
    DataType[DataType["DT_UINT8"] = 4] = "DT_UINT8";
    DataType[DataType["DT_INT16"] = 5] = "DT_INT16";
    DataType[DataType["DT_INT8"] = 6] = "DT_INT8";
    DataType[DataType["DT_STRING"] = 7] = "DT_STRING";
    DataType[DataType["DT_COMPLEX64"] = 8] = "DT_COMPLEX64";
    DataType[DataType["DT_INT64"] = 9] = "DT_INT64";
    DataType[DataType["DT_BOOL"] = 10] = "DT_BOOL";
    DataType[DataType["DT_QINT8"] = 11] = "DT_QINT8";
    DataType[DataType["DT_QUINT8"] = 12] = "DT_QUINT8";
    DataType[DataType["DT_QINT32"] = 13] = "DT_QINT32";
    DataType[DataType["DT_BFLOAT16"] = 14] = "DT_BFLOAT16";
    DataType[DataType["DT_QINT16"] = 15] = "DT_QINT16";
    DataType[DataType["DT_QUINT16"] = 16] = "DT_QUINT16";
    DataType[DataType["DT_UINT16"] = 17] = "DT_UINT16";
    DataType[DataType["DT_COMPLEX128"] = 18] = "DT_COMPLEX128";
    DataType[DataType["DT_HALF"] = 19] = "DT_HALF";
    DataType[DataType["DT_RESOURCE"] = 20] = "DT_RESOURCE";
    DataType[DataType["DT_VARIANT"] = 21] = "DT_VARIANT";
    DataType[DataType["DT_UINT32"] = 22] = "DT_UINT32";
    DataType[DataType["DT_UINT64"] = 23] = "DT_UINT64";
    // Do not use!  These are only for parameters.  Every enum above
    // should have a corresponding value below (verified by types_test).
    DataType[DataType["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF";
    DataType[DataType["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF";
    DataType[DataType["DT_INT32_REF"] = 103] = "DT_INT32_REF";
    DataType[DataType["DT_UINT8_REF"] = 104] = "DT_UINT8_REF";
    DataType[DataType["DT_INT16_REF"] = 105] = "DT_INT16_REF";
    DataType[DataType["DT_INT8_REF"] = 106] = "DT_INT8_REF";
    DataType[DataType["DT_STRING_REF"] = 107] = "DT_STRING_REF";
    DataType[DataType["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF";
    DataType[DataType["DT_INT64_REF"] = 109] = "DT_INT64_REF";
    DataType[DataType["DT_BOOL_REF"] = 110] = "DT_BOOL_REF";
    DataType[DataType["DT_QINT8_REF"] = 111] = "DT_QINT8_REF";
    DataType[DataType["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF";
    DataType[DataType["DT_QINT32_REF"] = 113] = "DT_QINT32_REF";
    DataType[DataType["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF";
    DataType[DataType["DT_QINT16_REF"] = 115] = "DT_QINT16_REF";
    DataType[DataType["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF";
    DataType[DataType["DT_UINT16_REF"] = 117] = "DT_UINT16_REF";
    DataType[DataType["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF";
    DataType[DataType["DT_HALF_REF"] = 119] = "DT_HALF_REF";
    DataType[DataType["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF";
    DataType[DataType["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF";
    DataType[DataType["DT_UINT32_REF"] = 122] = "DT_UINT32_REF";
    DataType[DataType["DT_UINT64_REF"] = 123] = "DT_UINT64_REF";
})(DataType || (DataType = {}));
var SaverDef;
(function(SaverDef) {
    /** CheckpointFormatVersion enum. */ let CheckpointFormatVersion;
    (function(CheckpointFormatVersion) {
        CheckpointFormatVersion[CheckpointFormatVersion["LEGACY"] = 0] = "LEGACY";
        CheckpointFormatVersion[CheckpointFormatVersion["V1"] = 1] = "V1";
        CheckpointFormatVersion[CheckpointFormatVersion["V2"] = 2] = "V2";
    })(CheckpointFormatVersion = SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {}));
})(SaverDef || (SaverDef = {}));

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fBdpJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Register an Op for graph model executor. This allows you to register
 * TensorFlow custom op or override existing op.
 *
 * Here is an example of registering a new MatMul Op.
 * ```js
 * const customMatmul = (node) =>
 *    tf.matMul(
 *        node.inputs[0], node.inputs[1],
 *        node.attrs['transpose_a'], node.attrs['transpose_b']);
 *
 * tf.registerOp('MatMul', customMatmul);
 * ```
 * The inputs and attrs of the node object are based on the TensorFlow op
 * registry.
 *
 * @param name The Tensorflow Op name.
 * @param opFunc An op function which is called with the current graph node
 * during execution and needs to return a tensor or a list of tensors. The node
 * has the following attributes:
 *    - attr: A map from attribute name to its value
 *    - inputs: A list of input tensors
 *
 * @doc {heading: 'Models', subheading: 'Op Registry'}
 */ parcelHelpers.export(exports, "registerOp", ()=>registerOp);
/**
 * Retrieve the OpMapper object for the registered op.
 *
 * @param name The Tensorflow Op name.
 *
 * @doc {heading: 'Models', subheading: 'Op Registry'}
 */ parcelHelpers.export(exports, "getRegisteredOp", ()=>getRegisteredOp);
/**
 * Deregister the Op for graph model executor.
 *
 * @param name The Tensorflow Op name.
 *
 * @doc {heading: 'Models', subheading: 'Op Registry'}
 */ parcelHelpers.export(exports, "deregisterOp", ()=>deregisterOp);
const CUSTOM_OPS = {};
function registerOp(name, opFunc) {
    const opMapper = {
        tfOpName: name,
        category: "custom",
        inputs: [],
        attrs: [],
        customExecutor: opFunc
    };
    CUSTOM_OPS[name] = opMapper;
}
function getRegisteredOp(name) {
    return CUSTOM_OPS[name];
}
function deregisterOp(name) {
    delete CUSTOM_OPS[name];
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lA8AN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "getParamValue", ()=>getParamValue);
/**
 * Retrieve the tensor from tensorsMap based on input name.
 * @param name Node input name
 * @param tensorsMap Tensors map keyed by the node
 * @param context contains tensors and information for running the current node.
 * @param resourceManager Optional. Contains global resources of the model.
 */ parcelHelpers.export(exports, "getTensor", ()=>getTensor);
/**
 * Retrieve the tensors based on input name for current context.
 * @param name Node input name
 * @param tensorsMap Tensors map keyed by the node
 */ parcelHelpers.export(exports, "getTensorsForCurrentContext", ()=>getTensorsForCurrentContext);
/**
 * Returns the node name, outputName and index from the Node input name.
 * @param inputName The input name of the node, in format of
 * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is
 * default to 0.
 * If the input name contains output name i.e. StringSplit:indices:0, it will
 * return ['StringSplit', 0, 'indices'].
 */ parcelHelpers.export(exports, "getNodeNameAndIndex", ()=>getNodeNameAndIndex);
parcelHelpers.export(exports, "parseNodeName", ()=>parseNodeName);
parcelHelpers.export(exports, "split", ()=>split);
parcelHelpers.export(exports, "getPadding", ()=>getPadding);
/**
 *  Reuse the tensor if it is marked as keep, otherwise clone the tensor to
 *  avoid disposal. This is important for TensorArray and TensorList ops, since
 *  internally they use a tensor as the id for TensorArray and TensorList, and
 * to simplify lookup, they also use Tensor.id as the key to the internal map.
 * These id tensors have been marked as kept in the backend, we need avoid clone
 * them in order to create new Tensor.id.
 * @param tensor
 */ parcelHelpers.export(exports, "cloneTensor", ()=>cloneTensor);
var _tfjsCore = require("@tensorflow/tfjs-core");
function getParamValue(paramName, node, tensorMap, context, resourceManager) {
    const inputParam = node.inputParams[paramName];
    if (inputParam && inputParam.inputIndexStart !== undefined) {
        const start = inputParam.inputIndexStart;
        const end = inputParam.inputIndexEnd === 0 ? undefined : inputParam.inputIndexEnd === undefined ? start + 1 : inputParam.inputIndexEnd;
        const shiftedStart = start < 0 ? node.inputNames.length + start : start;
        if (inputParam.type === "tensor") return getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager);
        if (inputParam.type === "tensors") {
            // TODO(mattSoulanille): This filters out NoOp nodes during execution, but
            // these should really never be in the execution graph in the first place.
            // They're necessary for ordering the graph, but should not be visible
            // during execution. Perhaps have different sets of children, one for
            // control dependencies and another for real dependencies.
            const inputs = node.inputs.slice(start, end);
            const inputNames = node.inputNames.slice(start, end).filter((_name, index)=>{
                var _a;
                return ((_a = inputs[index]) === null || _a === void 0 ? void 0 : _a.op) !== "NoOp";
            });
            return inputNames.map((name)=>getTensor(name, tensorMap, context, resourceManager));
        }
        const tensor = getTensor(node.inputNames[shiftedStart], tensorMap, context, resourceManager);
        const data = tensor.dataSync();
        return inputParam.type === "number" ? data[0] : (0, _tfjsCore.util).toNestedArray(tensor.shape, data);
    }
    const attrParam = node.attrParams[paramName];
    return attrParam && attrParam.value;
}
function getTensor(name, tensorsMap, context, resourceManager) {
    const [nodeName, index] = parseNodeName(name, context);
    if (resourceManager != null) {
        const tensor = resourceManager.getHashTableHandleByName(nodeName);
        if (tensor != null) return tensor;
    }
    const contextId = context.currentContextIds.find((contextId)=>{
        return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)];
    });
    return contextId !== undefined ? tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : undefined;
}
function getTensorsForCurrentContext(name, tensorsMap, context) {
    return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)];
}
function getNodeNameAndIndex(inputName, context) {
    const [nodeName, index, outputName] = parseNodeName(inputName, context);
    return [
        getNodeNameWithContextId(nodeName, context && context.currentContextId),
        index,
        outputName
    ];
}
function getNodeNameWithContextId(name, contextId) {
    return !!contextId ? `${name}-${contextId}` : name;
}
function parseNodeName(name, context) {
    if (name === "") return [
        "",
        0,
        undefined
    ];
    const isCacheEnabled = context != null && context.parseNodeNameCache != null;
    if (isCacheEnabled) {
        const cachedResult = context.parseNodeNameCache.get(name);
        if (cachedResult != null) return cachedResult;
    }
    const parts = name.split(":");
    let result;
    if (parts.length === 1) result = [
        name,
        0,
        undefined
    ];
    else {
        const nodeName = parts[0];
        const outputName = parts.length === 3 ? parts[1] : undefined;
        const index = Number(parts[parts.length - 1]);
        result = [
            nodeName,
            index,
            outputName
        ];
    }
    if (isCacheEnabled) context.parseNodeNameCache.set(name, result);
    return result;
}
function split(arr, size) {
    const res = [];
    for(let i = 0; i < arr.length; i += size)res.push(arr.slice(i, i + size));
    return res;
}
function getPadding(node, tensorMap, context) {
    let pad = getParamValue("pad", node, tensorMap, context);
    if (pad === "explicit") {
        // This is 1d array, we need to convert it to 2d array
        pad = getParamValue("explicitPaddings", node, tensorMap, context);
        const explicitPadding = [
            [
                0,
                0
            ],
            [
                0,
                0
            ],
            [
                0,
                0
            ],
            [
                0,
                0
            ]
        ];
        for(let i = 0; i < 4; i++){
            explicitPadding[i][0] = pad[i * 2];
            explicitPadding[i][1] = pad[i * 2 + 1];
        }
        return explicitPadding;
    }
    return pad;
}
function cloneTensor(tensor) {
    return tensor.kept ? tensor : (0, _tfjsCore.clone)(tensor);
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ji9iB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "Add",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "AddV2",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "AddN",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "end": 0,
                "name": "tensors",
                "type": "tensors"
            }
        ]
    },
    {
        "tfOpName": "BiasAdd",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Sub",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "RealDiv",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Div",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "DivNoNan",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "FloorDiv",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Mul",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Maximum",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Minimum",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Pow",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "SquaredDifference",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Mod",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "FloorMod",
        "category": "arithmetic",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jvEs5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "Abs",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Acos",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Asin",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Atan",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Atan2",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "y",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Ceil",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "ClipByValue",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "clipValueMin",
                "type": "number"
            },
            {
                "start": 2,
                "name": "clipValueMax",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Complex",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "real",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "imag",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "ComplexAbs",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Cos",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Cosh",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Elu",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Exp",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Floor",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Log",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Imag",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "Tout",
                "name": "outputType",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Neg",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Real",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "Tout",
                "name": "outputType",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Prelu",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "alpha",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Relu",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Relu6",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Selu",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Sigmoid",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Sin",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Sinh",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Sqrt",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Rsqrt",
        "category": "basic_math",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Square",
        "category": "basic_math",
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                "type": "tensor"
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                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
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                "tfName": "T",
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                "name": "x",
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                "type": "tensor"
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        "attrs": [
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                "tfName": "alpha",
                "name": "alpha",
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                "defaultValue": 0.2
            },
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                "type": "dtype",
                "notSupported": true
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        ]
    },
    {
        "tfOpName": "IsNan",
        "category": "basic_math",
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                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
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        "tfOpName": "IsFinite",
        "category": "basic_math",
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                "name": "x",
                "type": "tensor"
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        ],
        "attrs": [
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                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
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    },
    {
        "tfOpName": "IsInf",
        "category": "basic_math",
        "inputs": [
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                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
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},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lLjRP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "EmptyTensorList",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "elementShape",
                "type": "shape"
            },
            {
                "start": 1,
                "name": "maxNumElements",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "LoopCond",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "pred",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "Switch",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "data",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "pred",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "Merge",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "end": 0,
                "name": "tensors",
                "type": "tensors"
            }
        ]
    },
    {
        "tfOpName": "Enter",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensor",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "frame_name",
                "name": "frameName",
                "type": "string"
            },
            {
                "tfName": "is_constant",
                "name": "isConstant",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "Exit",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensor",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
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        ]
    },
    {
        "tfOpName": "NextIteration",
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        "inputs": [
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                "start": 0,
                "name": "tensor",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "TensorArrayV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "size",
                "type": "number"
            }
        ],
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            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            },
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                "tfName": "element_shape",
                "name": "elementShape",
                "type": "shape"
            },
            {
                "tfName": "dynamic_size",
                "name": "dynamicSize",
                "type": "bool"
            },
            {
                "tfName": "clear_after_read",
                "name": "clearAfterRead",
                "type": "bool"
            },
            {
                "tfName": "identical_element_shapes",
                "name": "identicalElementShapes",
                "type": "bool"
            },
            {
                "tfName": "tensor_array_name",
                "name": "name",
                "type": "string"
            }
        ]
    },
    {
        "tfOpName": "TensorArrayWriteV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorArrayId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "index",
                "type": "number"
            },
            {
                "start": 2,
                "name": "tensor",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "flowIn",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "TensorArrayReadV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorArrayId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "index",
                "type": "number"
            },
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                "start": 2,
                "name": "flowIn",
                "type": "number"
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        ],
        "attrs": [
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "TensorArrayGatherV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorArrayId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "flowIn",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            },
            {
                "tfName": "element_shape",
                "name": "elementShape",
                "type": "shape"
            }
        ]
    },
    {
        "tfOpName": "TensorArrayScatterV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorArrayId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "tensor",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "flowIn",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "TensorArrayConcatV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorArrayId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "flowIn",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            },
            {
                "tfName": "element_shape_except0",
                "name": "elementShapeExcept0",
                "type": "shape",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "TensorArraySplitV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorArrayId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "tensor",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "lengths",
                "type": "number[]"
            },
            {
                "start": 3,
                "name": "flowIn",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "TensorArraySizeV3",
        "category": "control",
        "inputs": [
            {
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                "name": "tensorArrayId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "flowIn",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "TensorArrayCloseV3",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorArrayId",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "StatelessIf",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "cond",
                "type": "tensor"
            },
            {
                "start": 1,
                "end": 0,
                "name": "args",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "then_branch",
                "name": "thenBranch",
                "type": "func"
            },
            {
                "tfName": "else_branch",
                "name": "elseBranch",
                "type": "func"
            }
        ]
    },
    {
        "tfOpName": "If",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "cond",
                "type": "tensor"
            },
            {
                "start": 1,
                "end": 0,
                "name": "args",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "then_branch",
                "name": "thenBranch",
                "type": "func"
            },
            {
                "tfName": "else_branch",
                "name": "elseBranch",
                "type": "func"
            }
        ]
    },
    {
        "tfOpName": "StatelessWhile",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "end": 0,
                "name": "args",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "cond",
                "name": "cond",
                "type": "func"
            },
            {
                "tfName": "body",
                "name": "body",
                "type": "func"
            }
        ]
    },
    {
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                "end": 0,
                "name": "args",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "cond",
                "name": "cond",
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            },
            {
                "tfName": "body",
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            }
        ]
    },
    {
        "tfOpName": "TensorListScatter",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensor",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "elementShape",
                "type": "shape"
            }
        ],
        "attrs": [
            {
                "tfName": "element_dtype",
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    {
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        "inputs": [
            {
                "start": 0,
                "name": "tensor",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "elementShape",
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            },
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                "start": 3,
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                "type": "number"
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        ],
        "attrs": [
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
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        ]
    },
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        "tfOpName": "TensorListGather",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorListId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "elementShape",
                "type": "shape"
            }
        ],
        "attrs": [
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "TensorListGetItem",
        "category": "control",
        "inputs": [
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                "start": 0,
                "name": "tensorListId",
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        ],
        "attrs": [
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                "tfName": "element_dtype",
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        "tfOpName": "TensorListSetItem",
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                "name": "tensorListId",
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                "type": "dtype"
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    {
        "tfOpName": "TensorListReserve",
        "category": "control",
        "inputs": [
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                "start": 0,
                "name": "elementShape",
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            {
                "start": 1,
                "name": "numElements",
                "type": "number"
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        ],
        "attrs": [
            {
                "tfName": "element_dtype",
                "name": "elementDType",
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        ],
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            {
                "tfName": "element_dtype",
                "name": "elementDType",
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        "tfOpName": "TensorListStack",
        "category": "control",
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                "name": "tensorListId",
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                "start": 0,
                "name": "tensor",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "elementShape",
                "type": "shape"
            },
            {
                "start": 2,
                "name": "lengths",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
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        ]
    },
    {
        "tfOpName": "TensorListConcat",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorListId",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "element_shape",
                "name": "elementShape",
                "type": "shape"
            },
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
            }
        ]
    },
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                "start": 0,
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        ],
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            {
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                "name": "elementShape",
                "type": "shape"
            },
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "TensorListPopBack",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorListId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "elementShape",
                "type": "shape"
            }
        ],
        "attrs": [
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "TensorListPushBack",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorListId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "tensor",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "element_dtype",
                "name": "elementDType",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "TensorListLength",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorListId",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "TensorListResize",
        "category": "control",
        "inputs": [
            {
                "start": 0,
                "name": "tensorListId",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "size",
                "type": "number"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6d1E7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "AvgPool",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            },
            {
                "tfName": "ksize",
                "name": "kernelSize",
                "type": "number[]"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "MaxPool",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            },
            {
                "tfName": "ksize",
                "name": "kernelSize",
                "type": "number[]"
            },
            {
                "tfName": "explicit_paddings",
                "name": "explicitPaddings",
                "type": "number[]",
                "defaultValue": [],
                "notSupported": true
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "MaxPoolWithArgmax",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "ksize",
                "name": "kernelSize",
                "type": "number[]"
            },
            {
                "tfName": "include_batch_in_index",
                "name": "includeBatchInIndex",
                "type": "bool"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "AvgPool3D",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            },
            {
                "tfName": "ksize",
                "name": "kernelSize",
                "type": "number[]"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "MaxPool3D",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            },
            {
                "tfName": "ksize",
                "name": "kernelSize",
                "type": "number[]"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Conv1D",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "stride",
                "name": "stride",
                "type": "number"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "defaultValue": "NWC"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "dilation",
                "name": "dilation",
                "type": "number",
                "defaultValue": 1
            }
        ]
    },
    {
        "tfOpName": "Conv2D",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "useCudnnOnGpu",
                "name": "useCudnnOnGpu",
                "type": "bool"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "defaultValue": "NHWC"
            },
            {
                "tfName": "explicit_paddings",
                "name": "explicitPaddings",
                "type": "number[]",
                "defaultValue": []
            },
            {
                "tfName": "dilations",
                "name": "dilations",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "_FusedConv2D",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            },
            {
                "start": 2,
                "end": 0,
                "name": "args",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "num_args",
                "name": "numArgs",
                "type": "number"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "explicit_paddings",
                "name": "explicitPaddings",
                "type": "number[]",
                "defaultValue": []
            },
            {
                "tfName": "use_cudnn_on_gpu",
                "name": "useCudnnOnGpu",
                "type": "bool",
                "defaultValue": true
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "defaultValue": "NHWC"
            },
            {
                "tfName": "dilations",
                "name": "dilations",
                "type": "number[]",
                "defaultValue": [
                    1,
                    1,
                    1,
                    1
                ]
            },
            {
                "tfName": "fused_ops",
                "name": "fusedOps",
                "type": "string[]",
                "defaultValue": []
            },
            {
                "tfName": "epsilon",
                "name": "epsilon",
                "type": "number",
                "defaultValue": 0.0001
            },
            {
                "tfName": "leakyrelu_alpha",
                "name": "leakyreluAlpha",
                "type": "number",
                "defaultValue": 0.2
            }
        ]
    },
    {
        "tfOpName": "Conv2DBackpropInput",
        "category": "convolution",
        "inputs": [
            {
                "start": 2,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            },
            {
                "start": 0,
                "name": "outputShape",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            },
            {
                "tfName": "explicit_paddings",
                "name": "explicitPaddings",
                "type": "number[]",
                "defaultValue": []
            },
            {
                "tfName": "dilations",
                "name": "dilations",
                "type": "number[]",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "DepthwiseConv2d",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "input",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "defaultValue": "NHWC"
            },
            {
                "tfName": "explicit_paddings",
                "name": "explicitPaddings",
                "type": "number[]",
                "defaultValue": []
            },
            {
                "tfName": "dilations",
                "name": "dilations",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "DepthwiseConv2dNative",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "input",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "defaultValue": "NHWC"
            },
            {
                "tfName": "explicit_paddings",
                "name": "explicitPaddings",
                "type": "number[]",
                "defaultValue": []
            },
            {
                "tfName": "dilations",
                "name": "dilations",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "FusedDepthwiseConv2dNative",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            },
            {
                "start": 2,
                "end": 0,
                "name": "args",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "num_args",
                "name": "numArgs",
                "type": "number"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "defaultValue": "NHWC"
            },
            {
                "tfName": "dilations",
                "name": "dilations",
                "type": "number[]",
                "defaultValue": [
                    1,
                    1,
                    1,
                    1
                ]
            },
            {
                "tfName": "fused_ops",
                "name": "fusedOps",
                "type": "string[]",
                "defaultValue": []
            },
            {
                "tfName": "explicit_paddings",
                "name": "explicitPaddings",
                "type": "number[]",
                "defaultValue": []
            }
        ]
    },
    {
        "tfOpName": "Conv3D",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "defaultValue": "NHWC"
            },
            {
                "tfName": "dilations",
                "name": "dilations",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "Dilation2D",
        "category": "convolution",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "filter",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "strides",
                "name": "strides",
                "type": "number[]"
            },
            {
                "tfName": "rates",
                "name": "dilations",
                "type": "number[]"
            },
            {
                "tfName": "padding",
                "name": "pad",
                "type": "string"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bqRRS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "Fill",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "shape",
                "type": "number[]"
            },
            {
                "start": 1,
                "name": "value",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "LinSpace",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "start",
                "type": "number"
            },
            {
                "start": 1,
                "name": "stop",
                "type": "number"
            },
            {
                "start": 2,
                "name": "num",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "OneHot",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "indices",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "depth",
                "type": "number"
            },
            {
                "start": 2,
                "name": "onValue",
                "type": "number",
                "defaultValue": 1
            },
            {
                "start": 3,
                "name": "offValue",
                "type": "number",
                "defaultValue": 0
            }
        ],
        "attrs": [
            {
                "tfName": "axis",
                "name": "axis",
                "type": "number",
                "notSupported": true
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "Ones",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "shape",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "OnesLike",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "RandomStandardNormal",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "shape",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "seed",
                "name": "seed",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "seed2",
                "name": "seed2",
                "type": "number",
                "defaultValue": 0,
                "notSupported": true
            },
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            },
            {
                "tfName": "T",
                "name": "T",
                "type": "number",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "RandomUniform",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "shape",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "minval",
                "name": "minval",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "maxval",
                "name": "maxval",
                "type": "number",
                "defaultValue": 1
            },
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            },
            {
                "tfName": "seed",
                "name": "seed",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "seed2",
                "name": "seed2",
                "type": "number",
                "defaultValue": 0,
                "notSupported": true
            },
            {
                "tfName": "T",
                "name": "T",
                "type": "number",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "RandomUniformInt",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "shape",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "minval",
                "name": "minval",
                "type": "number"
            },
            {
                "tfName": "maxval",
                "name": "maxval",
                "type": "number"
            },
            {
                "tfName": "seed",
                "name": "seed",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "seed2",
                "name": "seed2",
                "type": "number",
                "defaultValue": 0,
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Range",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "start",
                "type": "number"
            },
            {
                "start": 1,
                "name": "stop",
                "type": "number"
            },
            {
                "start": 2,
                "name": "step",
                "type": "number",
                "defaultValue": 0
            }
        ],
        "attrs": [
            {
                "tfName": "Tidx",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "TruncatedNormal",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "shape",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "means",
                "name": "mean",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "stddev",
                "name": "stdDev",
                "type": "number",
                "defaultValue": 1
            },
            {
                "tfName": "seed",
                "name": "seed",
                "type": "number"
            },
            {
                "tfName": "seed2",
                "name": "seed2",
                "type": "number",
                "defaultValue": 0,
                "notSupported": true
            },
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            },
            {
                "tfName": "T",
                "name": "T",
                "type": "number",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Zeros",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "shape",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "ZerosLike",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "Multinomial",
        "category": "creation",
        "inputs": [
            {
                "start": 0,
                "name": "logits",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "numSamples",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "seed",
                "name": "seed",
                "type": "number"
            },
            {
                "tfName": "seed2",
                "name": "seed2",
                "type": "number"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            },
            {
                "tfName": "output_dtype",
                "name": "output_dtype",
                "type": "dtype"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dk8bW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "NonMaxSuppressionV2",
        "category": "dynamic",
        "inputs": [
            {
                "start": 0,
                "name": "boxes",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "scores",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "maxOutputSize",
                "type": "number"
            },
            {
                "start": 3,
                "name": "iouThreshold",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "NonMaxSuppressionV3",
        "category": "dynamic",
        "inputs": [
            {
                "start": 0,
                "name": "boxes",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "scores",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "maxOutputSize",
                "type": "number"
            },
            {
                "start": 3,
                "name": "iouThreshold",
                "type": "number"
            },
            {
                "start": 4,
                "name": "scoreThreshold",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "NonMaxSuppressionV4",
        "category": "dynamic",
        "inputs": [
            {
                "start": 0,
                "name": "boxes",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "scores",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "maxOutputSize",
                "type": "number"
            },
            {
                "start": 3,
                "name": "iouThreshold",
                "type": "number"
            },
            {
                "start": 4,
                "name": "scoreThreshold",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "T_threshold",
                "name": "threshold",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "pad_to_max_output_size",
                "name": "padToMaxOutputSize",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "NonMaxSuppressionV5",
        "category": "dynamic",
        "inputs": [
            {
                "start": 0,
                "name": "boxes",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "scores",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "maxOutputSize",
                "type": "number"
            },
            {
                "start": 3,
                "name": "iouThreshold",
                "type": "number"
            },
            {
                "start": 4,
                "name": "scoreThreshold",
                "type": "number"
            },
            {
                "start": 5,
                "name": "softNmsSigma",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "Where",
        "category": "dynamic",
        "inputs": [
            {
                "start": 0,
                "name": "condition",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "ListDiff",
        "category": "dynamic",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "y",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2nQsa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "LowerBound",
        "category": "evaluation",
        "inputs": [
            {
                "start": 0,
                "name": "sortedSequence",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "values",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "TopKV2",
        "category": "evaluation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "k",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "sorted",
                "name": "sorted",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "UpperBound",
        "category": "evaluation",
        "inputs": [
            {
                "start": 0,
                "name": "sortedSequence",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "values",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "Unique",
        "category": "evaluation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "UniqueV2",
        "category": "evaluation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5sTUq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "PlaceholderWithDefault",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "default",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "shape",
                "name": "shape",
                "type": "shape"
            },
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "Placeholder",
        "category": "graph",
        "attrs": [
            {
                "tfName": "shape",
                "name": "shape",
                "type": "shape"
            },
            {
                "tfName": "dtype",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "Const",
        "category": "graph"
    },
    {
        "tfOpName": "Identity",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "IdentityN",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "end": 0,
                "name": "x",
                "type": "tensors"
            }
        ]
    },
    {
        "tfOpName": "Snapshot",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "Rank",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "Size",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "Shape",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "ShapeN",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "end": 0,
                "name": "x",
                "type": "tensors"
            }
        ]
    },
    {
        "tfOpName": "Print",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "data",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "message",
                "name": "message",
                "type": "string"
            },
            {
                "tfName": "first_n",
                "name": "firstN",
                "type": "number",
                "notSupported": true
            },
            {
                "tfName": "summarize",
                "name": "summarize",
                "type": "number",
                "defaultValue": 3
            }
        ]
    },
    {
        "tfOpName": "NoOp",
        "category": "graph",
        "inputs": []
    },
    {
        "tfOpName": "StopGradient",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "FakeQuantWithMinMaxVars",
        "category": "graph",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "min",
                "name": "min",
                "type": "number"
            },
            {
                "tfName": "max",
                "name": "max",
                "type": "number"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aL6VI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "HashTable",
        "category": "hash_table",
        "inputs": [],
        "attrs": [
            {
                "tfName": "shared_name",
                "name": "sharedName",
                "type": "string"
            },
            {
                "tfName": "use_node_name_sharing",
                "name": "useNodeNameSharing",
                "type": "bool"
            },
            {
                "tfName": "key_dtype",
                "name": "keyDType",
                "type": "dtype"
            },
            {
                "tfName": "value_dtype",
                "name": "valueDType",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "HashTableV2",
        "category": "hash_table",
        "inputs": [],
        "attrs": [
            {
                "tfName": "shared_name",
                "name": "sharedName",
                "type": "string"
            },
            {
                "tfName": "use_node_name_sharing",
                "name": "useNodeNameSharing",
                "type": "bool"
            },
            {
                "tfName": "key_dtype",
                "name": "keyDType",
                "type": "dtype"
            },
            {
                "tfName": "value_dtype",
                "name": "valueDType",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "LookupTableImport",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "keys",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "values",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "Tin",
                "name": "tIn",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "Tout",
                "name": "tOut",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LookupTableImportV2",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "keys",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "values",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "Tin",
                "name": "tIn",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "Tout",
                "name": "tOut",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LookupTableFind",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "keys",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "defaultValue",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "Tin",
                "name": "tIn",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "Tout",
                "name": "tOut",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LookupTableFindV2",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "keys",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "defaultValue",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "Tin",
                "name": "tIn",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "Tout",
                "name": "tOut",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LookupTableSize",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "LookupTableSizeV2",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "InitializeTable",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "keys",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "values",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "InitializeTableV2",
        "category": "hash_table",
        "inputs": [
            {
                "start": 0,
                "name": "tableHandle",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "keys",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "values",
                "type": "tensor"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"20Hqa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "ResizeBilinear",
        "category": "image",
        "inputs": [
            {
                "start": 0,
                "name": "images",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "size",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "align_corners",
                "name": "alignCorners",
                "type": "bool"
            },
            {
                "tfName": "half_pixel_centers",
                "name": "halfPixelCenters",
                "type": "bool"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "ResizeNearestNeighbor",
        "category": "image",
        "inputs": [
            {
                "start": 0,
                "name": "images",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "size",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "align_corners",
                "name": "alignCorners",
                "type": "bool"
            },
            {
                "tfName": "half_pixel_centers",
                "name": "halfPixelCenters",
                "type": "bool"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "CropAndResize",
        "category": "image",
        "inputs": [
            {
                "start": 0,
                "name": "image",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "boxes",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "boxInd",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "cropSize",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "method",
                "name": "method",
                "type": "string"
            },
            {
                "tfName": "extrapolation_value",
                "name": "extrapolationValue",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "ImageProjectiveTransformV3",
        "category": "image",
        "inputs": [
            {
                "start": 0,
                "name": "images",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "transforms",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "outputShape",
                "type": "number[]"
            },
            {
                "start": 3,
                "name": "fillValue",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "interpolation",
                "name": "interpolation",
                "type": "string"
            },
            {
                "tfName": "fill_mode",
                "name": "fillMode",
                "type": "string"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lRZZd":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "Equal",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "NotEqual",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Greater",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "GreaterEqual",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Less",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LessEqual",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LogicalAnd",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LogicalNot",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LogicalOr",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Select",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "condition",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "SelectV2",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "condition",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "BitwiseAnd",
        "category": "logical",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "y",
                "type": "tensor"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fzvw9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "_FusedMatMul",
        "category": "matrices",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            },
            {
                "start": 2,
                "end": 0,
                "name": "args",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "num_args",
                "name": "numArgs",
                "type": "number"
            },
            {
                "tfName": "fused_ops",
                "name": "fusedOps",
                "type": "string[]",
                "defaultValue": []
            },
            {
                "tfName": "epsilon",
                "name": "epsilon",
                "type": "number",
                "defaultValue": 0.0001
            },
            {
                "tfName": "transpose_a",
                "name": "transposeA",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "transpose_b",
                "name": "transposeB",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "leakyrelu_alpha",
                "name": "leakyreluAlpha",
                "type": "number",
                "defaultValue": 0.2
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "MatMul",
        "category": "matrices",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "transpose_a",
                "name": "transposeA",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "transpose_b",
                "name": "transposeB",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "BatchMatMul",
        "category": "matrices",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "adj_x",
                "name": "transposeA",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "adj_y",
                "name": "transposeB",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "BatchMatMulV2",
        "category": "matrices",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "b",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "adj_x",
                "name": "transposeA",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "adj_y",
                "name": "transposeB",
                "type": "bool",
                "defaultValue": false
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Transpose",
        "category": "matrices",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "perm",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Einsum",
        "category": "matrices",
        "inputs": [
            {
                "start": 0,
                "end": 0,
                "name": "tensors",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "equation",
                "name": "equation",
                "type": "string"
            },
            {
                "tfName": "N",
                "name": "n",
                "type": "number",
                "defaultValue": 2
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "MatrixBandPart",
        "category": "matrices",
        "inputs": [
            {
                "start": 0,
                "name": "a",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "numLower",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "numUpper",
                "type": "tensor"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5PcxC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "EuclideanNorm",
        "category": "normalization",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool",
                "defaultValue": false
            }
        ]
    },
    {
        "tfOpName": "FusedBatchNorm",
        "category": "normalization",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "scale",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "offset",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "mean",
                "type": "tensor"
            },
            {
                "start": 4,
                "name": "variance",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "epsilon",
                "name": "epsilon",
                "type": "number",
                "defaultValue": 0.001
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "FusedBatchNormV2",
        "category": "normalization",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "scale",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "offset",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "mean",
                "type": "tensor"
            },
            {
                "start": 4,
                "name": "variance",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "epsilon",
                "name": "epsilon",
                "type": "number",
                "defaultValue": 0.001
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "FusedBatchNormV3",
        "category": "normalization",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "scale",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "offset",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "mean",
                "type": "tensor"
            },
            {
                "start": 4,
                "name": "variance",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "epsilon",
                "name": "epsilon",
                "type": "number",
                "defaultValue": 0.001
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "LRN",
        "category": "normalization",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "depth_radius",
                "name": "radius",
                "type": "number",
                "defaultValue": 5
            },
            {
                "tfName": "bias",
                "name": "bias",
                "type": "number",
                "defaultValue": 1
            },
            {
                "tfName": "alpha",
                "name": "alpha",
                "type": "number",
                "defaultValue": 1
            },
            {
                "tfName": "beta",
                "name": "beta",
                "type": "number",
                "defaultValue": 0.5
            }
        ]
    },
    {
        "tfOpName": "Softmax",
        "category": "normalization",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "LogSoftmax",
        "category": "normalization",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aLLnY":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "Bincount",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "size",
                "type": "number"
            },
            {
                "start": 2,
                "name": "weights",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "DenseBincount",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "size",
                "type": "number"
            },
            {
                "start": 2,
                "name": "weights",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "binary_output",
                "name": "binaryOutput",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "Max",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "Mean",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "Min",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "Sum",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "All",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "Any",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "ArgMax",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "ArgMin",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "Prod",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "keep_dims",
                "name": "keepDims",
                "type": "bool"
            },
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Cumprod",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "exclusive",
                "name": "exclusive",
                "type": "bool"
            },
            {
                "tfName": "reverse",
                "name": "reverse",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "Cumsum",
        "category": "reduction",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "exclusive",
                "name": "exclusive",
                "type": "bool"
            },
            {
                "tfName": "reverse",
                "name": "reverse",
                "type": "bool"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"byP8Z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "ConcatV2",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "end": -1,
                "name": "tensors",
                "type": "tensors"
            },
            {
                "start": -1,
                "name": "axis",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "N",
                "name": "n",
                "type": "number",
                "defaultValue": 2
            }
        ]
    },
    {
        "tfOpName": "Concat",
        "category": "slice_join",
        "inputs": [
            {
                "start": 1,
                "end": 0,
                "name": "tensors",
                "type": "tensors"
            },
            {
                "start": 0,
                "name": "axis",
                "type": "number"
            }
        ],
        "attrs": [
            {
                "tfName": "N",
                "name": "n",
                "type": "number",
                "defaultValue": 2
            }
        ]
    },
    {
        "tfOpName": "GatherV2",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "axis",
                "type": "number",
                "defaultValue": 0
            }
        ],
        "attrs": [
            {
                "tfName": "batch_dims",
                "name": "batchDims",
                "type": "number",
                "defaultValue": 0
            }
        ]
    },
    {
        "tfOpName": "Gather",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "validate_indices",
                "name": "validateIndices",
                "type": "bool",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Reverse",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "dims",
                "type": "bool[]"
            }
        ]
    },
    {
        "tfOpName": "ReverseV2",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "Slice",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "begin",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "size",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "StridedSlice",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "begin",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "end",
                "type": "number[]"
            },
            {
                "start": 3,
                "name": "strides",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "begin_mask",
                "name": "beginMask",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "end_mask",
                "name": "endMask",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "new_axis_mask",
                "name": "newAxisMask",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "ellipsis_mask",
                "name": "ellipsisMask",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "shrink_axis_mask",
                "name": "shrinkAxisMask",
                "type": "number",
                "defaultValue": 0
            }
        ]
    },
    {
        "tfOpName": "Pack",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "end": 0,
                "name": "tensors",
                "type": "tensors"
            }
        ],
        "attrs": [
            {
                "tfName": "axis",
                "name": "axis",
                "type": "number",
                "defaultValue": 0
            }
        ]
    },
    {
        "tfOpName": "Unpack",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "tensor",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "axis",
                "name": "axis",
                "type": "number",
                "defaultValue": 0
            },
            {
                "tfName": "num",
                "name": "num",
                "type": "number",
                "defaultValue": 0,
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "Tile",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "reps",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "Split",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "axis",
                "type": "number",
                "defaultValue": 0
            },
            {
                "start": 1,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "num_split",
                "name": "numOrSizeSplits",
                "type": "number",
                "defaultValue": 1
            }
        ]
    },
    {
        "tfOpName": "SplitV",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "numOrSizeSplits",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "axis",
                "type": "number",
                "defaultValue": 0
            }
        ]
    },
    {
        "tfOpName": "ScatterNd",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "indices",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "values",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "shape",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "GatherNd",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "SparseToDense",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "sparseIndices",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "outputShape",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "sparseValues",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "defaultValue",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "validate_indices",
                "name": "validateIndices",
                "type": "bool",
                "defaultValue": false,
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "TensorScatterUpdate",
        "category": "slice_join",
        "inputs": [
            {
                "start": 0,
                "name": "tensor",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "values",
                "type": "tensor"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1gqiT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "SparseFillEmptyRows",
        "category": "sparse",
        "inputs": [
            {
                "start": 0,
                "name": "indices",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "values",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "denseShape",
                "type": "tensor"
            },
            {
                "start": 3,
                "name": "defaultValue",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "SparseReshape",
        "category": "sparse",
        "inputs": [
            {
                "start": 0,
                "name": "inputIndices",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "inputShape",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "newShape",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "T",
                "name": "dtype",
                "type": "dtype",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "SparseSegmentMean",
        "category": "sparse",
        "inputs": [
            {
                "start": 0,
                "name": "data",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "segmentIds",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "SparseSegmentSum",
        "category": "sparse",
        "inputs": [
            {
                "start": 0,
                "name": "data",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "indices",
                "type": "tensor"
            },
            {
                "start": 2,
                "name": "segmentIds",
                "type": "tensor"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dJaL2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "FFT",
        "category": "spectral",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "IFFT",
        "category": "spectral",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ]
    },
    {
        "tfOpName": "RFFT",
        "category": "spectral",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "fft_length",
                "type": "number",
                "notSupported": true
            }
        ]
    },
    {
        "tfOpName": "IRFFT",
        "category": "spectral",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "fft_length",
                "type": "number",
                "notSupported": true
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"j8hv4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "StaticRegexReplace",
        "category": "string",
        "inputs": [
            {
                "start": 0,
                "name": "input",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "pattern",
                "name": "pattern",
                "type": "string"
            },
            {
                "tfName": "rewrite",
                "name": "rewrite",
                "type": "string"
            },
            {
                "tfName": "replace_global",
                "name": "replaceGlobal",
                "type": "bool"
            }
        ]
    },
    {
        "tfOpName": "StringNGrams",
        "category": "string",
        "inputs": [
            {
                "start": 0,
                "name": "data",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "dataSplits",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "separator",
                "name": "separator",
                "type": "string"
            },
            {
                "tfName": "ngram_widths",
                "name": "nGramWidths",
                "type": "number[]"
            },
            {
                "tfName": "left_pad",
                "name": "leftPad",
                "type": "string"
            },
            {
                "tfName": "right_pad",
                "name": "rightPad",
                "type": "string"
            },
            {
                "tfName": "pad_width",
                "name": "padWidth",
                "type": "number"
            },
            {
                "tfName": "preserve_short_sequences",
                "name": "preserveShortSequences",
                "type": "bool"
            }
        ],
        "outputs": [
            "ngrams",
            "ngrams_splits"
        ]
    },
    {
        "tfOpName": "StringSplit",
        "category": "string",
        "inputs": [
            {
                "start": 0,
                "name": "input",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "delimiter",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "skip_empty",
                "name": "skipEmpty",
                "type": "bool"
            }
        ],
        "outputs": [
            "indices",
            "values",
            "shape"
        ]
    },
    {
        "tfOpName": "StringToHashBucketFast",
        "category": "string",
        "inputs": [
            {
                "start": 0,
                "name": "input",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "num_buckets",
                "name": "numBuckets",
                "type": "number"
            }
        ]
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eSckc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "json", ()=>json);
const json = [
    {
        "tfOpName": "Cast",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "SrcT",
                "name": "sdtype",
                "type": "dtype",
                "notSupported": true
            },
            {
                "tfName": "DstT",
                "name": "dtype",
                "type": "dtype"
            }
        ]
    },
    {
        "tfOpName": "ExpandDims",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "axis",
                "type": "number"
            }
        ]
    },
    {
        "tfOpName": "MirrorPad",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "padding",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "mode",
                "name": "mode",
                "type": "string"
            }
        ]
    },
    {
        "tfOpName": "Pad",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "padding",
                "type": "number[]"
            }
        ],
        "attrs": [
            {
                "tfName": "constant_value",
                "name": "constantValue",
                "type": "number",
                "defaultValue": 0
            }
        ]
    },
    {
        "tfOpName": "PadV2",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "padding",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "constantValue",
                "type": "number",
                "defaultValue": 0
            }
        ]
    },
    {
        "tfOpName": "Reshape",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "shape",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "EnsureShape",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "shape",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "Squeeze",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "axis",
                "tfDeprecatedName": "squeeze_dims",
                "name": "axis",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "SpaceToBatchND",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "blockShape",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "paddings",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "BatchToSpaceND",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "blockShape",
                "type": "number[]"
            },
            {
                "start": 2,
                "name": "crops",
                "type": "number[]"
            }
        ]
    },
    {
        "tfOpName": "DepthToSpace",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            }
        ],
        "attrs": [
            {
                "tfName": "block_size",
                "name": "blockSize",
                "type": "number"
            },
            {
                "tfName": "data_format",
                "name": "dataFormat",
                "type": "string"
            }
        ]
    },
    {
        "tfOpName": "BroadcastTo",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "x",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "shape",
                "type": "number[]"
            }
        ],
        "attrs": []
    },
    {
        "tfOpName": "BroadcastArgs",
        "category": "transformation",
        "inputs": [
            {
                "start": 0,
                "name": "s0",
                "type": "tensor"
            },
            {
                "start": 1,
                "name": "s1",
                "type": "tensor"
            }
        ],
        "attrs": []
    }
];

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iu0wq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "GraphExecutor", ()=>GraphExecutor);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _utils = require("../operations/executors/utils");
var _operationExecutor = require("../operations/operation_executor");
var _executionContext = require("./execution_context");
var _modelAnalysis = require("./model_analysis");
class GraphExecutor {
    get weightIds() {
        return this.parent ? this.parent.weightIds : this._weightIds;
    }
    get functionExecutorMap() {
        return this.parent ? this.parent.functionExecutorMap : this._functionExecutorMap;
    }
    get weightMap() {
        return this.parent ? this.parent.weightMap : this._weightMap;
    }
    set weightMap(weightMap) {
        const weightIds = Object.keys(weightMap).map((key)=>weightMap[key].map((tensor)=>tensor.id));
        this._weightIds = [].concat(...weightIds);
        this._weightMap = weightMap;
    }
    /**
     * Set `ResourceManager` shared by executors of a model.
     * @param resourceManager: `ResourceManager` of the `GraphModel`.
     */ set resourceManager(resourceManager) {
        this._resourceManager = resourceManager;
    }
    get inputs() {
        return this._inputs.map((node)=>{
            return {
                name: node.name,
                shape: node.attrParams["shape"] ? node.attrParams["shape"].value : undefined,
                dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : undefined
            };
        });
    }
    get outputs() {
        return this._outputs.map((node)=>{
            return {
                name: node.name,
                shape: node.attrParams["shape"] ? node.attrParams["shape"].value : undefined,
                dtype: node.attrParams["dtype"] ? node.attrParams["dtype"].value : undefined
            };
        });
    }
    get inputNodes() {
        return this._inputs.map((node)=>node.signatureKey || node.name);
    }
    get outputNodes() {
        return this._outputs.map((node)=>{
            const name = node.signatureKey || node.name;
            return node.defaultOutput ? `${name}:${node.defaultOutput}` : name;
        });
    }
    get functions() {
        return Object.keys(this._functions).reduce((map, key)=>{
            map[key] = this._functions[key].signature;
            return map;
        }, {});
    }
    /**
     *
     * @param graph Graph the model or function graph to be executed.
     * @param parent When building function exector you need to set the parent
     * executor. Since the weights and function executor maps are set at parant
     * level, that function executor can access the function maps and weight maps
     * through the parent.
     */ constructor(graph, parent){
        this.graph = graph;
        this.parent = parent;
        this.compiledMap = new Map();
        this.parseNodeNameCache = new Map();
        this._weightMap = {};
        this.SEPARATOR = ",";
        this._functions = {};
        this._functionExecutorMap = {};
        this.keepIntermediateTensors = false;
        this._outputs = graph.outputs;
        this._inputs = graph.inputs;
        this._initNodes = graph.initNodes;
        this._signature = graph.signature;
        this._functions = graph.functions;
        // create sub-graph executors
        if (graph.functions != null) Object.keys(graph.functions).forEach((name)=>{
            this._functionExecutorMap[name] = new GraphExecutor(graph.functions[name], this);
        });
    }
    getCompilationKey(inputs, outputs) {
        const sortedInputs = inputs.map((node)=>node.name).sort();
        const sortedOutputs = outputs.map((node)=>node.name).sort();
        return sortedInputs.join(this.SEPARATOR) + "--" + sortedOutputs.join(this.SEPARATOR);
    }
    /**
     * Compiles the inference graph and returns the minimal set of nodes that are
     * required for execution, in the correct execution order.
     * @returns {Object} compilation The compile result.
     * @returns {Node[]} compilation.orderedNodes Nodes in the correct execution
     *     order.
     * @returns {Map<string, Node[]>} compilation.nodeLiveUntilMap A map from node
     *     to disposable nodes after its execution. That is, for a node `x`,
     *     `nodeLiveUntilMap[x]` indicates all nodes whose intermediate
     *     tensors should be disposed after `x` is executed.
     */ compile(inputs, outputs) {
        const executionInfo = (0, _modelAnalysis.getExecutionSubgraph)(inputs, outputs, this.weightMap, this._initNodes);
        const { missingInputs, dynamicNode, syncInputs } = executionInfo;
        if (dynamicNode != null) throw new Error(`This execution contains the node '${dynamicNode.name}', which has ` + `the dynamic op '${dynamicNode.op}'. Please use ` + `model.executeAsync() instead. Alternatively, to avoid the ` + `dynamic ops, specify the inputs [${syncInputs}]`);
        if (missingInputs.length > 0) {
            const outNames = outputs.map((n)=>n.name);
            const inNames = Object.keys(inputs);
            throw new Error(`Cannot compute the outputs [${outNames}] from the provided inputs ` + `[${inNames}]. Missing the following inputs: [${missingInputs}]`);
        }
        const orderedNodes = (0, _modelAnalysis.getNodesInTopologicalOrder)(this.graph, executionInfo);
        const nodeLiveUntilMap = (0, _modelAnalysis.getNodeLiveUntilMap)(orderedNodes);
        return {
            orderedNodes,
            nodeLiveUntilMap
        };
    }
    cloneAndKeepTensor(tensor) {
        if (tensor == null) return null;
        const clone = tensor.clone();
        // Keep the clone because`model.execute()` may be called within
        // a `tidy()`, but the user may inspect these tensors after the
        // tidy.
        (0, _tfjsCore.keep)(clone);
        return clone;
    }
    cloneTensorList(tensors) {
        if (!tensors) return null;
        const clonedTensor = tensors.map((tensor)=>{
            return this.cloneAndKeepTensor(tensor);
        });
        return clonedTensor;
    }
    cloneTensorMap(tensorsMap) {
        return Object.fromEntries(Object.entries(tensorsMap).map(([name, tensorsList])=>{
            return [
                name,
                this.cloneTensorList(tensorsList)
            ];
        }));
    }
    /**
     * Executes the inference for given input tensors.
     * @param inputs Tensor map for the model inputs, keyed by the input node
     * names.
     * @param outputs Optional. output node name from the Tensorflow model, if
     * no outputs are specified, the default outputs of the model would be used.
     * You can inspect intermediate nodes of the model by adding them to the
     * outputs array.
     */ execute(inputs, outputs) {
        // Dispose any tensors from a prior run to avoid leaking them.
        this.disposeIntermediateTensors();
        inputs = this.mapInputs(inputs);
        const names = Object.keys(inputs).sort();
        this.checkInputs(inputs);
        this.checkInputShapeAndType(inputs);
        outputs = this.mapOutputs(outputs);
        this.checkOutputs(outputs);
        const inputNodes = names.map((name)=>this.graph.nodes[(0, _utils.parseNodeName)(name)[0]]);
        const outputNodeNames = outputs.map((name)=>(0, _utils.parseNodeName)(name)[0]);
        const outputNodeNameSet = new Set(outputNodeNames);
        let outputNodes = outputNodeNames.map((name)=>this.graph.nodes[name]);
        // If no outputs are specified, then use the default outputs of the model.
        if (outputNodes.length === 0) outputNodes = this._outputs;
        const compilationKey = this.getCompilationKey(inputNodes, outputNodes);
        // Do nothing if the compiled graph cache contains the input.
        let compilation = this.compiledMap.get(compilationKey);
        if (compilation == null) {
            compilation = this.compile(inputs, outputNodes);
            this.compiledMap.set(compilationKey, compilation);
        }
        // Keep tensors if KEEP_INTERMEDIATE_TENSORS is on.
        try {
            this.keepIntermediateTensors = (0, _tfjsCore.env)().getBool("KEEP_INTERMEDIATE_TENSORS");
        } catch (e) {
            this.keepIntermediateTensors = false;
            console.warn(e.message);
        }
        const tensorArrayMap = {};
        const tensorListMap = {};
        return (0, _tfjsCore.tidy)(()=>{
            const context = new (0, _executionContext.ExecutionContext)(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache);
            const tensorsMap = Object.assign({}, this.weightMap);
            if (this.keepIntermediateTensors) this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);
            Object.keys(inputs).forEach((name)=>{
                const [nodeName, index] = (0, _utils.parseNodeName)(name, context);
                const tensors = [];
                tensors[index] = inputs[name];
                tensorsMap[nodeName] = tensors;
                if (this.keepIntermediateTensors) this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);
            });
            const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
            const { orderedNodes, nodeLiveUntilMap } = compilation;
            for (const node of orderedNodes){
                if (tensorsMap[node.name]) continue;
                const tensors = (0, _operationExecutor.executeOp)(node, tensorsMap, context, this._resourceManager);
                if ((0, _tfjsCore.util).isPromise(tensors)) throw new Error(`The execution of the op '${node.op}' returned a promise. ` + `Please use model.executeAsync() instead.`);
                tensorsMap[node.name] = tensors;
                if (this.keepIntermediateTensors) this.clonedTensorsMap[node.name] = this.cloneTensorList(tensors);
                this.checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorsMap, context, tensorsToKeep, outputNodeNameSet, nodeLiveUntilMap.get(node.name));
            }
            // dispose the context for the root executor
            if (this.parent == null) context.dispose(tensorsToKeep);
            return outputs.map((name)=>(0, _utils.getTensor)(name, tensorsMap, context));
        });
    }
    getFrozenTensorIds(tensorMap) {
        const ids = [].concat.apply([], Object.keys(tensorMap).map((key)=>tensorMap[key]).map((tensors)=>tensors.map((tensor)=>tensor.id)));
        return new Set(ids);
    }
    checkTensorForDisposal(nodeName, node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount) {
        // Skip output nodes and any control flow nodes, since its dependency is
        // tricky to track correctly.
        if ((0, _modelAnalysis.isControlFlow)(node) || outputNodeNameSet.has(nodeName)) return;
        for (const tensor of tensorMap[nodeName]){
            if (tensor == null) continue;
            intermediateTensorConsumerCount[tensor.id] = (intermediateTensorConsumerCount[tensor.id] || 0) + node.children.length;
        }
        for (const input of node.inputs){
            // Skip any control flow nodes, since its dependency is tricky to track
            // correctly.
            if ((0, _modelAnalysis.isControlFlow)(input)) continue;
            const tensors = (0, _utils.getTensorsForCurrentContext)(input.name, tensorMap, context);
            if (tensors == null) continue;
            for (const tensor of tensors){
                if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) continue;
                // Only intermediate nodes' tensors have counts set, not marked as
                // kept, and not in `tensorsToKeep`.
                // Input and weight nodes' tensors should exist in `tensorsToKeep`.
                // Output and control flow nodes' tensors should never have count set.
                const count = intermediateTensorConsumerCount[tensor.id];
                if (count === 1) {
                    tensor.dispose();
                    delete intermediateTensorConsumerCount[tensor.id];
                } else if (count != null) intermediateTensorConsumerCount[tensor.id]--;
            }
        }
    }
    checkTensorForDisposalWithNodeLiveUntilInfo(node, tensorMap, context, tensorsToKeep, outputNodeNameSet, liveUntilNodes) {
        function isNonDisposableNode(node) {
            // Skip output nodes and any control flow nodes, since its dependency is
            // tricky to track correctly.
            return (0, _modelAnalysis.isControlFlow)(node) || outputNodeNameSet.has(node.name);
        }
        if ((0, _modelAnalysis.isControlFlow)(node) || liveUntilNodes == null) return;
        for (const nodeToDispose of liveUntilNodes){
            if (isNonDisposableNode(nodeToDispose)) continue;
            const tensors = (0, _utils.getTensorsForCurrentContext)(nodeToDispose.name, tensorMap, context);
            for (const tensor of tensors){
                if (!tensor || tensor.kept || tensorsToKeep.has(tensor.id)) continue;
                tensor.dispose();
            }
        }
    }
    /**
     * Executes the inference for given input tensors in Async fashion.
     * @param inputs Tensor map for the model inputs, keyed by the input node
     * names.
     * @param outputs output node name from the Tensorflow model, if no outputs
     * are specified, the default outputs of the model would be used. You can
     * inspect intermediate nodes of the model by adding them to the outputs
     * array.
     */ async executeAsync(inputs, outputs) {
        return this._executeAsync(inputs, outputs);
    }
    disposeIntermediateTensors() {
        if (!this.clonedTensorsMap) return;
        Object.values(this.clonedTensorsMap).forEach((tensorsList)=>{
            for (const tensor of tensorsList)if (tensor && !tensor.isDisposed) tensor.dispose();
        });
        this.clonedTensorsMap = null;
    }
    getIntermediateTensors() {
        return this.clonedTensorsMap;
    }
    /**
     * Executes the inference for given input tensors in Async fashion.
     * @param inputs Tensor map for the model inputs, keyed by the input node
     * names.
     * @param outputs Optional. output node name from the Tensorflow model,
     * if no outputs are specified, the default outputs of the model would be
     * used. You can inspect intermediate nodes of the model by adding them to
     * the outputs array.
     * @param isFunctionExecution Optional. Flag for executing a function.
     * @param tensorArrayMap Optional, global TensorArray map by id. Used for
     * function execution.
     * @param tensorArrayMap Optional global TensorList map by id. Used for
     * function execution.
     */ async _executeAsync(inputs, outputs, isFunctionExecution = false, tensorArrayMap = {}, tensorListMap = {}) {
        // Dispose any tensors from a prior run to avoid leaking them.
        this.disposeIntermediateTensors();
        if (!isFunctionExecution) {
            inputs = this.mapInputs(inputs);
            this.checkInputs(inputs);
            this.checkInputShapeAndType(inputs);
            outputs = this.mapOutputs(outputs);
            this.checkOutputs(outputs);
        }
        // Keep tensors if KEEP_INTERMEDIATE_TENSORS is on.
        try {
            this.keepIntermediateTensors = (0, _tfjsCore.env)().getBool("KEEP_INTERMEDIATE_TENSORS");
        } catch (e) {
            this.keepIntermediateTensors = false;
            console.warn(e.message);
        }
        const context = new (0, _executionContext.ExecutionContext)(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap, this.parseNodeNameCache);
        if (this.keepIntermediateTensors) this.clonedTensorsMap = this.cloneTensorMap(this.weightMap);
        // Graph with control flow op requires runtime evaluation of the execution
        // order, while without control flow the execution order is pre-determined
        // in the compile method.
        const tensorsMap = await this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution);
        const results = outputs.map((name)=>(0, _utils.getTensor)(name, tensorsMap, context));
        // dispose all the intermediate tensors
        const outputIds = results.map((t)=>t.id);
        const inputIds = Object.keys(inputs).map((name)=>inputs[name].id);
        const keepIds = new Set([
            ...outputIds,
            ...inputIds,
            ...this.weightIds
        ]);
        Object.values(tensorsMap).forEach((tensorsList)=>{
            tensorsList.forEach((tensor)=>{
                if (tensor && !tensor.isDisposed && !keepIds.has(tensor.id)) tensor.dispose();
            });
        });
        // dispose the context for the root executor
        if (this.parent == null) context.dispose(keepIds);
        return results;
    }
    async executeFunctionAsync(inputs, tensorArrayMap, tensorListMap) {
        const mappedInputs = inputs.reduce((map, tensor, index)=>{
            map[this.inputs[index].name] = tensor;
            return map;
        }, {});
        return this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap);
    }
    /**
     * When there are control flow nodes in the graph, the graph execution use
     * ExecutionContext to keep track of the frames and loop iterators.
     * @param inputs placeholder tensors for the graph.
     * @param context the execution context object for current execution.
     * @param outputNames Optional. output node name from the Tensorflow model,
     * if no outputs are specified, the default outputs of the model would be
     * used. You can inspect intermediate nodes of the model by adding them to
     * the outputs array.
     * @param isFunctionExecution Flag for executing a function.
     */ async executeWithControlFlow(inputs, context, outputNames, isFunctionExecution) {
        const names = Object.keys(inputs);
        const inputNodes = names.map((name)=>this.graph.nodes[(0, _utils.parseNodeName)(name)[0]]);
        const outputNodeNames = outputNames.map((name)=>(0, _utils.parseNodeName)(name)[0]);
        const outputNodeNameSet = new Set(outputNodeNames);
        let outputNodes = outputNodeNames.map((name)=>this.graph.nodes[name]);
        // If no outputs are specified, then use the default outputs of the model.
        if (outputNodes.length === 0) outputNodes = this._outputs;
        const { usedNodes, missingInputs, dynamicNode, syncInputs } = (0, _modelAnalysis.getExecutionSubgraph)(inputs, outputNodes, this.weightMap, this._initNodes);
        // First nodes to execute include inputNodes, weights, and initNodes.
        const stack = [
            ...inputNodes,
            ...this.graph.weights,
            ...this._initNodes || []
        ].map((node)=>{
            return {
                node,
                contexts: context.currentContext
            };
        });
        const tensorsMap = Object.assign({}, this.weightMap);
        Object.keys(inputs).forEach((name)=>{
            const [nodeName, index] = (0, _utils.parseNodeName)(name);
            const tensors = [];
            tensors[index] = inputs[name];
            tensorsMap[nodeName] = tensors;
        });
        const intermediateTensorConsumerCount = {};
        const tensorsToKeep = this.getFrozenTensorIds(tensorsMap);
        const added = {};
        while(stack.length > 0){
            const promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes);
            await Promise.all(promises);
        }
        if (dynamicNode == null && !isFunctionExecution) console.warn(`This model execution did not contain any nodes with control flow ` + `or dynamic output shapes. You can use model.execute() instead.`);
        const missingOutputs = outputNodes.filter((node)=>!(0, _modelAnalysis.isControlFlow)(node) && !(0, _utils.getTensor)(node.name, tensorsMap, context)).map((node)=>node.name);
        if (missingOutputs.length > 0) {
            let alternativeMsg = "";
            if (dynamicNode != null) alternativeMsg = `Alternatively, to avoid the dynamic ops, use model.execute() ` + `and specify the inputs [${syncInputs}]`;
            throw new Error(`Cannot compute the outputs [${missingOutputs}] from the provided ` + `inputs [${names}]. Consider providing the following inputs: ` + `[${missingInputs}]. ${alternativeMsg}`);
        }
        return tensorsMap;
    }
    processStack(inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount, usedNodes) {
        const promises = [];
        while(stack.length > 0){
            const item = stack.pop();
            context.currentContext = item.contexts;
            let nodeName = "";
            // The tensor of the Enter op with isConstant set should be set
            // in the parent scope, so it will be available as constant for the
            // whole loop.
            if (item.node.op === "Enter" && (0, _utils.getParamValue)("isConstant", item.node, tensorMap, context)) [nodeName] = (0, _utils.getNodeNameAndIndex)(item.node.name, context);
            // only process nodes that are not in the tensorMap yet, this include
            // inputNodes and internal initNodes.
            if (tensorMap[item.node.name] == null) {
                const tensors = (0, _operationExecutor.executeOp)(item.node, tensorMap, context, this._resourceManager);
                if (!nodeName) [nodeName] = (0, _utils.getNodeNameAndIndex)(item.node.name, context);
                const currentContext = context.currentContext;
                if ((0, _tfjsCore.util).isPromise(tensors)) promises.push(tensors.then((t)=>{
                    tensorMap[nodeName] = t;
                    if (this.keepIntermediateTensors) this.clonedTensorsMap[nodeName] = this.cloneTensorList(t);
                    context.currentContext = currentContext;
                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount);
                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
                    return t;
                }));
                else {
                    tensorMap[nodeName] = tensors;
                    if (this.keepIntermediateTensors) this.clonedTensorsMap[nodeName] = this.cloneTensorList(tensors);
                    this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNodeNameSet, intermediateTensorConsumerCount);
                    this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
                }
            } else this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes);
        }
        return promises;
    }
    processChildNodes(node, stack, context, tensorMap, added, usedNodes) {
        node.children.forEach((childNode)=>{
            const [nodeName] = (0, _utils.getNodeNameAndIndex)(childNode.name, context);
            if (added[nodeName] || !usedNodes.has(childNode.name)) return;
            // Merge op can be pushed if any of its inputs has value.
            if (childNode.op === "Merge") {
                if (childNode.inputNames.some((name)=>{
                    return !!(0, _utils.getTensor)(name, tensorMap, context);
                })) {
                    added[nodeName] = true;
                    stack.push({
                        contexts: context.currentContext,
                        node: childNode
                    });
                }
            } else if (childNode.inputNames.every((name)=>{
                return !!(0, _utils.getTensor)(name, tensorMap, context);
            })) {
                added[nodeName] = true;
                stack.push({
                    contexts: context.currentContext,
                    node: childNode
                });
            }
        });
    }
    /**
     * Releases the memory used by the weight tensors.
     */ dispose() {
        Object.keys(this.weightMap).forEach((key)=>this.weightMap[key].forEach((tensor)=>tensor.dispose()));
    }
    checkInputShapeAndType(inputs) {
        Object.keys(inputs).forEach((name)=>{
            const input = inputs[name];
            const [nodeName] = (0, _utils.parseNodeName)(name);
            const node = this.graph.nodes[nodeName];
            if (node.attrParams["shape"] && node.attrParams["shape"].value) {
                const shape = node.attrParams["shape"].value;
                const match = shape.length === input.shape.length && input.shape.every((dim, index)=>shape[index] === -1 || shape[index] === dim);
                (0, _tfjsCore.util).assert(match, ()=>`The shape of dict['${node.name}'] provided in ` + `model.execute(dict) must be [${shape}], but was ` + `[${input.shape}]`);
            }
            if (node.attrParams["dtype"] && node.attrParams["dtype"].value) (0, _tfjsCore.util).assert(input.dtype === node.attrParams["dtype"].value, ()=>`The dtype of dict['${node.name}'] provided in ` + `model.execute(dict) must be ` + `${node.attrParams["dtype"].value}, but was ${input.dtype}`);
        });
    }
    mapInputs(inputs) {
        var _a, _b;
        const result = {};
        for(const inputName in inputs){
            const tensor = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.inputs) === null || _b === void 0 ? void 0 : _b[inputName];
            if (tensor != null) result[tensor.name] = inputs[inputName];
            else result[inputName] = inputs[inputName];
        }
        return result;
    }
    checkInputs(inputs) {
        const notInGraph = Object.keys(inputs).filter((name)=>{
            const [nodeName] = (0, _utils.parseNodeName)(name);
            return this.graph.nodes[nodeName] == null;
        });
        if (notInGraph.length > 0) throw new Error(`The dict provided in model.execute(dict) has ` + `keys: [${notInGraph}] that are not part of graph`);
    }
    mapOutputs(outputs) {
        return outputs.map((name)=>{
            var _a, _b;
            const tensor = (_b = (_a = this._signature) === null || _a === void 0 ? void 0 : _a.outputs) === null || _b === void 0 ? void 0 : _b[name];
            if (tensor != null) return tensor.name;
            return name;
        }, {});
    }
    checkOutputs(outputs) {
        outputs.forEach((name)=>{
            const [normalizedName] = (0, _utils.parseNodeName)(name);
            if (!this.graph.nodes[normalizedName]) throw new Error(`The output '${name}' is not found in the graph`);
        });
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","../operations/executors/utils":"lA8AN","../operations/operation_executor":"4Dits","./execution_context":"gK8yG","./model_analysis":"kpmwj","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4Dits":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Executes the op defined by the node object.
 * @param node
 * @param tensorMap contains tensors for executed nodes and weights
 * @param context contains tensors and information for running the current node.
 * @param resourceManager Optional. Contains global resources of the model.
 */ parcelHelpers.export(exports, "executeOp", ()=>executeOp);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _nodeValueImpl = require("./custom_op/node_value_impl");
var _register = require("./custom_op/register");
var _arithmeticExecutor = require("./executors/arithmetic_executor");
var _basicMathExecutor = require("./executors/basic_math_executor");
var _controlExecutor = require("./executors/control_executor");
var _convolutionExecutor = require("./executors/convolution_executor");
var _creationExecutor = require("./executors/creation_executor");
var _dynamicExecutor = require("./executors/dynamic_executor");
var _evaluationExecutor = require("./executors/evaluation_executor");
var _graphExecutor = require("./executors/graph_executor");
var _hashTableExecutor = require("./executors/hash_table_executor");
var _imageExecutor = require("./executors/image_executor");
var _logicalExecutor = require("./executors/logical_executor");
var _matricesExecutor = require("./executors/matrices_executor");
var _normalizationExecutor = require("./executors/normalization_executor");
var _raggedExecutor = require("./executors/ragged_executor");
var _reductionExecutor = require("./executors/reduction_executor");
var _sliceJoinExecutor = require("./executors/slice_join_executor");
var _sparseExecutor = require("./executors/sparse_executor");
var _spectralExecutor = require("./executors/spectral_executor");
var _stringExecutor = require("./executors/string_executor");
var _transformationExecutor = require("./executors/transformation_executor");
function executeOp(node, tensorMap, context, resourceManager, tidy = _tfjsCore.tidy) {
    const value = ((node, tensorMap, context)=>{
        switch(node.category){
            case "arithmetic":
                return tidy(()=>_arithmeticExecutor.executeOp(node, tensorMap, context));
            case "basic_math":
                return tidy(()=>_basicMathExecutor.executeOp(node, tensorMap, context));
            case "control":
                return _controlExecutor.executeOp(node, tensorMap, context);
            case "convolution":
                return tidy(()=>_convolutionExecutor.executeOp(node, tensorMap, context));
            case "creation":
                return tidy(()=>_creationExecutor.executeOp(node, tensorMap, context));
            case "dynamic":
                return _dynamicExecutor.executeOp(node, tensorMap, context);
            case "evaluation":
                return tidy(()=>_evaluationExecutor.executeOp(node, tensorMap, context));
            case "image":
                return tidy(()=>_imageExecutor.executeOp(node, tensorMap, context));
            case "graph":
                return tidy(()=>_graphExecutor.executeOp(node, tensorMap, context));
            case "logical":
                return tidy(()=>_logicalExecutor.executeOp(node, tensorMap, context));
            case "matrices":
                return tidy(()=>_matricesExecutor.executeOp(node, tensorMap, context));
            case "normalization":
                return tidy(()=>_normalizationExecutor.executeOp(node, tensorMap, context));
            case "ragged":
                return tidy(()=>_raggedExecutor.executeOp(node, tensorMap, context));
            case "reduction":
                return tidy(()=>_reductionExecutor.executeOp(node, tensorMap, context));
            case "slice_join":
                return tidy(()=>_sliceJoinExecutor.executeOp(node, tensorMap, context));
            case "sparse":
                return tidy(()=>_sparseExecutor.executeOp(node, tensorMap, context));
            case "spectral":
                return tidy(()=>_spectralExecutor.executeOp(node, tensorMap, context));
            case "string":
                return tidy(()=>_stringExecutor.executeOp(node, tensorMap, context));
            case "transformation":
                return tidy(()=>_transformationExecutor.executeOp(node, tensorMap, context));
            case "hash_table":
                return _hashTableExecutor.executeOp(node, tensorMap, context, resourceManager);
            case "custom":
                const opMapper = (0, _register.getRegisteredOp)(node.op);
                if (opMapper && opMapper.customExecutor) return opMapper.customExecutor(new (0, _nodeValueImpl.NodeValueImpl)(node, tensorMap, context));
                else throw TypeError(`Custom op ${node.op} is not registered.`);
            default:
                throw TypeError(`Unknown op '${node.op}'. File an issue at ` + `https://github.com/tensorflow/tfjs/issues so we can add it` + `, or register a custom execution with tf.registerOp()`);
        }
    })(node, tensorMap, context);
    if (_tfjsCore.util.isPromise(value)) return value.then((data)=>[].concat(data));
    return [].concat(value);
}

},{"@tensorflow/tfjs-core":"21Ftl","./custom_op/node_value_impl":"af83H","./custom_op/register":"fBdpJ","./executors/arithmetic_executor":"8J0UN","./executors/basic_math_executor":"j2IHU","./executors/control_executor":"1V4dK","./executors/convolution_executor":"b2H1R","./executors/creation_executor":"kSSii","./executors/dynamic_executor":"37XKU","./executors/evaluation_executor":"9cjVI","./executors/graph_executor":"jqg8t","./executors/hash_table_executor":"9g7sC","./executors/image_executor":"5sGLS","./executors/logical_executor":"30ObX","./executors/matrices_executor":"hCdlL","./executors/normalization_executor":"iGve1","./executors/ragged_executor":"aEvoT","./executors/reduction_executor":"bj9OH","./executors/slice_join_executor":"3HYHo","./executors/sparse_executor":"cwC1Y","./executors/spectral_executor":"jduek","./executors/string_executor":"4NdVa","./executors/transformation_executor":"3LErR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"af83H":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Helper class for lookup inputs and params for nodes in the model graph.
 */ parcelHelpers.export(exports, "NodeValueImpl", ()=>NodeValueImpl);
var _utils = require("../executors/utils");
var _operationMapper = require("../operation_mapper");
class NodeValueImpl {
    constructor(node, tensorMap, context){
        this.node = node;
        this.tensorMap = tensorMap;
        this.context = context;
        this.inputs = [];
        this.attrs = {};
        this.inputs = node.inputNames.map((name)=>this.getInput(name));
        if (node.rawAttrs != null) this.attrs = Object.keys(node.rawAttrs).reduce((attrs, key)=>{
            attrs[key] = this.getAttr(key);
            return attrs;
        }, {});
    }
    /**
     * Return the value of the attribute or input param.
     * @param name String: name of attribute or input param.
     */ getInput(name) {
        return (0, _utils.getTensor)(name, this.tensorMap, this.context);
    }
    /**
     * Return the value of the attribute or input param.
     * @param name String: name of attribute or input param.
     */ getAttr(name, defaultValue) {
        const value = this.node.rawAttrs[name];
        if (value.tensor != null) return (0, _utils.getTensor)(name, this.tensorMap, this.context);
        if (value.i != null || value.f != null) return (0, _operationMapper.getNumberParam)(this.node.rawAttrs, name, defaultValue);
        if (value.s != null) return (0, _operationMapper.getStringParam)(this.node.rawAttrs, name, defaultValue);
        if (value.b != null) return (0, _operationMapper.getBoolParam)(this.node.rawAttrs, name, defaultValue);
        if (value.shape != null) return (0, _operationMapper.getTensorShapeParam)(this.node.rawAttrs, name, defaultValue);
        if (value.type != null) return (0, _operationMapper.getDtypeParam)(this.node.rawAttrs, name, defaultValue);
        if (value.list != null) {
            if (value.list.i != null || value.list.f != null) return (0, _operationMapper.getNumericArrayParam)(this.node.rawAttrs, name, defaultValue);
            if (value.list.s != null) return (0, _operationMapper.getStringArrayParam)(this.node.rawAttrs, name, defaultValue);
            if (value.list.shape != null) return (0, _operationMapper.getTensorShapeArrayParam)(this.node.rawAttrs, name, defaultValue);
            if (value.list.b != null) return (0, _operationMapper.getBoolArrayParam)(this.node.rawAttrs, name, defaultValue);
            if (value.list.type != null) return (0, _operationMapper.getDtypeArrayParam)(this.node.rawAttrs, name, defaultValue);
        }
        return defaultValue;
    }
}

},{"../executors/utils":"lA8AN","../operation_mapper":"ewPe6","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8J0UN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "BiasAdd":
        case "AddV2":
        case "Add":
            return [
                ops.add((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "AddN":
            return [
                ops.addN((0, _utils.getParamValue)("tensors", node, tensorMap, context))
            ];
        case "FloorMod":
        case "Mod":
            return [
                ops.mod((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Mul":
            return [
                ops.mul((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "RealDiv":
        case "Div":
            return [
                ops.div((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "DivNoNan":
            return [
                ops.divNoNan((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "FloorDiv":
            return [
                ops.floorDiv((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Sub":
            return [
                ops.sub((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Minimum":
            return [
                ops.minimum((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Maximum":
            return [
                ops.maximum((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Pow":
            return [
                ops.pow((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "SquaredDifference":
            return [
                ops.squaredDifference((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "arithmetic";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iUHBn":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * This file exports ops used by the converters executors. By default it
 * re-exports all ops. In a custom build this is aliased to a file that will
 * only exports ops for a given model.json.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
var _ops = require("./ops");
parcelHelpers.exportAll(_ops, exports);

},{"./ops":"13k1e","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"j2IHU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "Abs":
        case "ComplexAbs":
            return [
                ops.abs((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Acos":
            return [
                ops.acos((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Acosh":
            return [
                ops.acosh((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Asin":
            return [
                ops.asin((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Asinh":
            return [
                ops.asinh((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Atan":
            return [
                ops.atan((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Atan2":
            return [
                ops.atan2((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("y", node, tensorMap, context))
            ];
        case "Atanh":
            return [
                ops.atanh((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Ceil":
            return [
                ops.ceil((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Complex":
            return [
                ops.complex((0, _utils.getParamValue)("real", node, tensorMap, context), (0, _utils.getParamValue)("imag", node, tensorMap, context))
            ];
        case "Cos":
            return [
                ops.cos((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Cosh":
            return [
                ops.cosh((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Elu":
            return [
                ops.elu((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Erf":
            return [
                ops.erf((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Exp":
            return [
                ops.exp((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Expm1":
            return [
                ops.expm1((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Floor":
            return [
                ops.floor((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Log":
            return [
                ops.log((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Log1p":
            return [
                ops.log1p((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Imag":
            return [
                ops.imag((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Neg":
            return [
                ops.neg((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Reciprocal":
            return [
                ops.reciprocal((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Real":
            return [
                ops.real((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Relu":
            return [
                ops.relu((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Round":
            return [
                ops.round((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Selu":
            return [
                ops.selu((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Sigmoid":
            return [
                ops.sigmoid((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Sin":
            return [
                ops.sin((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Sign":
            return [
                ops.sign((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Sinh":
            return [
                ops.sinh((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Softplus":
            return [
                ops.softplus((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Sqrt":
            return [
                ops.sqrt((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Square":
            return [
                ops.square((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Tanh":
            return [
                ops.tanh((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Tan":
            return [
                ops.tan((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "ClipByValue":
            return [
                ops.clipByValue((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("clipValueMin", node, tensorMap, context), (0, _utils.getParamValue)("clipValueMax", node, tensorMap, context))
            ];
        case "Relu6":
            return [
                ops.relu6((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "Rsqrt":
            return [
                ops.rsqrt((0, _utils.getTensor)(node.inputNames[0], tensorMap, context))
            ];
        case "LeakyRelu":
            return [
                ops.leakyRelu((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("alpha", node, tensorMap, context))
            ];
        case "Prelu":
            return [
                ops.prelu((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("alpha", node, tensorMap, context))
            ];
        case "IsNan":
            return [
                ops.isNaN((0, _utils.getTensor)(node.inputNames[0], tensorMap, context))
            ];
        case "IsInf":
            return [
                ops.isInf((0, _utils.getTensor)(node.inputNames[0], tensorMap, context))
            ];
        case "IsFinite":
            return [
                ops.isFinite((0, _utils.getTensor)(node.inputNames[0], tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "basic_math";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1V4dK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _tensorArray = require("../../executor/tensor_array");
var _tensorList = require("../../executor/tensor_list");
var _utils = require("./utils");
const executeOp = async (node, tensorMap, context)=>{
    switch(node.op){
        case "If":
        case "StatelessIf":
            {
                const thenFunc = (0, _utils.getParamValue)("thenBranch", node, tensorMap, context);
                const elseFunc = (0, _utils.getParamValue)("elseBranch", node, tensorMap, context);
                const cond = (0, _utils.getParamValue)("cond", node, tensorMap, context);
                const args = (0, _utils.getParamValue)("args", node, tensorMap, context);
                const condValue = await cond.data();
                if (condValue[0]) return context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
                else return context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
            }
        case "While":
        case "StatelessWhile":
            {
                const bodyFunc = (0, _utils.getParamValue)("body", node, tensorMap, context);
                const condFunc = (0, _utils.getParamValue)("cond", node, tensorMap, context);
                const args = (0, _utils.getParamValue)("args", node, tensorMap, context);
                // Calculate the condition of the loop
                const condResult = await context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap);
                const argIds = args.map((tensor)=>tensor.id);
                let condValue = await condResult[0].data();
                // Dispose the intermediate tensors for condition function
                condResult.forEach((tensor)=>{
                    if (!tensor.kept && argIds.indexOf(tensor.id) === -1) tensor.dispose();
                });
                let result = args;
                while(condValue[0]){
                    // Record the previous result for intermediate tensor tracking
                    const origResult = result;
                    // Execution the body of the loop
                    result = await context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
                    const resultIds = result.map((tensor)=>tensor.id);
                    // Dispose the intermediate tensor for body function that is not global
                    // kept, not input/output of the body function
                    origResult.forEach((tensor)=>{
                        if (!tensor.kept && argIds.indexOf(tensor.id) === -1 && resultIds.indexOf(tensor.id) === -1) tensor.dispose();
                    });
                    // Recalcuate the condition of the loop using the latest results.
                    const condResult = await context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap);
                    condValue = await condResult[0].data();
                    // Dispose the intermediate tensors for condition function
                    condResult.forEach((tensor)=>{
                        if (!tensor.kept && argIds.indexOf(tensor.id) === -1 && resultIds.indexOf(tensor.id) === -1) tensor.dispose();
                    });
                }
                return result;
            }
        case "LoopCond":
            {
                const pred = (0, _utils.getParamValue)("pred", node, tensorMap, context);
                return [
                    (0, _utils.cloneTensor)(pred)
                ];
            }
        case "Switch":
            {
                const pred = (0, _utils.getParamValue)("pred", node, tensorMap, context);
                let data = (0, _utils.getParamValue)("data", node, tensorMap, context);
                if (!data.kept) data = (0, _utils.cloneTensor)(data);
                // Outputs nodes :0 => false, :1 => true
                return (await pred.data())[0] ? [
                    undefined,
                    data
                ] : [
                    data,
                    undefined
                ];
            }
        case "Merge":
            {
                const inputName = node.inputNames.find((name)=>(0, _utils.getTensor)(name, tensorMap, context) !== undefined);
                if (inputName) {
                    const data = (0, _utils.getTensor)(inputName, tensorMap, context);
                    return [
                        (0, _utils.cloneTensor)(data)
                    ];
                }
                return undefined;
            }
        case "Enter":
            {
                const frameId = (0, _utils.getParamValue)("frameName", node, tensorMap, context);
                const data = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                context.enterFrame(frameId);
                return [
                    (0, _utils.cloneTensor)(data)
                ];
            }
        case "Exit":
            {
                const data = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                context.exitFrame();
                return [
                    (0, _utils.cloneTensor)(data)
                ];
            }
        case "NextIteration":
            {
                const data = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                context.nextIteration();
                return [
                    (0, _utils.cloneTensor)(data)
                ];
            }
        case "TensorArrayV3":
            {
                const size = (0, _utils.getParamValue)("size", node, tensorMap, context);
                const dtype = (0, _utils.getParamValue)("dtype", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const dynamicSize = (0, _utils.getParamValue)("dynamicSize", node, tensorMap, context);
                const clearAfterRead = (0, _utils.getParamValue)("clearAfterRead", node, tensorMap, context);
                const identicalElementShapes = (0, _utils.getParamValue)("identicalElementShapes", node, tensorMap, context);
                const name = (0, _utils.getParamValue)("name", node, tensorMap, context);
                const tensorArray = new (0, _tensorArray.TensorArray)(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead);
                context.addTensorArray(tensorArray);
                return [
                    tensorArray.idTensor,
                    (0, _tfjsCore.scalar)(1.0)
                ];
            }
        case "TensorArrayWriteV3":
            {
                const id = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const index = (0, _utils.getParamValue)("index", node, tensorMap, context);
                const writeTensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const writeTensorArray = context.getTensorArray(id.id);
                writeTensorArray.write(index, writeTensor);
                return [
                    writeTensorArray.idTensor
                ];
            }
        case "TensorArrayReadV3":
            {
                const readId = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const readIndex = (0, _utils.getParamValue)("index", node, tensorMap, context);
                const readTensorArray = context.getTensorArray(readId.id);
                return [
                    readTensorArray.read(readIndex)
                ];
            }
        case "TensorArrayGatherV3":
            {
                const gatherId = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const gatherIndices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                const gatherDtype = (0, _utils.getParamValue)("dtype", node, tensorMap, context);
                const gatherTensorArray = context.getTensorArray(gatherId.id);
                return [
                    gatherTensorArray.gather(gatherIndices, gatherDtype)
                ];
            }
        case "TensorArrayScatterV3":
            {
                const scatterId = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const scatterIndices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                const scatterTensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const scatterTensorArray = context.getTensorArray(scatterId.id);
                scatterTensorArray.scatter(scatterIndices, scatterTensor);
                return [
                    scatterTensorArray.idTensor
                ];
            }
        case "TensorArrayConcatV3":
            {
                const concatId = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const concatTensorArray = context.getTensorArray(concatId.id);
                const concatDtype = (0, _utils.getParamValue)("dtype", node, tensorMap, context);
                return [
                    concatTensorArray.concat(concatDtype)
                ];
            }
        case "TensorArraySplitV3":
            {
                const splitId = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const splitTensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const lengths = (0, _utils.getParamValue)("lengths", node, tensorMap, context);
                const splitTensorArray = context.getTensorArray(splitId.id);
                splitTensorArray.split(lengths, splitTensor);
                return [
                    splitTensorArray.idTensor
                ];
            }
        case "TensorArraySizeV3":
            {
                const sizeId = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const sizeTensorArray = context.getTensorArray(sizeId.id);
                return [
                    (0, _tfjsCore.scalar)(sizeTensorArray.size(), "int32")
                ];
            }
        case "TensorArrayCloseV3":
            {
                const closeId = (0, _utils.getParamValue)("tensorArrayId", node, tensorMap, context);
                const closeTensorArray = context.getTensorArray(closeId.id);
                closeTensorArray.clearAndClose();
                return [
                    closeTensorArray.idTensor
                ];
            }
        case "TensorListSetItem":
            {
                const idTensor = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const index = (0, _utils.getParamValue)("index", node, tensorMap, context);
                const writeTensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const tensorList = context.getTensorList(idTensor.id);
                tensorList.setItem(index, writeTensor);
                return [
                    tensorList.idTensor
                ];
            }
        case "TensorListGetItem":
            {
                const idTensor = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const readIndex = (0, _utils.getParamValue)("index", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const elementDType = (0, _utils.getParamValue)("elementDType", node, tensorMap, context);
                const tensorList = context.getTensorList(idTensor.id);
                return [
                    tensorList.getItem(readIndex, elementShape, elementDType)
                ];
            }
        case "TensorListScatterV2":
        case "TensorListScatter":
            {
                const scatterIndices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                const scatterTensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const numElements = (0, _utils.getParamValue)("numElements", node, tensorMap, context);
                const tensorList = (0, _tensorList.scatter)(scatterTensor, scatterIndices, elementShape, numElements);
                context.addTensorList(tensorList);
                return [
                    tensorList.idTensor
                ];
            }
        case "TensorListReserve":
        case "EmptyTensorList":
            {
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const elementDtype = (0, _utils.getParamValue)("elementDType", node, tensorMap, context);
                let numElementsParam;
                if (node.op === "TensorListReserve") numElementsParam = "numElements";
                else numElementsParam = "maxNumElements";
                const numElements = (0, _utils.getParamValue)(numElementsParam, node, tensorMap, context);
                const maxNumElements = node.op === "TensorListReserve" ? -1 : numElements;
                const tensorList = (0, _tensorList.reserve)(elementShape, elementDtype, numElements, maxNumElements);
                context.addTensorList(tensorList);
                return [
                    tensorList.idTensor
                ];
            }
        case "TensorListGather":
            {
                const gatherId = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const gatherIndices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const elementDtype = (0, _utils.getParamValue)("elementDType", node, tensorMap, context);
                const tensorList = context.getTensorList(gatherId.id);
                return [
                    tensorList.gather(gatherIndices, elementDtype, elementShape)
                ];
            }
        case "TensorListStack":
            {
                const idTensor = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const elementDtype = (0, _utils.getParamValue)("elementDType", node, tensorMap, context);
                const numElements = (0, _utils.getParamValue)("numElements", node, tensorMap, context);
                const tensorList = context.getTensorList(idTensor.id);
                return [
                    tensorList.stack(elementShape, elementDtype, numElements)
                ];
            }
        case "TensorListFromTensor":
            {
                const tensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const elementDtype = (0, _utils.getParamValue)("elementDType", node, tensorMap, context);
                const tensorList = (0, _tensorList.fromTensor)(tensor, elementShape, elementDtype);
                context.addTensorList(tensorList);
                return [
                    tensorList.idTensor
                ];
            }
        case "TensorListConcat":
        case "TensorListConcatV2":
            {
                const concatId = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const tensorList = context.getTensorList(concatId.id);
                const concatDtype = (0, _utils.getParamValue)("dtype", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                return [
                    tensorList.concat(concatDtype, elementShape)
                ];
            }
        case "TensorListPushBack":
            {
                const idTensor = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const writeTensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const tensorList = context.getTensorList(idTensor.id);
                tensorList.pushBack(writeTensor);
                return [
                    tensorList.idTensor
                ];
            }
        case "TensorListPopBack":
            {
                const idTensor = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const elementDType = (0, _utils.getParamValue)("elementDType", node, tensorMap, context);
                const tensorList = context.getTensorList(idTensor.id);
                return [
                    tensorList.popBack(elementShape, elementDType)
                ];
            }
        case "TensorListSplit":
            {
                const splitTensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                const elementShape = (0, _utils.getParamValue)("elementShape", node, tensorMap, context);
                const lengths = (0, _utils.getParamValue)("lengths", node, tensorMap, context);
                const tensorList = (0, _tensorList.split)(splitTensor, lengths, elementShape);
                context.addTensorList(tensorList);
                return [
                    tensorList.idTensor
                ];
            }
        case "TensorListLength":
            {
                const idTensor = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const tensorList = context.getTensorList(idTensor.id);
                return [
                    (0, _tfjsCore.scalar)(tensorList.size(), "int32")
                ];
            }
        case "TensorListResize":
            {
                const idTensor = (0, _utils.getParamValue)("tensorListId", node, tensorMap, context);
                const size = (0, _utils.getParamValue)("size", node, tensorMap, context);
                const srcTensorList = context.getTensorList(idTensor.id);
                const destTensorList = srcTensorList.resize(size);
                context.addTensorList(destTensorList);
                return [
                    destTensorList.idTensor
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "control";

},{"@tensorflow/tfjs-core":"21Ftl","../../executor/tensor_array":"1VnUW","../../executor/tensor_list":"bKjQu","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1VnUW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * The TensorArray object keeps an array of Tensors.  It
 * allows reading from the array and writing to the array.
 */ parcelHelpers.export(exports, "TensorArray", ()=>TensorArray);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _tensorUtils = require("./tensor_utils");
class TensorArray {
    constructor(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead){
        this.name = name;
        this.dtype = dtype;
        this.maxSize = maxSize;
        this.elementShape = elementShape;
        this.identicalElementShapes = identicalElementShapes;
        this.dynamicSize = dynamicSize;
        this.clearAfterRead = clearAfterRead;
        this.tensors = [];
        this.closed_ = false;
        this.idTensor = (0, _tfjsCore.scalar)(0);
        (0, _tfjsCore.keep)(this.idTensor);
    }
    get id() {
        return this.idTensor.id;
    }
    get closed() {
        return this.closed_;
    }
    /**
     * Dispose the tensors and idTensor and mark the TensoryArray as closed.
     */ clearAndClose(keepIds) {
        this.tensors.forEach((tensor)=>{
            if (keepIds == null || !keepIds.has(tensor.tensor.id)) tensor.tensor.dispose();
        });
        this.tensors = [];
        this.closed_ = true;
        this.idTensor.dispose();
    }
    size() {
        return this.tensors.length;
    }
    /**
     * Read the value at location index in the TensorArray.
     * @param index Number the index to read from.
     */ read(index) {
        if (this.closed_) throw new Error(`TensorArray ${this.name} has already been closed.`);
        if (index < 0 || index >= this.size()) throw new Error(`Tried to read from index ${index}, but array size is: ${this.size()}`);
        const tensorWithState = this.tensors[index];
        if (tensorWithState.cleared) throw new Error(`TensorArray ${this.name}: Could not read index ${index} twice because it was cleared after a previous read ` + `(perhaps try setting clear_after_read = false?).`);
        if (this.clearAfterRead) tensorWithState.cleared = true;
        tensorWithState.read = true;
        return tensorWithState.tensor;
    }
    /**
     * Helper method to read multiple tensors from the specified indices.
     */ readMany(indices) {
        return indices.map((index)=>this.read(index));
    }
    /**
     * Write value into the index of the TensorArray.
     * @param index number the index to write to.
     * @param tensor
     */ write(index, tensor) {
        if (this.closed_) throw new Error(`TensorArray ${this.name} has already been closed.`);
        if (index < 0 || !this.dynamicSize && index >= this.maxSize) throw new Error(`Tried to write to index ${index}, but array is not resizeable and size is: ${this.maxSize}`);
        const t = this.tensors[index] || {};
        if (tensor.dtype !== this.dtype) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index},
          because the value dtype is ${tensor.dtype}, but TensorArray dtype is ${this.dtype}.`);
        // Set the shape for the first time write to unknow shape tensor array
        if (this.size() === 0 && (this.elementShape == null || this.elementShape.length === 0)) this.elementShape = tensor.shape;
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(this.elementShape, tensor.shape, `TensorArray ${this.name}: Could not write to TensorArray index ${index}.`);
        if (t.read) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been read.`);
        if (t.written) throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${index}, because it has already been written.`);
        t.tensor = tensor;
        (0, _tfjsCore.keep)(tensor);
        t.written = true;
        this.tensors[index] = t;
    }
    /**
     * Helper method to write multiple tensors to the specified indices.
     */ writeMany(indices, tensors) {
        if (indices.length !== tensors.length) throw new Error(`TensorArray ${this.name}: could not write multiple tensors,` + `because the index size: ${indices.length} is not the same as tensors size: ${tensors.length}.`);
        indices.forEach((i, index)=>this.write(i, tensors[index]));
    }
    /**
     * Return selected values in the TensorArray as a packed Tensor. All of
     * selected values must have been written and their shapes must all match.
     * @param [indices] number[] Optional. Taking values in [0, max_value). If the
     *    TensorArray is not dynamic, max_value=size(). If not specified returns
     *    all tensors in the original order.
     * @param [dtype]
     */ gather(indices, dtype) {
        if (!!dtype && dtype !== this.dtype) throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${dtype}`);
        if (!indices) {
            indices = [];
            for(let i = 0; i < this.size(); i++)indices.push(i);
        } else indices = indices.slice(0, this.size());
        if (indices.length === 0) return (0, _tfjsCore.tensor)([], [
            0
        ].concat(this.elementShape));
        // Read all the PersistentTensors into a vector to keep track of
        // their memory.
        const tensors = this.readMany(indices);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: ");
        return (0, _tfjsCore.stack)(tensors, 0);
    }
    /**
     * Return the values in the TensorArray as a concatenated Tensor.
     */ concat(dtype) {
        if (!!dtype && dtype !== this.dtype) throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${dtype}`);
        if (this.size() === 0) return (0, _tfjsCore.tensor)([], [
            0
        ].concat(this.elementShape));
        const indices = [];
        for(let i = 0; i < this.size(); i++)indices.push(i);
        // Collect all the tensors from the tensors array.
        const tensors = this.readMany(indices);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(this.elementShape, tensors[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${tensors[0].shape})`);
        return (0, _tfjsCore.concat)(tensors, 0);
    }
    /**
     * Scatter the values of a Tensor in specific indices of a TensorArray.
     * @param indices number[] values in [0, max_value). If the
     *    TensorArray is not dynamic, max_value=size().
     * @param tensor Tensor input tensor.
     */ scatter(indices, tensor) {
        if (tensor.dtype !== this.dtype) throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);
        if (indices.length !== tensor.shape[0]) throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);
        const maxIndex = Math.max(...indices);
        if (!this.dynamicSize && maxIndex >= this.maxSize) throw new Error(`Max index must be < array size (${maxIndex}  vs. ${this.maxSize})`);
        this.writeMany(indices, (0, _tfjsCore.unstack)(tensor, 0));
    }
    /**
     * Split the values of a Tensor into the TensorArray.
     * @param length number[] with the lengths to use when splitting value along
     *    its first dimension.
     * @param tensor Tensor, the tensor to split.
     */ split(length, tensor) {
        if (tensor.dtype !== this.dtype) throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${tensor.dtype}`);
        let totalLength = 0;
        const cumulativeLengths = length.map((len)=>{
            totalLength += len;
            return totalLength;
        });
        if (totalLength !== tensor.shape[0]) throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${totalLength}, and tensor's shape is: ${tensor.shape}`);
        if (!this.dynamicSize && length.length !== this.maxSize) throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${length.length}), ` + "and the TensorArray is not marked as dynamically resizeable");
        const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;
        const tensors = [];
        (0, _tfjsCore.tidy)(()=>{
            tensor = (0, _tfjsCore.reshape)(tensor, [
                1,
                totalLength,
                elementPerRow
            ]);
            for(let i = 0; i < length.length; ++i){
                const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
                const indices = [
                    0,
                    previousLength,
                    0
                ];
                const sizes = [
                    1,
                    length[i],
                    elementPerRow
                ];
                tensors[i] = (0, _tfjsCore.reshape)((0, _tfjsCore.slice)(tensor, indices, sizes), this.elementShape);
            }
            return tensors;
        });
        const indices = [];
        for(let i = 0; i < length.length; i++)indices[i] = i;
        this.writeMany(indices, tensors);
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","./tensor_utils":"bzuYR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bzuYR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /**
 * This differs from util.assertShapesMatch in that it allows values of
 * negative one, an undefined size of a dimensinon, in a shape to match
 * anything.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Used by TensorList and TensorArray to verify if elementShape matches, support
 * negative value as the dim shape.
 * @param shapeA
 * @param shapeB
 * @param errorMessagePrefix
 */ parcelHelpers.export(exports, "assertShapesMatchAllowUndefinedSize", ()=>assertShapesMatchAllowUndefinedSize);
parcelHelpers.export(exports, "fullDefinedShape", ()=>fullDefinedShape);
/**
 * Generate the output element shape from the list elementShape, list tensors
 * and input param.
 * @param listElementShape
 * @param tensors
 * @param elementShape
 */ parcelHelpers.export(exports, "inferElementShape", ()=>inferElementShape);
parcelHelpers.export(exports, "mergeElementShape", ()=>mergeElementShape);
var _tfjsCore = require("@tensorflow/tfjs-core");
function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix = "") {
    // constant shape means unknown rank
    if (typeof shapeA === "number" || typeof shapeB === "number") return;
    (0, _tfjsCore.util).assert(shapeA.length === shapeB.length, ()=>errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
    for(let i = 0; i < shapeA.length; i++){
        const dim0 = shapeA[i];
        const dim1 = shapeB[i];
        (0, _tfjsCore.util).assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, ()=>errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);
    }
}
function fullDefinedShape(elementShape) {
    if (typeof elementShape === "number" || elementShape.some((dim)=>dim < 0)) return false;
    return true;
}
function inferElementShape(listElementShape, tensors, elementShape) {
    let partialShape = mergeElementShape(listElementShape, elementShape);
    const notfullDefinedShape = !fullDefinedShape(partialShape);
    if (notfullDefinedShape && tensors.length === 0) throw new Error(`Tried to calculate elements of an empty list` + ` with non-fully-defined elementShape: ${partialShape}`);
    if (notfullDefinedShape) tensors.forEach((tensor)=>{
        partialShape = mergeElementShape(tensor.shape, partialShape);
    });
    if (!fullDefinedShape(partialShape)) throw new Error(`Non-fully-defined elementShape: ${partialShape}`);
    return partialShape;
}
function mergeElementShape(elementShapeA, elementShapeB) {
    if (typeof elementShapeA === "number") return elementShapeB;
    if (typeof elementShapeB === "number") return elementShapeA;
    if (elementShapeA.length !== elementShapeB.length) throw new Error(`Incompatible ranks during merge: ${elementShapeA} vs. ${elementShapeB}`);
    const result = [];
    for(let i = 0; i < elementShapeA.length; ++i){
        const dim0 = elementShapeA[i];
        const dim1 = elementShapeB[i];
        if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) throw new Error(`Incompatible shape during merge: ${elementShapeA} vs. ${elementShapeB}`);
        result[i] = dim0 >= 0 ? dim0 : dim1;
    }
    return result;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bKjQu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * TensorList stores a container of `tf.Tensor` objects, which are accessible
 * via tensors field.
 *
 * In order to get a copy of the underlying list, use the copy method:
 * ```
 *    TensorList b = a.copy();
 *    b.tensors().pushBack(t);  // This does not modify a.tensors().
 * ```
 *
 * Note that this is not a deep copy: the memory locations of the underlying
 * tensors will still point to the same locations of the corresponding tensors
 * in the original.
 */ parcelHelpers.export(exports, "TensorList", ()=>TensorList);
/**
 * Creates a TensorList which, when stacked, has the value of tensor.
 * @param tensor from tensor
 * @param elementShape output tensor element shape
 */ parcelHelpers.export(exports, "fromTensor", ()=>fromTensor);
/**
 * Return a TensorList of the given size with empty elements.
 * @param elementShape the shape of the future elements of the list
 * @param elementDtype the desired type of elements in the list
 * @param numElements the number of elements to reserve
 * @param maxNumElements the maximum number of elements in th list
 */ parcelHelpers.export(exports, "reserve", ()=>reserve);
/**
 * Put tensors at specific indices of a stacked tensor into a TensorList.
 * @param indices list of indices on how to scatter the tensor.
 * @param tensor input tensor.
 * @param elementShape the shape of the future elements of the list
 * @param numElements the number of elements to scatter
 */ parcelHelpers.export(exports, "scatter", ()=>scatter);
/**
 * Split the values of a Tensor into a TensorList.
 * @param length the lengths to use when splitting value along
 *    its first dimension.
 * @param tensor the tensor to split.
 * @param elementShape the shape of the future elements of the list
 */ parcelHelpers.export(exports, "split", ()=>split);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _tensorUtils = require("./tensor_utils");
class TensorList {
    get id() {
        return this.idTensor.id;
    }
    /**
     *
     * @param tensors list of tensors
     * @param elementShape shape of each tensor, this can be a single number (any
     * shape is allowed) or partial shape (dim = -1).
     * @param elementDtype data type of each tensor
     * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1
     *   meaning that the size of `tensors` is unbounded.
     */ constructor(tensors, elementShape, elementDtype, maxNumElements = -1){
        this.tensors = tensors;
        this.elementShape = elementShape;
        this.elementDtype = elementDtype;
        if (tensors != null) tensors.forEach((tensor)=>{
            if (elementDtype !== tensor.dtype) throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${tensor.dtype}`);
            (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(elementShape, tensor.shape, "TensorList shape mismatch: ");
            (0, _tfjsCore.keep)(tensor);
        });
        this.idTensor = (0, _tfjsCore.scalar)(0);
        this.maxNumElements = maxNumElements;
        (0, _tfjsCore.keep)(this.idTensor);
    }
    /**
     * Get a new TensorList containing a copy of the underlying tensor container.
     */ copy() {
        return new TensorList([
            ...this.tensors
        ], this.elementShape, this.elementDtype);
    }
    /**
     * Dispose the tensors and idTensor and clear the tensor list.
     */ clearAndClose(keepIds) {
        this.tensors.forEach((tensor)=>{
            if (keepIds == null || !keepIds.has(tensor.id)) tensor.dispose();
        });
        this.tensors.length = 0;
        this.idTensor.dispose();
    }
    /**
     * The size of the tensors in the tensor list.
     */ size() {
        return this.tensors.length;
    }
    /**
     * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1)
     * tf.Tensor.
     * @param elementShape shape of each tensor
     * @param elementDtype data type of each tensor
     * @param numElements the number of elements to stack
     */ stack(elementShape, elementDtype, numElements = -1) {
        if (elementDtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
        if (numElements !== -1 && this.tensors.length !== numElements) throw new Error(`Operation expected a list with ${numElements} elements but got a list with ${this.tensors.length} elements.`);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(elementShape, this.elementShape, "TensorList shape mismatch: ");
        const outputElementShape = (0, _tensorUtils.inferElementShape)(this.elementShape, this.tensors, elementShape);
        return (0, _tfjsCore.tidy)(()=>{
            const reshapedTensors = this.tensors.map((tensor)=>(0, _tfjsCore.reshape)(tensor, outputElementShape));
            return (0, _tfjsCore.stack)(reshapedTensors, 0);
        });
    }
    /**
     * Pop a tensor from the end of the list.
     * @param elementShape shape of the tensor
     * @param elementDtype data type of the tensor
     */ popBack(elementShape, elementDtype) {
        if (elementDtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
        if (this.size() === 0) throw new Error("Trying to pop from an empty list.");
        const outputElementShape = (0, _tensorUtils.inferElementShape)(this.elementShape, this.tensors, elementShape);
        const tensor = this.tensors.pop();
        tensor.kept = false;
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(tensor.shape, elementShape, "TensorList shape mismatch: ");
        return (0, _tfjsCore.reshape)(tensor, outputElementShape);
    }
    /**
     * Push a tensor to the end of the list.
     * @param tensor Tensor to be pushed.
     */ pushBack(tensor) {
        if (tensor.dtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(tensor.shape, this.elementShape, "TensorList shape mismatch: ");
        if (this.maxNumElements === this.size()) throw new Error(`Trying to push element into a full list.`);
        (0, _tfjsCore.keep)(tensor);
        this.tensors.push(tensor);
    }
    /**
     * Update the size of the list.
     * @param size the new size of the list.
     */ resize(size) {
        if (size < 0) throw new Error(`TensorListResize expects size to be non-negative. Got: ${size}`);
        if (this.maxNumElements !== -1 && size > this.maxNumElements) throw new Error(`TensorListResize input size ${size} is greater maxNumElement ${this.maxNumElements}.`);
        const destTensorList = new TensorList([], this.elementShape, this.elementDtype, this.maxNumElements);
        destTensorList.tensors.length = size;
        for(let i = 0; i < Math.min(this.tensors.length, size); ++i)destTensorList.tensors[i] = this.tensors[i];
        return destTensorList;
    }
    /**
     * Retrieve the element at the provided index
     * @param elementShape shape of the tensor
     * @param elementDtype dtype of the tensor
     * @param elementIndex index of the tensor
     */ getItem(elementIndex, elementShape, elementDtype) {
        if (elementDtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
        if (elementIndex < 0 || elementIndex > this.tensors.length) throw new Error(`Trying to access element ${elementIndex} in a list with ${this.tensors.length} elements.`);
        if (this.tensors[elementIndex] == null) throw new Error(`element at index ${elementIndex} is null.`);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(this.tensors[elementIndex].shape, elementShape, "TensorList shape mismatch: ");
        const outputElementShape = (0, _tensorUtils.inferElementShape)(this.elementShape, this.tensors, elementShape);
        return (0, _tfjsCore.reshape)(this.tensors[elementIndex], outputElementShape);
    }
    /**
     * Set the tensor at the index
     * @param elementIndex index of the tensor
     * @param tensor the tensor to be inserted into the list
     */ setItem(elementIndex, tensor) {
        if (tensor.dtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${this.elementDtype}`);
        if (elementIndex < 0 || this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) throw new Error(`Trying to set element ${elementIndex} in a list with max ${this.maxNumElements} elements.`);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(this.elementShape, tensor.shape, "TensorList shape mismatch: ");
        (0, _tfjsCore.keep)(tensor);
        // dispose the previous value if it is replacing.
        if (this.tensors[elementIndex] != null) this.tensors[elementIndex].kept = false;
        this.tensors[elementIndex] = tensor;
    }
    /**
     * Return selected values in the TensorList as a stacked Tensor. All of
     * selected values must have been written and their shapes must all match.
     * @param indices indices of tensors to gather
     * @param elementDtype output tensor dtype
     * @param elementShape output tensor element shape
     */ gather(indices, elementDtype, elementShape) {
        if (elementDtype !== this.elementDtype) throw new Error(`Invalid data types; op elements ${elementDtype}, but list elements ${this.elementDtype}`);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(this.elementShape, elementShape, "TensorList shape mismatch: ");
        // When indices is greater than the size of the list, indices beyond the
        // size of the list are ignored.
        indices = indices.slice(0, this.size());
        const outputElementShape = (0, _tensorUtils.inferElementShape)(this.elementShape, this.tensors, elementShape);
        if (indices.length === 0) return (0, _tfjsCore.tensor)([], [
            0
        ].concat(outputElementShape));
        return (0, _tfjsCore.tidy)(()=>{
            const tensors = indices.map((i)=>(0, _tfjsCore.reshape)(this.tensors[i], outputElementShape));
            return (0, _tfjsCore.stack)(tensors, 0);
        });
    }
    /**
     * Return the values in the TensorList as a concatenated Tensor.
     * @param elementDtype output tensor dtype
     * @param elementShape output tensor element shape
     */ concat(elementDtype, elementShape) {
        if (!!elementDtype && elementDtype !== this.elementDtype) throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${elementDtype}`);
        (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(this.elementShape, elementShape, "TensorList shape mismatch: ");
        const outputElementShape = (0, _tensorUtils.inferElementShape)(this.elementShape, this.tensors, elementShape);
        if (this.size() === 0) return (0, _tfjsCore.tensor)([], [
            0
        ].concat(outputElementShape));
        return (0, _tfjsCore.tidy)(()=>{
            const tensors = this.tensors.map((t)=>(0, _tfjsCore.reshape)(t, outputElementShape));
            return (0, _tfjsCore.concat)(tensors, 0);
        });
    }
}
function fromTensor(tensor, elementShape, elementDtype) {
    const dtype = tensor.dtype;
    if (tensor.shape.length < 1) throw new Error(`Tensor must be at least a vector, but saw shape: ${tensor.shape}`);
    if (tensor.dtype !== elementDtype) throw new Error(`Invalid data types; op elements ${tensor.dtype}, but list elements ${elementDtype}`);
    const tensorElementShape = tensor.shape.slice(1);
    (0, _tensorUtils.assertShapesMatchAllowUndefinedSize)(tensorElementShape, elementShape, "TensorList shape mismatch: ");
    const tensorList = (0, _tfjsCore.unstack)(tensor);
    return new TensorList(tensorList, elementShape, dtype);
}
function reserve(elementShape, elementDtype, numElements, maxNumElements) {
    return new TensorList([], elementShape, elementDtype, maxNumElements);
}
function scatter(tensor, indices, elementShape, numElements) {
    if (indices.length !== tensor.shape[0]) throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${indices.length} vs. ${tensor.shape[0]}`);
    const maxIndex = Math.max(...indices);
    if (numElements != null && numElements !== -1 && maxIndex >= numElements) throw new Error(`Max index must be < array size (${maxIndex}  vs. ${numElements})`);
    const list = new TensorList([], elementShape, tensor.dtype, numElements);
    const tensors = (0, _tfjsCore.unstack)(tensor, 0);
    indices.forEach((value, index)=>{
        list.setItem(value, tensors[index]);
    });
    return list;
}
function split(tensor, length, elementShape) {
    let totalLength = 0;
    const cumulativeLengths = length.map((len)=>{
        totalLength += len;
        return totalLength;
    });
    if (totalLength !== tensor.shape[0]) throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${totalLength}, and tensor's shape is: ${tensor.shape}`);
    const shapeWithoutFirstDim = tensor.shape.slice(1);
    const outputElementShape = (0, _tensorUtils.mergeElementShape)(shapeWithoutFirstDim, elementShape);
    const elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength;
    const tensors = (0, _tfjsCore.tidy)(()=>{
        const tensors = [];
        tensor = (0, _tfjsCore.reshape)(tensor, [
            1,
            totalLength,
            elementPerRow
        ]);
        for(let i = 0; i < length.length; ++i){
            const previousLength = i === 0 ? 0 : cumulativeLengths[i - 1];
            const indices = [
                0,
                previousLength,
                0
            ];
            const sizes = [
                1,
                length[i],
                elementPerRow
            ];
            tensors[i] = (0, _tfjsCore.reshape)((0, _tfjsCore.slice)(tensor, indices, sizes), outputElementShape);
        }
        tensor.dispose();
        return tensors;
    });
    const list = new TensorList([], elementShape, tensor.dtype, length.length);
    for(let i = 0; i < tensors.length; i++)list.setItem(i, tensors[i]);
    return list;
}

},{"@tensorflow/tfjs-core":"21Ftl","./tensor_utils":"bzuYR","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"b2H1R":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
function fusedConvAndDepthWiseParams(node, tensorMap, context) {
    const [extraOp, activationFunc] = (0, _utils.getParamValue)("fusedOps", node, tensorMap, context);
    const isBiasAdd = extraOp === "biasadd";
    const noBiasAdd = !isBiasAdd;
    const isPrelu = activationFunc === "prelu";
    const isBatchNorm = extraOp === "fusedbatchnorm";
    const numArgs = (0, _utils.getParamValue)("numArgs", node, tensorMap, context);
    if (isBiasAdd) {
        if (isPrelu && numArgs !== 2) throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
        if (!isPrelu && isBiasAdd && numArgs !== 1) throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.");
    }
    if (isBatchNorm) throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");
    const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
    const pad = (0, _utils.getPadding)(node, tensorMap, context);
    const dataFormat = (0, _utils.getParamValue)("dataFormat", node, tensorMap, context).toUpperCase();
    const dilations = (0, _utils.getParamValue)("dilations", node, tensorMap, context);
    let [biasArg, preluArg] = (0, _utils.getParamValue)("args", node, tensorMap, context);
    if (noBiasAdd) {
        preluArg = biasArg;
        biasArg = undefined;
    }
    const leakyreluAlpha = (0, _utils.getParamValue)("leakyreluAlpha", node, tensorMap, context);
    return {
        stride,
        pad,
        dataFormat,
        dilations,
        biasArg,
        preluArg,
        activationFunc,
        leakyreluAlpha
    };
}
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "Conv1D":
            {
                const stride = (0, _utils.getParamValue)("stride", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const dataFormat = (0, _utils.getParamValue)("dataFormat", node, tensorMap, context).toUpperCase();
                const dilation = (0, _utils.getParamValue)("dilation", node, tensorMap, context);
                return [
                    ops.conv1d((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("filter", node, tensorMap, context), stride, pad, dataFormat, dilation)
                ];
            }
        case "Conv2D":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getPadding)(node, tensorMap, context);
                const dataFormat = (0, _utils.getParamValue)("dataFormat", node, tensorMap, context).toUpperCase();
                const dilations = (0, _utils.getParamValue)("dilations", node, tensorMap, context);
                return [
                    ops.conv2d((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("filter", node, tensorMap, context), [
                        stride[1],
                        stride[2]
                    ], pad, dataFormat, [
                        dilations[1],
                        dilations[2]
                    ])
                ];
            }
        case "_FusedConv2D":
            {
                const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);
                return [
                    ops.fused.conv2d({
                        x: (0, _utils.getParamValue)("x", node, tensorMap, context),
                        filter: (0, _utils.getParamValue)("filter", node, tensorMap, context),
                        strides: [
                            stride[1],
                            stride[2]
                        ],
                        pad: pad,
                        dataFormat: dataFormat,
                        dilations: [
                            dilations[1],
                            dilations[2]
                        ],
                        bias: biasArg,
                        activation: activationFunc,
                        preluActivationWeights: preluArg,
                        leakyreluAlpha
                    })
                ];
            }
        case "FusedDepthwiseConv2dNative":
            {
                const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);
                return [
                    ops.fused.depthwiseConv2d({
                        x: (0, _utils.getParamValue)("x", node, tensorMap, context),
                        filter: (0, _utils.getParamValue)("filter", node, tensorMap, context),
                        strides: [
                            stride[1],
                            stride[2]
                        ],
                        pad: pad,
                        dataFormat: dataFormat,
                        dilations: [
                            dilations[1],
                            dilations[2]
                        ],
                        bias: biasArg,
                        activation: activationFunc,
                        preluActivationWeights: preluArg,
                        leakyreluAlpha
                    })
                ];
            }
        case "Conv2DBackpropInput":
        case "Conv2dTranspose":
            {
                const shape = (0, _utils.getParamValue)("outputShape", node, tensorMap, context);
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getPadding)(node, tensorMap, context);
                return [
                    ops.conv2dTranspose((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("filter", node, tensorMap, context), shape, [
                        stride[1],
                        stride[2]
                    ], pad)
                ];
            }
        case "DepthwiseConv2dNative":
        case "DepthwiseConv2d":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getPadding)(node, tensorMap, context);
                const dilations = (0, _utils.getParamValue)("dilations", node, tensorMap, context);
                const dataFormat = (0, _utils.getParamValue)("dataFormat", node, tensorMap, context).toUpperCase();
                return [
                    ops.depthwiseConv2d((0, _utils.getParamValue)("input", node, tensorMap, context), (0, _utils.getParamValue)("filter", node, tensorMap, context), [
                        stride[1],
                        stride[2]
                    ], pad, dataFormat, [
                        dilations[1],
                        dilations[2]
                    ])
                ];
            }
        case "Conv3D":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const dataFormat = (0, _utils.getParamValue)("dataFormat", node, tensorMap, context).toUpperCase();
                const dilations = (0, _utils.getParamValue)("dilations", node, tensorMap, context);
                return [
                    ops.conv3d((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("filter", node, tensorMap, context), [
                        stride[1],
                        stride[2],
                        stride[3]
                    ], pad, dataFormat, [
                        dilations[1],
                        dilations[2],
                        dilations[3]
                    ])
                ];
            }
        case "AvgPool":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const kernelSize = (0, _utils.getParamValue)("kernelSize", node, tensorMap, context);
                return [
                    ops.avgPool((0, _utils.getParamValue)("x", node, tensorMap, context), [
                        kernelSize[1],
                        kernelSize[2]
                    ], [
                        stride[1],
                        stride[2]
                    ], pad)
                ];
            }
        case "MaxPool":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const kernelSize = (0, _utils.getParamValue)("kernelSize", node, tensorMap, context);
                return [
                    ops.maxPool((0, _utils.getParamValue)("x", node, tensorMap, context), [
                        kernelSize[1],
                        kernelSize[2]
                    ], [
                        stride[1],
                        stride[2]
                    ], pad)
                ];
            }
        case "MaxPoolWithArgmax":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const kernelSize = (0, _utils.getParamValue)("kernelSize", node, tensorMap, context);
                const includeBatchInIndex = (0, _utils.getParamValue)("includeBatchInIndex", node, tensorMap, context);
                const { result, indexes } = ops.maxPoolWithArgmax((0, _utils.getParamValue)("x", node, tensorMap, context), [
                    kernelSize[1],
                    kernelSize[2]
                ], [
                    stride[1],
                    stride[2]
                ], pad, includeBatchInIndex);
                return [
                    result,
                    indexes
                ];
            }
        case "AvgPool3D":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const kernelSize = (0, _utils.getParamValue)("kernelSize", node, tensorMap, context);
                return [
                    ops.avgPool3d((0, _utils.getParamValue)("x", node, tensorMap, context), [
                        kernelSize[1],
                        kernelSize[2],
                        kernelSize[3]
                    ], [
                        stride[1],
                        stride[2],
                        stride[3]
                    ], pad)
                ];
            }
        case "MaxPool3D":
            {
                const stride = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const kernelSize = (0, _utils.getParamValue)("kernelSize", node, tensorMap, context);
                return [
                    ops.maxPool3d((0, _utils.getParamValue)("x", node, tensorMap, context), [
                        kernelSize[1],
                        kernelSize[2],
                        kernelSize[3]
                    ], [
                        stride[1],
                        stride[2],
                        stride[3]
                    ], pad)
                ];
            }
        case "Dilation2D":
            {
                const strides = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const pad = (0, _utils.getParamValue)("pad", node, tensorMap, context);
                const dilations = (0, _utils.getParamValue)("dilations", node, tensorMap, context);
                // strides: [1, stride_height, stride_width, 1].
                const strideHeight = strides[1];
                const strideWidth = strides[2];
                // dilations: [1, dilation_height, dilation_width, 1].
                const dilationHeight = dilations[1];
                const dilationWidth = dilations[2];
                return [
                    ops.dilation2d((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("filter", node, tensorMap, context), [
                        strideHeight,
                        strideWidth
                    ], pad, [
                        dilationHeight,
                        dilationWidth
                    ], "NHWC" /* dataFormat */ )
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "convolution";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kSSii":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "Fill":
            {
                const shape = (0, _utils.getParamValue)("shape", node, tensorMap, context);
                const dtype = (0, _utils.getParamValue)("dtype", node, tensorMap, context);
                const value = (0, _utils.getParamValue)("value", node, tensorMap, context);
                return [
                    ops.fill(shape, value, dtype)
                ];
            }
        case "LinSpace":
            {
                const start = (0, _utils.getParamValue)("start", node, tensorMap, context);
                const stop = (0, _utils.getParamValue)("stop", node, tensorMap, context);
                const num = (0, _utils.getParamValue)("num", node, tensorMap, context);
                return [
                    ops.linspace(start, stop, num)
                ];
            }
        case "Multinomial":
            {
                const logits = (0, _utils.getParamValue)("logits", node, tensorMap, context);
                const numSamples = (0, _utils.getParamValue)("numSamples", node, tensorMap, context);
                const seed = (0, _utils.getParamValue)("seed", node, tensorMap, context);
                return [
                    ops.multinomial(logits, numSamples, seed)
                ];
            }
        case "OneHot":
            {
                const indices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                const depth = (0, _utils.getParamValue)("depth", node, tensorMap, context);
                const onValue = (0, _utils.getParamValue)("onValue", node, tensorMap, context);
                const offValue = (0, _utils.getParamValue)("offValue", node, tensorMap, context);
                const dtype = (0, _utils.getParamValue)("dtype", node, tensorMap, context);
                return [
                    ops.oneHot(indices, depth, onValue, offValue, dtype)
                ];
            }
        case "Ones":
            return [
                ops.ones((0, _utils.getParamValue)("shape", node, tensorMap, context), (0, _utils.getParamValue)("dtype", node, tensorMap, context))
            ];
        case "OnesLike":
            return [
                ops.onesLike((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "RandomStandardNormal":
            return [
                ops.randomStandardNormal((0, _utils.getParamValue)("shape", node, tensorMap, context), (0, _utils.getParamValue)("dtype", node, tensorMap, context), (0, _utils.getParamValue)("seed", node, tensorMap, context))
            ];
        case "RandomUniform":
            return [
                ops.randomUniform(// tslint:disable-next-line:no-any
                (0, _utils.getParamValue)("shape", node, tensorMap, context), (0, _utils.getParamValue)("minval", node, tensorMap, context), (0, _utils.getParamValue)("maxval", node, tensorMap, context), (0, _utils.getParamValue)("dtype", node, tensorMap, context))
            ];
        case "RandomUniformInt":
            return [
                ops.randomUniformInt((0, _utils.getParamValue)("shape", node, tensorMap, context), (0, _utils.getParamValue)("minval", node, tensorMap, context), (0, _utils.getParamValue)("maxval", node, tensorMap, context), (0, _utils.getParamValue)("seed", node, tensorMap, context))
            ];
        case "Range":
            {
                const start = (0, _utils.getParamValue)("start", node, tensorMap, context);
                const stop = (0, _utils.getParamValue)("stop", node, tensorMap, context);
                const step = (0, _utils.getParamValue)("step", node, tensorMap, context);
                return [
                    ops.range(start, stop, step, (0, _utils.getParamValue)("dtype", node, tensorMap, context))
                ];
            }
        case "TruncatedNormal":
            {
                const shape = (0, _utils.getParamValue)("shape", node, tensorMap, context);
                const mean = (0, _utils.getParamValue)("mean", node, tensorMap, context);
                const stdDev = (0, _utils.getParamValue)("stdDev", node, tensorMap, context);
                const seed = (0, _utils.getParamValue)("seed", node, tensorMap, context);
                return [
                    ops.truncatedNormal(shape, mean, stdDev, (0, _utils.getParamValue)("dtype", node, tensorMap, context), seed)
                ];
            }
        case "Zeros":
            return [
                ops.zeros((0, _utils.getParamValue)("shape", node, tensorMap, context), (0, _utils.getParamValue)("dtype", node, tensorMap, context))
            ];
        case "ZerosLike":
            return [
                ops.zerosLike((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "creation";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"37XKU":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
function nmsParams(node, tensorMap, context) {
    const boxes = (0, _utils.getParamValue)("boxes", node, tensorMap, context);
    const scores = (0, _utils.getParamValue)("scores", node, tensorMap, context);
    const maxOutputSize = (0, _utils.getParamValue)("maxOutputSize", node, tensorMap, context);
    const iouThreshold = (0, _utils.getParamValue)("iouThreshold", node, tensorMap, context);
    const scoreThreshold = (0, _utils.getParamValue)("scoreThreshold", node, tensorMap, context);
    const softNmsSigma = (0, _utils.getParamValue)("softNmsSigma", node, tensorMap, context);
    return {
        boxes,
        scores,
        maxOutputSize,
        iouThreshold,
        scoreThreshold,
        softNmsSigma
    };
}
const executeOp = async (node, tensorMap, context, resourceManager, ops = _opsForConverter)=>{
    switch(node.op){
        case "NonMaxSuppressionV5":
            {
                const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = nmsParams(node, tensorMap, context);
                const result = await ops.image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
                return [
                    result.selectedIndices,
                    result.selectedScores
                ];
            }
        case "NonMaxSuppressionV4":
            {
                const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);
                const padToMaxOutputSize = (0, _utils.getParamValue)("padToMaxOutputSize", node, tensorMap, context);
                const result = await ops.image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
                return [
                    result.selectedIndices,
                    result.validOutputs
                ];
            }
        case "NonMaxSuppressionV3":
        case "NonMaxSuppressionV2":
            {
                const { boxes, scores, maxOutputSize, iouThreshold, scoreThreshold } = nmsParams(node, tensorMap, context);
                return [
                    await ops.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)
                ];
            }
        case "Where":
            {
                const condition = ops.cast((0, _utils.getParamValue)("condition", node, tensorMap, context), "bool");
                const result = [
                    await ops.whereAsync(condition)
                ];
                condition.dispose();
                return result;
            }
        case "ListDiff":
            return ops.setdiff1dAsync((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("y", node, tensorMap, context));
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "dynamic";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9cjVI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "LowerBound":
            {
                const sortedSequence = (0, _utils.getParamValue)("sortedSequence", node, tensorMap, context);
                const values = (0, _utils.getParamValue)("values", node, tensorMap, context);
                return [
                    ops.lowerBound(sortedSequence, values)
                ];
            }
        case "TopKV2":
            {
                const x = (0, _utils.getParamValue)("x", node, tensorMap, context);
                const k = (0, _utils.getParamValue)("k", node, tensorMap, context);
                const sorted = (0, _utils.getParamValue)("sorted", node, tensorMap, context);
                const result = ops.topk(x, k, sorted);
                return [
                    result.values,
                    result.indices
                ];
            }
        case "UpperBound":
            {
                const sortedSequence = (0, _utils.getParamValue)("sortedSequence", node, tensorMap, context);
                const values = (0, _utils.getParamValue)("values", node, tensorMap, context);
                return [
                    ops.upperBound(sortedSequence, values)
                ];
            }
        case "Unique":
            {
                const x = (0, _utils.getParamValue)("x", node, tensorMap, context);
                const result = ops.unique(x);
                return [
                    result.values,
                    result.indices
                ];
            }
        case "UniqueV2":
            {
                const x = (0, _utils.getParamValue)("x", node, tensorMap, context);
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const result = ops.unique(x, axis);
                return [
                    result.values,
                    result.indices
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "evaluation";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jqg8t":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "Const":
            return tensorMap[node.name];
        case "PlaceholderWithDefault":
            const def = (0, _utils.getParamValue)("default", node, tensorMap, context);
            return [
                (0, _utils.getTensor)(node.name, tensorMap, context) || def
            ];
        case "Placeholder":
            return [
                (0, _utils.getTensor)(node.name, tensorMap, context)
            ];
        case "Identity":
        case "StopGradient":
        case "FakeQuantWithMinMaxVars":
            {
                const data = (0, _utils.getParamValue)("x", node, tensorMap, context);
                return [
                    (0, _utils.cloneTensor)(data)
                ];
            }
        case "IdentityN":
            return (0, _utils.getParamValue)("x", node, tensorMap, context).map((t)=>(0, _utils.cloneTensor)(t));
        case "Snapshot":
            const snapshot = (0, _utils.getParamValue)("x", node, tensorMap, context);
            return [
                (0, _utils.cloneTensor)(snapshot)
            ];
        case "Shape":
            return [
                ops.tensor1d((0, _utils.getParamValue)("x", node, tensorMap, context).shape, "int32")
            ];
        case "ShapeN":
            return (0, _utils.getParamValue)("x", node, tensorMap, context).map((t)=>ops.tensor1d(t.shape));
        case "Size":
            return [
                ops.scalar((0, _utils.getParamValue)("x", node, tensorMap, context).size, "int32")
            ];
        case "Rank":
            return [
                ops.scalar((0, _utils.getParamValue)("x", node, tensorMap, context).rank, "int32")
            ];
        case "NoOp":
            return [
                ops.scalar(1)
            ];
        case "Print":
            const input = (0, _utils.getParamValue)("x", node, tensorMap, context);
            const data = (0, _utils.getParamValue)("data", node, tensorMap, context);
            const message = (0, _utils.getParamValue)("message", node, tensorMap, context);
            const summarize = (0, _utils.getParamValue)("summarize", node, tensorMap, context);
            console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance.");
            console.log(message);
            for(let i = 0; i < data.length; i++)console.log(Array.prototype.slice.call(data[i].dataSync()).slice(0, summarize));
            return [
                input
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "graph";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9g7sC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _hashTable = require("../../executor/hash_table");
var _utils = require("./utils");
const executeOp = async (node, tensorMap, context, resourceManager)=>{
    switch(node.op){
        case "HashTable":
        case "HashTableV2":
            {
                const existingTableHandle = resourceManager.getHashTableHandleByName(node.name);
                // Table is shared with initializer.
                if (existingTableHandle != null) return [
                    existingTableHandle
                ];
                else {
                    const keyDType = (0, _utils.getParamValue)("keyDType", node, tensorMap, context);
                    const valueDType = (0, _utils.getParamValue)("valueDType", node, tensorMap, context);
                    const hashTable = new (0, _hashTable.HashTable)(keyDType, valueDType);
                    resourceManager.addHashTable(node.name, hashTable);
                    return [
                        hashTable.handle
                    ];
                }
            }
        case "InitializeTable":
        case "InitializeTableV2":
        case "LookupTableImport":
        case "LookupTableImportV2":
            {
                const handle = (0, _utils.getParamValue)("tableHandle", node, tensorMap, context, resourceManager);
                const keys = (0, _utils.getParamValue)("keys", node, tensorMap, context);
                const values = (0, _utils.getParamValue)("values", node, tensorMap, context);
                const hashTable = resourceManager.getHashTableById(handle.id);
                return [
                    await hashTable.import(keys, values)
                ];
            }
        case "LookupTableFind":
        case "LookupTableFindV2":
            {
                const handle = (0, _utils.getParamValue)("tableHandle", node, tensorMap, context, resourceManager);
                const keys = (0, _utils.getParamValue)("keys", node, tensorMap, context);
                const defaultValue = (0, _utils.getParamValue)("defaultValue", node, tensorMap, context);
                const hashTable = resourceManager.getHashTableById(handle.id);
                return [
                    await hashTable.find(keys, defaultValue)
                ];
            }
        case "LookupTableSize":
        case "LookupTableSizeV2":
            {
                const handle = (0, _utils.getParamValue)("tableHandle", node, tensorMap, context, resourceManager);
                const hashTable = resourceManager.getHashTableById(handle.id);
                return [
                    hashTable.tensorSize()
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "hash_table";

},{"../../executor/hash_table":"fZZAu","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fZZAu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Hashtable contains a set of tensors, which can be accessed by key.
 */ parcelHelpers.export(exports, "HashTable", ()=>HashTable);
var _tfjsCore = require("@tensorflow/tfjs-core");
// tslint:disable-next-line: no-imports-from-dist
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
class HashTable {
    get id() {
        return this.handle.id;
    }
    /**
     * Constructor of HashTable. Creates a hash table.
     *
     * @param keyDType `dtype` of the table keys.
     * @param valueDType `dtype` of the table values.
     */ constructor(keyDType, valueDType){
        this.keyDType = keyDType;
        this.valueDType = valueDType;
        this.handle = (0, _tfjsCore.scalar)(0);
        // tslint:disable-next-line: no-any
        this.tensorMap = new Map();
        (0, _tfjsCore.keep)(this.handle);
    }
    /**
     * Dispose the tensors and handle and clear the hashtable.
     */ clearAndClose() {
        this.tensorMap.forEach((value)=>value.dispose());
        this.tensorMap.clear();
        this.handle.dispose();
    }
    /**
     * The number of items in the hash table.
     */ size() {
        return this.tensorMap.size;
    }
    /**
     * The number of items in the hash table as a rank-0 tensor.
     */ tensorSize() {
        return _opsForConverter.scalar(this.size(), "int32");
    }
    /**
     * Replaces the contents of the table with the specified keys and values.
     * @param keys Keys to store in the hashtable.
     * @param values Values to store in the hashtable.
     */ async import(keys, values) {
        this.checkKeyAndValueTensor(keys, values);
        // We only store the primitive values of the keys, this allows lookup
        // to be O(1).
        const $keys = await keys.data();
        // Clear the hashTable before inserting new values.
        this.tensorMap.forEach((value)=>value.dispose());
        this.tensorMap.clear();
        return (0, _tfjsCore.tidy)(()=>{
            const $values = (0, _tfjsCore.unstack)(values);
            const keysLength = $keys.length;
            const valuesLength = $values.length;
            (0, _tfjsCore.util).assert(keysLength === valuesLength, ()=>`The number of elements doesn't match, keys has ` + `${keysLength} elements, the values has ${valuesLength} ` + `elements.`);
            for(let i = 0; i < keysLength; i++){
                const key = $keys[i];
                const value = $values[i];
                (0, _tfjsCore.keep)(value);
                this.tensorMap.set(key, value);
            }
            return this.handle;
        });
    }
    /**
     * Looks up keys in a hash table, outputs the corresponding values.
     *
     * Performs batch lookups, for every element in the key tensor, `find`
     * stacks the corresponding value into the return tensor.
     *
     * If an element is not present in the table, the given `defaultValue` is
     * used.
     *
     * @param keys Keys to look up. Must have the same type as the keys of the
     *     table.
     * @param defaultValue The scalar `defaultValue` is the value output for keys
     *     not present in the table. It must also be of the same type as the
     *     table values.
     */ async find(keys, defaultValue) {
        this.checkKeyAndValueTensor(keys, defaultValue);
        const $keys = await keys.data();
        return (0, _tfjsCore.tidy)(()=>{
            const result = [];
            for(let i = 0; i < $keys.length; i++){
                const key = $keys[i];
                const value = this.findWithDefault(key, defaultValue);
                result.push(value);
            }
            return (0, _tfjsCore.stack)(result);
        });
    }
    // tslint:disable-next-line: no-any
    findWithDefault(key, defaultValue) {
        const result = this.tensorMap.get(key);
        return result != null ? result : defaultValue;
    }
    checkKeyAndValueTensor(key, value) {
        if (key.dtype !== this.keyDType) throw new Error(`Expect key dtype ${this.keyDType}, but got ` + `${key.dtype}`);
        if (value.dtype !== this.valueDType) throw new Error(`Expect value dtype ${this.valueDType}, but got ` + `${value.dtype}`);
    }
}

},{"@tensorflow/tfjs-core":"21Ftl","@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5sGLS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "ResizeBilinear":
            {
                const images = (0, _utils.getParamValue)("images", node, tensorMap, context);
                const size = (0, _utils.getParamValue)("size", node, tensorMap, context);
                const alignCorners = (0, _utils.getParamValue)("alignCorners", node, tensorMap, context);
                const halfPixelCenters = (0, _utils.getParamValue)("halfPixelCenters", node, tensorMap, context);
                return [
                    ops.image.resizeBilinear(images, [
                        size[0],
                        size[1]
                    ], alignCorners, halfPixelCenters)
                ];
            }
        case "ResizeNearestNeighbor":
            {
                const images = (0, _utils.getParamValue)("images", node, tensorMap, context);
                const size = (0, _utils.getParamValue)("size", node, tensorMap, context);
                const alignCorners = (0, _utils.getParamValue)("alignCorners", node, tensorMap, context);
                const halfPixelCenters = (0, _utils.getParamValue)("halfPixelCenters", node, tensorMap, context);
                return [
                    ops.image.resizeNearestNeighbor(images, [
                        size[0],
                        size[1]
                    ], alignCorners, halfPixelCenters)
                ];
            }
        case "CropAndResize":
            {
                const image = (0, _utils.getParamValue)("image", node, tensorMap, context);
                const boxes = (0, _utils.getParamValue)("boxes", node, tensorMap, context);
                const boxInd = (0, _utils.getParamValue)("boxInd", node, tensorMap, context);
                const cropSize = (0, _utils.getParamValue)("cropSize", node, tensorMap, context);
                const method = (0, _utils.getParamValue)("method", node, tensorMap, context);
                const extrapolationValue = (0, _utils.getParamValue)("extrapolationValue", node, tensorMap, context);
                return [
                    ops.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)
                ];
            }
        case "ImageProjectiveTransformV3":
            {
                const images = (0, _utils.getParamValue)("images", node, tensorMap, context);
                const transforms = (0, _utils.getParamValue)("transforms", node, tensorMap, context);
                const outputShape = (0, _utils.getParamValue)("outputShape", node, tensorMap, context);
                const fillValue = (0, _utils.getParamValue)("fillValue", node, tensorMap, context);
                const interpolation = (0, _utils.getParamValue)("interpolation", node, tensorMap, context);
                const fillMode = (0, _utils.getParamValue)("fillMode", node, tensorMap, context);
                return [
                    ops.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "image";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"30ObX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "Equal":
            return [
                ops.equal((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "NotEqual":
            return [
                ops.notEqual((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Greater":
            return [
                ops.greater((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "GreaterEqual":
            return [
                ops.greaterEqual((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Less":
            return [
                ops.less((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "LessEqual":
            return [
                ops.lessEqual((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "LogicalAnd":
            return [
                ops.logicalAnd((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "LogicalNot":
            return [
                ops.logicalNot((0, _utils.getParamValue)("a", node, tensorMap, context))
            ];
        case "LogicalOr":
            return [
                ops.logicalOr((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "Select":
        case "SelectV2":
            return [
                ops.where((0, _utils.getParamValue)("condition", node, tensorMap, context), (0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        case "BitwiseAnd":
            return [
                ops.bitwiseAnd((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "logical";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hCdlL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "BatchMatMul":
        case "BatchMatMulV2":
        case "MatMul":
            return [
                ops.matMul((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("b", node, tensorMap, context), (0, _utils.getParamValue)("transposeA", node, tensorMap, context), (0, _utils.getParamValue)("transposeB", node, tensorMap, context))
            ];
        case "Einsum":
            return [
                ops.einsum((0, _utils.getParamValue)("equation", node, tensorMap, context), ...(0, _utils.getParamValue)("tensors", node, tensorMap, context))
            ];
        case "Transpose":
            return [
                ops.transpose((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("perm", node, tensorMap, context))
            ];
        case "_FusedMatMul":
            const [extraOp, activationFunc] = (0, _utils.getParamValue)("fusedOps", node, tensorMap, context);
            const isBiasAdd = extraOp === "biasadd";
            const isPrelu = activationFunc === "prelu";
            const numArgs = (0, _utils.getParamValue)("numArgs", node, tensorMap, context);
            const leakyreluAlpha = (0, _utils.getParamValue)("leakyreluAlpha", node, tensorMap, context);
            if (isBiasAdd) {
                if (isPrelu && numArgs !== 2) throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");
                if (!isPrelu && numArgs !== 1) throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.");
            }
            const [biasArg, preluArg] = (0, _utils.getParamValue)("args", node, tensorMap, context);
            return [
                ops.fused.matMul({
                    a: (0, _utils.getParamValue)("a", node, tensorMap, context),
                    b: (0, _utils.getParamValue)("b", node, tensorMap, context),
                    transposeA: (0, _utils.getParamValue)("transposeA", node, tensorMap, context),
                    transposeB: (0, _utils.getParamValue)("transposeB", node, tensorMap, context),
                    bias: biasArg,
                    activation: activationFunc,
                    preluActivationWeights: preluArg,
                    leakyreluAlpha
                })
            ];
        case "MatrixBandPart":
            return [
                ops.linalg.bandPart((0, _utils.getParamValue)("a", node, tensorMap, context), (0, _utils.getParamValue)("numLower", node, tensorMap, context), (0, _utils.getParamValue)("numUpper", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "matrices";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iGve1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "EuclideanNorm":
            return [
                ops.euclideanNorm((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("axis", node, tensorMap, context), (0, _utils.getParamValue)("keepDims", node, tensorMap, context))
            ];
        case "FusedBatchNorm":
        case "FusedBatchNormV2":
            return [
                ops.batchNorm((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("mean", node, tensorMap, context), (0, _utils.getParamValue)("variance", node, tensorMap, context), (0, _utils.getParamValue)("offset", node, tensorMap, context), (0, _utils.getParamValue)("scale", node, tensorMap, context), (0, _utils.getParamValue)("epsilon", node, tensorMap, context))
            ];
        case "FusedBatchNormV3":
            return [
                ops.batchNorm((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("mean", node, tensorMap, context), (0, _utils.getParamValue)("variance", node, tensorMap, context), (0, _utils.getParamValue)("offset", node, tensorMap, context), (0, _utils.getParamValue)("scale", node, tensorMap, context), (0, _utils.getParamValue)("epsilon", node, tensorMap, context))
            ];
        case "LRN":
            return [
                ops.localResponseNormalization((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("radius", node, tensorMap, context), (0, _utils.getParamValue)("bias", node, tensorMap, context), (0, _utils.getParamValue)("alpha", node, tensorMap, context), (0, _utils.getParamValue)("beta", node, tensorMap, context))
            ];
        case "Softmax":
            return [
                ops.softmax((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "LogSoftmax":
            return [
                ops.logSoftmax((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "normalization";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aEvoT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "RaggedGather":
            {
                const { outputNestedSplits, outputDenseValues } = ops.raggedGather((0, _utils.getParamValue)("paramsNestedSplits", node, tensorMap, context), (0, _utils.getParamValue)("paramsDenseValues", node, tensorMap, context), (0, _utils.getParamValue)("indices", node, tensorMap, context), (0, _utils.getParamValue)("outputRaggedRank", node, tensorMap, context));
                return outputNestedSplits.concat(outputDenseValues);
            }
        case "RaggedRange":
            {
                const { rtNestedSplits, rtDenseValues } = ops.raggedRange((0, _utils.getParamValue)("starts", node, tensorMap, context), (0, _utils.getParamValue)("limits", node, tensorMap, context), (0, _utils.getParamValue)("splits", node, tensorMap, context));
                return [
                    rtNestedSplits,
                    rtDenseValues
                ];
            }
        case "RaggedTensorToTensor":
            return [
                ops.raggedTensorToTensor((0, _utils.getParamValue)("shape", node, tensorMap, context), (0, _utils.getParamValue)("values", node, tensorMap, context), (0, _utils.getParamValue)("defaultValue", node, tensorMap, context), (0, _utils.getParamValue)("rowPartitionTensors", node, tensorMap, context), (0, _utils.getParamValue)("rowPartitionTypes", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "ragged";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bj9OH":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "Max":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const keepDims = (0, _utils.getParamValue)("keepDims", node, tensorMap, context);
                return [
                    ops.max((0, _utils.getParamValue)("x", node, tensorMap, context), axis, keepDims)
                ];
            }
        case "Mean":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const keepDims = (0, _utils.getParamValue)("keepDims", node, tensorMap, context);
                return [
                    ops.mean((0, _utils.getParamValue)("x", node, tensorMap, context), axis, keepDims)
                ];
            }
        case "Min":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const keepDims = (0, _utils.getParamValue)("keepDims", node, tensorMap, context);
                return [
                    ops.min((0, _utils.getParamValue)("x", node, tensorMap, context), axis, keepDims)
                ];
            }
        case "Sum":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const keepDims = (0, _utils.getParamValue)("keepDims", node, tensorMap, context);
                return [
                    ops.sum((0, _utils.getParamValue)("x", node, tensorMap, context), axis, keepDims)
                ];
            }
        case "All":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const keepDims = (0, _utils.getParamValue)("keepDims", node, tensorMap, context);
                return [
                    ops.all((0, _utils.getParamValue)("x", node, tensorMap, context), axis, keepDims)
                ];
            }
        case "Any":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const keepDims = (0, _utils.getParamValue)("keepDims", node, tensorMap, context);
                return [
                    ops.any((0, _utils.getParamValue)("x", node, tensorMap, context), axis, keepDims)
                ];
            }
        case "ArgMax":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                return [
                    ops.argMax((0, _utils.getParamValue)("x", node, tensorMap, context), axis)
                ];
            }
        case "ArgMin":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                return [
                    ops.argMin((0, _utils.getParamValue)("x", node, tensorMap, context), axis)
                ];
            }
        case "Prod":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const keepDims = (0, _utils.getParamValue)("keepDims", node, tensorMap, context);
                return [
                    ops.prod((0, _utils.getParamValue)("x", node, tensorMap, context), axis, keepDims)
                ];
            }
        case "Cumprod":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const exclusive = (0, _utils.getParamValue)("exclusive", node, tensorMap, context);
                const reverse = (0, _utils.getParamValue)("reverse", node, tensorMap, context);
                return [
                    ops.cumprod((0, _utils.getParamValue)("x", node, tensorMap, context), axis, exclusive, reverse)
                ];
            }
        case "Cumsum":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const exclusive = (0, _utils.getParamValue)("exclusive", node, tensorMap, context);
                const reverse = (0, _utils.getParamValue)("reverse", node, tensorMap, context);
                return [
                    ops.cumsum((0, _utils.getParamValue)("x", node, tensorMap, context), axis, exclusive, reverse)
                ];
            }
        case "Bincount":
            const x = (0, _utils.getParamValue)("x", node, tensorMap, context);
            const weights = (0, _utils.getParamValue)("weights", node, tensorMap, context);
            const size = (0, _utils.getParamValue)("size", node, tensorMap, context);
            return [
                ops.bincount(x, weights, size)
            ];
        case "DenseBincount":
            {
                const x = (0, _utils.getParamValue)("x", node, tensorMap, context);
                const weights = (0, _utils.getParamValue)("weights", node, tensorMap, context);
                const size = (0, _utils.getParamValue)("size", node, tensorMap, context);
                const binaryOutput = (0, _utils.getParamValue)("binaryOutput", node, tensorMap, context);
                return [
                    ops.denseBincount(x, weights, size, binaryOutput)
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "reduction";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3HYHo":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _tfjsCore = require("@tensorflow/tfjs-core");
// tslint:disable-next-line: no-imports-from-dist
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "ConcatV2":
        case "Concat":
            {
                const n = (0, _utils.getParamValue)("n", node, tensorMap, context);
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                let inputs = (0, _utils.getParamValue)("tensors", node, tensorMap, context);
                inputs = inputs.slice(0, n);
                return [
                    ops.concat(inputs, axis)
                ];
            }
        case "Gather":
            {
                const input = (0, _utils.getParamValue)("x", node, tensorMap, context);
                const indices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                return [
                    ops.gather(input, ops.cast(indices, "int32"), 0)
                ];
            }
        case "GatherV2":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const batchDims = (0, _utils.getParamValue)("batchDims", node, tensorMap, context);
                const input = (0, _utils.getParamValue)("x", node, tensorMap, context);
                const indices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                return [
                    ops.gather(input, ops.cast(indices, "int32"), axis, batchDims)
                ];
            }
        case "Reverse":
            {
                const dims = (0, _utils.getParamValue)("dims", node, tensorMap, context);
                const axis = [];
                for(let i = 0; i < dims.length; i++)if (dims[i]) axis.push(i);
                const input = (0, _utils.getParamValue)("x", node, tensorMap, context);
                return [
                    ops.reverse(input, axis)
                ];
            }
        case "ReverseV2":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const input = (0, _utils.getParamValue)("x", node, tensorMap, context);
                return [
                    ops.reverse(input, axis)
                ];
            }
        case "Slice":
            {
                // tslint:disable-next-line:no-any
                const begin = (0, _utils.getParamValue)("begin", node, tensorMap, context);
                // tslint:disable-next-line:no-any
                const size = (0, _utils.getParamValue)("size", node, tensorMap, context);
                return [
                    ops.slice((0, _utils.getParamValue)("x", node, tensorMap, context), begin, size)
                ];
            }
        case "StridedSlice":
            {
                const begin = (0, _utils.getParamValue)("begin", node, tensorMap, context);
                const end = (0, _utils.getParamValue)("end", node, tensorMap, context);
                const strides = (0, _utils.getParamValue)("strides", node, tensorMap, context);
                const beginMask = (0, _utils.getParamValue)("beginMask", node, tensorMap, context);
                const endMask = (0, _utils.getParamValue)("endMask", node, tensorMap, context);
                const ellipsisMask = (0, _utils.getParamValue)("ellipsisMask", node, tensorMap, context);
                const newAxisMask = (0, _utils.getParamValue)("newAxisMask", node, tensorMap, context);
                const shrinkAxisMask = (0, _utils.getParamValue)("shrinkAxisMask", node, tensorMap, context);
                const tensor = (0, _utils.getParamValue)("x", node, tensorMap, context);
                return [
                    ops.stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)
                ];
            }
        case "Pack":
            return (0, _tfjsCore.tidy)(()=>{
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const tensors = (0, _utils.getParamValue)("tensors", node, tensorMap, context);
                // Reshape the tensors to the first tensor's shape if they don't
                // match.
                const shape = tensors[0].shape;
                const squeezedShape = ops.squeeze(tensors[0]).shape;
                const mapped = tensors.map((tensor)=>{
                    const sameShape = (0, _tfjsCore.util).arraysEqual(tensor.shape, shape);
                    if (!sameShape && !(0, _tfjsCore.util).arraysEqual(ops.squeeze(tensor).shape, squeezedShape)) throw new Error("the input tensors shape does not match");
                    return sameShape ? tensor : ops.reshape(tensor, shape);
                });
                return [
                    ops.stack(mapped, axis)
                ];
            });
        case "Unpack":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const tensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                return ops.unstack(tensor, axis);
            }
        case "Tile":
            {
                const reps = (0, _utils.getParamValue)("reps", node, tensorMap, context);
                return [
                    ops.tile((0, _utils.getParamValue)("x", node, tensorMap, context), reps)
                ];
            }
        case "Split":
        case "SplitV":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                const numOrSizeSplits = (0, _utils.getParamValue)("numOrSizeSplits", node, tensorMap, context);
                const tensor = (0, _utils.getParamValue)("x", node, tensorMap, context);
                return ops.split(tensor, numOrSizeSplits, axis);
            }
        case "ScatterNd":
            {
                const indices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                const values = (0, _utils.getParamValue)("values", node, tensorMap, context);
                const shape = (0, _utils.getParamValue)("shape", node, tensorMap, context);
                return [
                    ops.scatterND(indices, values, shape)
                ];
            }
        case "GatherNd":
            {
                const x = (0, _utils.getParamValue)("x", node, tensorMap, context);
                const indices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                return [
                    ops.gatherND(x, indices)
                ];
            }
        case "SparseToDense":
            {
                const indices = (0, _utils.getParamValue)("sparseIndices", node, tensorMap, context);
                const shape = (0, _utils.getParamValue)("outputShape", node, tensorMap, context);
                const sparseValues = (0, _utils.getParamValue)("sparseValues", node, tensorMap, context);
                const defaultValue = (0, _utils.getParamValue)("defaultValue", node, tensorMap, context);
                return [
                    ops.sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? defaultValue : ops.cast(defaultValue, sparseValues.dtype))
                ];
            }
        case "TensorScatterUpdate":
            {
                const indices = (0, _utils.getParamValue)("indices", node, tensorMap, context);
                const values = (0, _utils.getParamValue)("values", node, tensorMap, context);
                const tensor = (0, _utils.getParamValue)("tensor", node, tensorMap, context);
                return [
                    ops.tensorScatterUpdate(tensor, indices, values)
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "slice_join";

},{"@tensorflow/tfjs-core":"21Ftl","@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cwC1Y":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "SparseFillEmptyRows":
            {
                const { outputIndices, outputValues, emptyRowIndicator, reverseIndexMap } = ops.sparse.sparseFillEmptyRows((0, _utils.getParamValue)("indices", node, tensorMap, context), (0, _utils.getParamValue)("values", node, tensorMap, context), (0, _utils.getParamValue)("denseShape", node, tensorMap, context), (0, _utils.getParamValue)("defaultValue", node, tensorMap, context));
                return [
                    outputIndices,
                    outputValues,
                    emptyRowIndicator,
                    reverseIndexMap
                ];
            }
        case "SparseReshape":
            {
                const { outputIndices, outputShape } = ops.sparse.sparseReshape((0, _utils.getParamValue)("inputIndices", node, tensorMap, context), (0, _utils.getParamValue)("inputShape", node, tensorMap, context), (0, _utils.getParamValue)("newShape", node, tensorMap, context));
                return [
                    outputIndices,
                    outputShape
                ];
            }
        case "SparseSegmentMean":
            {
                const outputData = ops.sparse.sparseSegmentMean((0, _utils.getParamValue)("data", node, tensorMap, context), (0, _utils.getParamValue)("indices", node, tensorMap, context), (0, _utils.getParamValue)("segmentIds", node, tensorMap, context));
                return [
                    outputData
                ];
            }
        case "SparseSegmentSum":
            {
                const outputData = ops.sparse.sparseSegmentSum((0, _utils.getParamValue)("data", node, tensorMap, context), (0, _utils.getParamValue)("indices", node, tensorMap, context), (0, _utils.getParamValue)("segmentIds", node, tensorMap, context));
                return [
                    outputData
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "sparse";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jduek":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "FFT":
            return [
                ops.fft((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "IFFT":
            return [
                ops.ifft((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "RFFT":
            return [
                ops.rfft((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        case "IRFFT":
            return [
                ops.irfft((0, _utils.getParamValue)("x", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "spectral";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4NdVa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "StaticRegexReplace":
            return [
                ops.string.staticRegexReplace((0, _utils.getParamValue)("input", node, tensorMap, context), (0, _utils.getParamValue)("pattern", node, tensorMap, context), (0, _utils.getParamValue)("rewrite", node, tensorMap, context), (0, _utils.getParamValue)("replaceGlobal", node, tensorMap, context))
            ];
        case "StringNGrams":
            {
                const { nGrams, nGramsSplits } = ops.string.stringNGrams((0, _utils.getParamValue)("data", node, tensorMap, context), (0, _utils.getParamValue)("dataSplits", node, tensorMap, context), (0, _utils.getParamValue)("separator", node, tensorMap, context), (0, _utils.getParamValue)("nGramWidths", node, tensorMap, context), (0, _utils.getParamValue)("leftPad", node, tensorMap, context), (0, _utils.getParamValue)("rightPad", node, tensorMap, context), (0, _utils.getParamValue)("padWidth", node, tensorMap, context), (0, _utils.getParamValue)("preserveShortSequences", node, tensorMap, context));
                return [
                    nGrams,
                    nGramsSplits
                ];
            }
        case "StringSplit":
            {
                const { indices, values, shape } = ops.string.stringSplit((0, _utils.getParamValue)("input", node, tensorMap, context), (0, _utils.getParamValue)("delimiter", node, tensorMap, context), (0, _utils.getParamValue)("skipEmpty", node, tensorMap, context));
                return [
                    indices,
                    values,
                    shape
                ];
            }
        case "StringToHashBucketFast":
            {
                const output = ops.string.stringToHashBucketFast((0, _utils.getParamValue)("input", node, tensorMap, context), (0, _utils.getParamValue)("numBuckets", node, tensorMap, context));
                return [
                    output
                ];
            }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "string";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3LErR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // tslint:disable-next-line: no-imports-from-dist
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "executeOp", ()=>executeOp);
parcelHelpers.export(exports, "CATEGORY", ()=>CATEGORY);
var _opsForConverter = require("@tensorflow/tfjs-core/dist/ops/ops_for_converter");
var _utils = require("./utils");
const executeOp = (node, tensorMap, context, ops = _opsForConverter)=>{
    switch(node.op){
        case "Cast":
            return [
                ops.cast((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("dtype", node, tensorMap, context))
            ];
        case "ExpandDims":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                return [
                    ops.expandDims((0, _utils.getParamValue)("x", node, tensorMap, context), axis)
                ];
            }
        case "Squeeze":
            {
                const axis = (0, _utils.getParamValue)("axis", node, tensorMap, context);
                return [
                    ops.squeeze((0, _utils.getParamValue)("x", node, tensorMap, context), axis)
                ];
            }
        case "Reshape":
            return [
                ops.reshape((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("shape", node, tensorMap, context))
            ];
        case "EnsureShape":
            return [
                ops.ensureShape((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("shape", node, tensorMap, context))
            ];
        case "MirrorPad":
            return [
                ops.mirrorPad((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("padding", node, tensorMap, context), (0, _utils.getParamValue)("mode", node, tensorMap, context))
            ];
        case "PadV2":
        case "Pad":
            return [
                ops.pad((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("padding", node, tensorMap, context), (0, _utils.getParamValue)("constantValue", node, tensorMap, context))
            ];
        case "SpaceToBatchND":
            {
                const blockShape = (0, _utils.getParamValue)("blockShape", node, tensorMap, context);
                const paddings = (0, _utils.getParamValue)("paddings", node, tensorMap, context);
                return [
                    ops.spaceToBatchND((0, _utils.getParamValue)("x", node, tensorMap, context), blockShape, paddings)
                ];
            }
        case "BatchToSpaceND":
            {
                const blockShape = (0, _utils.getParamValue)("blockShape", node, tensorMap, context);
                const crops = (0, _utils.getParamValue)("crops", node, tensorMap, context);
                return [
                    ops.batchToSpaceND((0, _utils.getParamValue)("x", node, tensorMap, context), blockShape, crops)
                ];
            }
        case "DepthToSpace":
            {
                const blockSize = (0, _utils.getParamValue)("blockSize", node, tensorMap, context);
                const dataFormat = (0, _utils.getParamValue)("dataFormat", node, tensorMap, context).toUpperCase();
                return [
                    ops.depthToSpace((0, _utils.getParamValue)("x", node, tensorMap, context), blockSize, dataFormat)
                ];
            }
        case "BroadcastTo":
            return [
                ops.broadcastTo((0, _utils.getParamValue)("x", node, tensorMap, context), (0, _utils.getParamValue)("shape", node, tensorMap, context))
            ];
        case "BroadcastArgs":
            return [
                ops.broadcastArgs((0, _utils.getParamValue)("s0", node, tensorMap, context), (0, _utils.getParamValue)("s1", node, tensorMap, context))
            ];
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
const CATEGORY = "transformation";

},{"@tensorflow/tfjs-core/dist/ops/ops_for_converter":"iUHBn","./utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gK8yG":[function(require,module,exports) {
/**
 * ExecutionContext captures the runtime environment of the node. It keeps
 * track of the current frame and iteration for the control flow ops.
 *
 * For example, typical Dynamic RNN model may contain loops, for which
 * TensorFlow will generate graphs with Enter/Exit nodes to control the
 * current execution frame, and NextIteration Nodes for iteration id increment.
 * For model with branch logic, TensorFLow will generate Switch/Merge ops.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ExecutionContext", ()=>ExecutionContext);
class ExecutionContext {
    constructor(weightMap = {}, tensorArrayMap = {}, tensorListMap = {}, functionMap = {}, parseNodeNameCache){
        this.weightMap = weightMap;
        this.tensorArrayMap = tensorArrayMap;
        this.tensorListMap = tensorListMap;
        this.functionMap = functionMap;
        this.parseNodeNameCache = parseNodeNameCache;
        this.rootContext = {
            id: 0,
            frameName: "",
            iterationId: 0
        };
        this.contexts = [
            this.rootContext
        ];
        this.lastId = 0;
        this.generateCurrentContextIds();
    }
    newFrame(id, frameName) {
        return {
            id,
            frameName,
            iterationId: 0
        };
    }
    /**
     * Set the current context
     * @param contexts: ExecutionContextInfo[] the current path of execution
     * frames
     */ set currentContext(contexts) {
        if (this.contexts !== contexts) {
            this.contexts = contexts;
            this.generateCurrentContextIds();
        }
    }
    get currentContext() {
        return this.contexts;
    }
    /**
     * Returns the current context in string format.
     */ get currentContextId() {
        return this._currentContextIds[0];
    }
    /**
     * Returns the current context and all parent contexts in string format.
     * This allow access to the nodes in the current and parent frames.
     */ get currentContextIds() {
        return this._currentContextIds;
    }
    generateCurrentContextIds() {
        const names = [];
        for(let i = 0; i < this.contexts.length - 1; i++){
            const contexts = this.contexts.slice(0, this.contexts.length - i);
            names.push(this.contextIdforContexts(contexts));
        }
        names.push("");
        this._currentContextIds = names;
    }
    contextIdforContexts(contexts) {
        return contexts ? contexts.map((context)=>context.id === 0 && context.iterationId === 0 ? "" : `${context.frameName}-${context.iterationId}`).join("/") : "";
    }
    /**
     * Enter a new frame, a new context is pushed on the current context list.
     * @param frameId new frame id
     */ enterFrame(frameId) {
        if (this.contexts) {
            this.lastId++;
            this.contexts = this.contexts.slice();
            this.contexts.push(this.newFrame(this.lastId, frameId));
            this._currentContextIds.unshift(this.contextIdforContexts(this.contexts));
        }
    }
    /**
     * Exit the current frame, the last context is removed from the current
     * context list.
     */ exitFrame() {
        if (this.contexts && this.contexts.length > 1) {
            this.contexts = this.contexts.slice();
            this.contexts.splice(-1);
            this.currentContextIds.shift();
        } else throw new Error("Cannot exit frame, the context is empty");
    }
    /**
     * Enter the next iteration of a loop, the iteration id of last context is
     * increased.
     */ nextIteration() {
        if (this.contexts && this.contexts.length > 0) {
            this.contexts = this.contexts.slice();
            this.lastId++;
            const context = Object.assign({}, this.contexts[this.contexts.length - 1]);
            context.iterationId += 1;
            context.id = this.lastId;
            this.contexts.splice(-1, 1, context);
            this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts));
        } else throw new Error("Cannot increase frame iteration, the context is empty");
    }
    getWeight(name) {
        return this.weightMap[name];
    }
    addTensorArray(tensorArray) {
        this.tensorArrayMap[tensorArray.id] = tensorArray;
    }
    getTensorArray(id) {
        return this.tensorArrayMap[id];
    }
    addTensorList(tensorList) {
        this.tensorListMap[tensorList.id] = tensorList;
    }
    getTensorList(id) {
        return this.tensorListMap[id];
    }
    dispose(keepIds) {
        for(const key in this.tensorArrayMap)this.tensorArrayMap[key].clearAndClose(keepIds);
        for(const key in this.tensorListMap)this.tensorListMap[key].clearAndClose(keepIds);
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kpmwj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Given graph inputs and desired outputs, find the minimal set of nodes
 * to execute in order to compute the outputs. In addition return other useful
 * info such:
 * - Missing inputs needed to compute the output.
 * - Whether the subgraph contains dynamic ops (control flow, dynamic shape).
 * - Alternative inputs in order to avoid async (dynamic op) execution.
 */ parcelHelpers.export(exports, "getExecutionSubgraph", ()=>getExecutionSubgraph);
/**
 * Given the execution info, return a list of nodes in topological order that
 * need to be executed to compute the output.
 */ parcelHelpers.export(exports, "getNodesInTopologicalOrder", ()=>getNodesInTopologicalOrder);
/**
 * Given the execution info, return a map from node name to the disposable
 * node name list after its execution.
 *
 * @returns A map from node name to disposable nodes after its
 *     execution. That is, for a node `x`, `nodeLiveUntilMap[x]` indicates
 *     all nodes which their intermediate tensors should be disposed after `x`
 *     being executed.
 */ parcelHelpers.export(exports, "getNodeLiveUntilMap", ()=>getNodeLiveUntilMap);
parcelHelpers.export(exports, "isControlFlow", ()=>isControlFlow);
parcelHelpers.export(exports, "isDynamicShape", ()=>isDynamicShape);
parcelHelpers.export(exports, "isHashTable", ()=>isHashTable);
var _utils = require("../operations/executors/utils");
function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) {
    const usedNodes = new Set();
    const missingInputs = [];
    let dynamicNode = null;
    let syncInputs = null;
    // Start with the outputs, going backwards and find all the nodes that are
    // needed to compute those outputs.
    const seen = new Set();
    const inputNodeNames = new Set(Object.keys(inputs).map((name)=>(0, _utils.parseNodeName)(name)[0]));
    initNodes = initNodes || [];
    const initNodeNames = new Set(initNodes.map((node)=>(0, _utils.parseNodeName)(node.name)[0]));
    const frontier = [
        ...outputs
    ];
    while(frontier.length > 0){
        const node = frontier.pop();
        if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) {
            if (dynamicNode == null) {
                dynamicNode = node;
                syncInputs = dynamicNode.children.map((child)=>child.name).filter((name)=>usedNodes.has(name));
            }
        }
        usedNodes.add(node.name);
        // Weights are dead end since we already have their values.
        if (weightMap[node.name] != null) continue;
        // This node is a dead end since it's one of the user-provided inputs.
        if (inputNodeNames.has(node.name)) continue;
        // This node is a dead end since it doesn't have any inputs.
        if (initNodeNames.has(node.name)) continue;
        if (node.inputs.length === 0) {
            missingInputs.push(node.name);
            continue;
        }
        node.inputs.forEach((input)=>{
            // Don't add to the frontier if it is already there.
            if (seen.has(input.name)) return;
            seen.add(input.name);
            frontier.push(input);
        });
    }
    return {
        inputs,
        outputs,
        usedNodes,
        missingInputs,
        dynamicNode,
        syncInputs
    };
}
function getNodesInTopologicalOrder(graph, executionInfo) {
    const { usedNodes, inputs } = executionInfo;
    const inputNodes = Object.keys(inputs).map((name)=>(0, _utils.parseNodeName)(name)[0]).map((name)=>graph.nodes[name]);
    const initNodes = graph.initNodes || [];
    const isUsed = (node)=>usedNodes.has(typeof node === "string" ? node : node.name);
    function unique(nodes) {
        return [
            ...new Map(nodes.map((node)=>[
                    node.name,
                    node
                ])).values()
        ];
    }
    const predefinedNodes = unique([
        ...inputNodes,
        ...graph.weights,
        ...initNodes
    ]).filter(isUsed);
    const allNodes = unique([
        ...predefinedNodes,
        ...Object.values(graph.nodes)
    ]).filter(isUsed);
    const nameToNode = new Map(allNodes.map((node)=>[
            node.name,
            node
        ]));
    const inCounts = {};
    for (const node of allNodes){
        inCounts[node.name] = inCounts[node.name] || 0;
        for (const child of node.children){
            // When the child is unused, set in counts to infinity so that it will
            // never be decreased to 0 and added to the execution list.
            if (!isUsed(child)) inCounts[child.name] = Number.POSITIVE_INFINITY;
            inCounts[child.name] = (inCounts[child.name] || 0) + 1;
        }
    }
    // Build execution order for all used nodes regardless whether they are
    // predefined or not.
    const frontier = Object.entries(inCounts).filter(([, inCount])=>inCount === 0).map(([name])=>name);
    const orderedNodeNames = [
        ...frontier
    ];
    while(frontier.length > 0){
        const nodeName = frontier.pop();
        const node = nameToNode.get(nodeName);
        for (const child of node.children.filter(isUsed))if (--inCounts[child.name] === 0) {
            orderedNodeNames.push(child.name);
            frontier.push(child.name);
        }
    }
    const orderedNodes = orderedNodeNames.map((name)=>nameToNode.get(name));
    const filteredOrderedNodes = filterPredefinedReachableNodes(orderedNodes, predefinedNodes);
    // TODO: Turn validation on/off with tf env flag.
    validateNodesExecutionOrder(filteredOrderedNodes, predefinedNodes);
    return filteredOrderedNodes;
}
/**
 * This is a helper function of `getNodesInTopologicalOrder`.
 * Returns ordered nodes reachable by at least one predefined node.
 * This can help us filter out redundant nodes from the returned node list.
 * For example:
 * If we have four nodes with dependencies like this:
 *   a --> b --> c --> d
 * when node `c` is predefined (e.g. given as an input tensor), we can
 * skip node `a` and `b` since their outputs will never be used.
 *
 * @param orderedNodes Graph nodes in execution order.
 * @param predefinedNodes Graph inputs, weights, and init nodes. Nodes in this
 *     list must have distinct names.
 */ function filterPredefinedReachableNodes(orderedNodes, predefinedNodes) {
    const nameToNode = new Map(orderedNodes.map((node)=>[
            node.name,
            node
        ]));
    // TODO: Filter out more nodes when >=2 nodes are predefined in a path.
    const stack = predefinedNodes.map((node)=>node.name);
    const predefinedReachableNodeNames = new Set(stack);
    // Perform a DFS starting from the set of all predefined nodes
    // to find the set of all nodes reachable from the predefined nodes.
    while(stack.length > 0){
        const nodeName = stack.pop();
        const node = nameToNode.get(nodeName);
        for (const child of node.children){
            if (!nameToNode.has(child.name) || predefinedReachableNodeNames.has(child.name)) continue;
            predefinedReachableNodeNames.add(child.name);
            stack.push(child.name);
        }
    }
    // Filter out unreachable nodes and build the ordered node list.
    const filteredOrderedNodes = orderedNodes.filter((node)=>predefinedReachableNodeNames.has(node.name));
    return filteredOrderedNodes;
}
class NodesExecutionOrderError extends Error {
    constructor(message){
        super(`NodesExecutionOrderError: ${message}`);
    }
}
/**
 * This is a helper function of `getNodesInTopologicalOrder`.
 * Validates property: given nodes `a` and `b`, Order(a) > Order(b) if `a`
 * is a child of `b`. This function throws an error if validation fails.
 *
 * @param orderedNodes Graph nodes in execution order.
 * @param predefinedNodes Graph inputs, weights, and init nodes. Nodes in this
 *     list must have distinct names.
 */ function validateNodesExecutionOrder(orderedNodes, predefinedNodes) {
    const nodeNameToOrder = new Map(orderedNodes.map((node, order)=>[
            node.name,
            order
        ]));
    const predefinedNodeNames = new Set(predefinedNodes.map((node)=>node.name));
    const isPredefined = (node)=>predefinedNodeNames.has(typeof node === "string" ? node : node.name);
    const willBeExecutedNodeNames = new Set(orderedNodes.map((node)=>node.name));
    const willBeExecuted = (node)=>willBeExecutedNodeNames.has(typeof node === "string" ? node : node.name);
    for (const node of orderedNodes){
        for (const child of node.children.filter(willBeExecuted)){
            if (!nodeNameToOrder.has(child.name)) throw new NodesExecutionOrderError(`Child ${child.name} of node ${node.name} is unreachable.`);
            if (nodeNameToOrder.get(node.name) > nodeNameToOrder.get(child.name)) throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run after its child ${child.name}.`);
        }
        if (!isPredefined(node)) for (const input of node.inputs){
            if (!nodeNameToOrder.has(input.name)) throw new NodesExecutionOrderError(`Input ${input.name} of node ${node.name} is unreachable.`);
            if (nodeNameToOrder.get(input.name) > nodeNameToOrder.get(node.name)) throw new NodesExecutionOrderError(`Node ${node.name} is scheduled to run before its input ${input.name}.`);
        }
    }
}
function getNodeLiveUntilMap(orderedNodes) {
    const nodeNameToOrder = new Map(orderedNodes.map((node, order)=>[
            node.name,
            order
        ]));
    const INF_LIFE = Number.MAX_SAFE_INTEGER;
    // Make control flow nodes (and consequently their direct parents)
    // live forever since they're tricky to track correctly.
    const selfLifespans = orderedNodes.map((node, nodeOrder)=>isControlFlow(node) ? INF_LIFE : nodeOrder);
    const getSelfLifeSpan = (node)=>{
        const selfLife = selfLifespans[nodeNameToOrder.get(node.name)];
        if (selfLife == null) // If nodeToOrder does not contain the node, it is unused or
        // unreachable in graph.
        return -1;
        return selfLife;
    };
    // `liveUntil[i]` points to the last node in the `orderedNodes` array that
    // may depend on tensors from node `i`. It indicates that all the
    // intermediate tensors from `orderedNodes[i]` should be disposed after
    // `orderedNodes[liveUntil[i]]` is executed.
    // A node lives long enough to pass on its tensors to its children.
    // It lives until at least `max(node's position, children's positions)`.
    const liveUntilOrders = orderedNodes.map((node, nodeOrder)=>{
        return node.children.map(getSelfLifeSpan).reduce((a, b)=>Math.max(a, b), selfLifespans[nodeOrder]);
    });
    // liveUntilMap:
    // - Key: Name of a node `x`
    // - Values: All nodes whose intermediate tensors should be disposed
    //           after `x` is executed.
    const liveUntilMap = new Map();
    for(let nodeOrder = 0; nodeOrder < orderedNodes.length; ++nodeOrder){
        const liveUntilOrder = liveUntilOrders[nodeOrder];
        if (liveUntilOrder === INF_LIFE) continue;
        const node = orderedNodes[nodeOrder];
        const liveUntilNode = orderedNodes[liveUntilOrder];
        if (!liveUntilMap.has(liveUntilNode.name)) liveUntilMap.set(liveUntilNode.name, []);
        liveUntilMap.get(liveUntilNode.name).push(node);
    }
    return liveUntilMap;
}
const CONTROL_FLOW_OPS = new Set([
    "Switch",
    "Merge",
    "Enter",
    "Exit",
    "NextIteration",
    "StatelessIf",
    "StatelessWhile",
    "if",
    "While"
]);
const DYNAMIC_SHAPE_OPS = new Set([
    "NonMaxSuppressionV2",
    "NonMaxSuppressionV3",
    "NonMaxSuppressionV5",
    "Where"
]);
const HASH_TABLE_OPS = new Set([
    "HashTable",
    "HashTableV2",
    "LookupTableImport",
    "LookupTableImportV2",
    "LookupTableFind",
    "LookupTableFindV2",
    "LookupTableSize",
    "LookupTableSizeV2"
]);
function isControlFlow(node) {
    return CONTROL_FLOW_OPS.has(node.op);
}
function isDynamicShape(node) {
    return DYNAMIC_SHAPE_OPS.has(node.op);
}
function isHashTable(node) {
    return HASH_TABLE_OPS.has(node.op);
}

},{"../operations/executors/utils":"lA8AN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9Yk8c":[function(require,module,exports) {
/**
 * Contains global resources of a model.
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ResourceManager", ()=>ResourceManager);
class ResourceManager {
    constructor(hashTableNameToHandle = {}, hashTableMap = {}){
        this.hashTableNameToHandle = hashTableNameToHandle;
        this.hashTableMap = hashTableMap;
    }
    /**
     * Register a `HashTable` in the resource manager.
     *
     * The `HashTable` can be retrieved by `resourceManager.getHashTableById`,
     * where id is the table handle tensor's id.
     *
     * @param name Op node name that creates the `HashTable`.
     * @param hashTable The `HashTable` to be added to resource manager.
     */ addHashTable(name, hashTable) {
        this.hashTableNameToHandle[name] = hashTable.handle;
        this.hashTableMap[hashTable.id] = hashTable;
    }
    /**
     * Get the table handle by node name.
     * @param name Op node name that creates the `HashTable`. This name is also
     *     used in the inputs list of lookup and import `HashTable` ops.
     */ getHashTableHandleByName(name) {
        return this.hashTableNameToHandle[name];
    }
    /**
     * Get the actual `HashTable` by its handle tensor's id.
     * @param id The id of the handle tensor.
     */ getHashTableById(id) {
        return this.hashTableMap[id];
    }
    /**
     * Dispose `ResourceManager`, including its hashTables and tensors in them.
     */ dispose() {
        for(const key in this.hashTableMap){
            this.hashTableMap[key].clearAndClose();
            delete this.hashTableMap[key];
        }
        for(const name in this.hashTableNameToHandle){
            this.hashTableNameToHandle[name].dispose();
            delete this.hashTableNameToHandle[name];
        }
    }
}

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"by8XT":[function(require,module,exports) {
/** @license See the LICENSE file. */ // This code is auto-generated, do not modify this file!
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "version", ()=>version);
const version = "4.21.0";

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"j6iw2":[function(require,module,exports) {
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    };
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        a = [
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            a,
            "object" == typeof window && window,
            "object" == typeof self && self,
            "object" == typeof global && global
        ];
        for(var b = 0; b < a.length; ++b){
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        }
        throw Error("Cannot find global object");
    }
    var y = ca(this);
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                resolve: f(this.I),
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        b.prototype.v = function(f, h) {
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                cancelable: !0
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                } : n;
            }
            var l, m, r = new b(function(p, n) {
                l = p;
                m = n;
            });
            this.T(k(f, l), k(h, m));
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        b.prototype.T = function(f, h) {
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            }
            var l = this;
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            this.u = !0;
        };
        b.resolve = d;
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                k(f);
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        };
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                        0 == n && l(p);
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                    var g = c++;
                    return {
                        value: b(g, a[g]),
                        done: !1
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                d = !0;
                return {
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                    value: void 0
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            if (null == d || d > e) d = e;
            d = Number(d);
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    z("Uint16Array.prototype.fill", F);
    z("Int32Array.prototype.fill", F);
    z("Uint32Array.prototype.fill", F);
    z("Float32Array.prototype.fill", F);
    z("Float64Array.prototype.fill", F);
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                var g = d[c];
                if (g === b || Object.is(g, b)) return !0;
            }
            return !1;
        };
    });
    z("String.prototype.includes", function(a) {
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        };
    });
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    function Ba(a) {
        var b;
        a: {
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                if (b = b.userAgent) break a;
            }
            b = "";
        }
        return -1 != b.indexOf(a);
    }
    var Ca = Array.prototype.map ? function(a, b) {
        return Array.prototype.map.call(a, b, void 0);
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    var Da = {}, Ea = null;
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                "+/",
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                "-_.",
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                k = b[k & 63];
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            k = d;
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                    l = a[e + 1], k = b[(l & 15) << 2] || d;
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            C: !0
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        return c;
    }
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    };
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    function M(a, b) {
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        this.i = this.l = -1;
    };
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        return !0;
    }
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            case 5:
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                L(a, a.h + 4);
                break;
            case 3:
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    }
    var O = "function" === typeof Symbol && "symbol" === typeof Symbol() ? Symbol() : void 0;
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            A: {
                value: b,
                configurable: !0,
                writable: !0,
                enumerable: !1
            }
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        return b;
    }
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        O ? a[O] && (a[O] &= ~b) : void 0 !== a.A && (a.A &= ~b);
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                writable: !0,
                enumerable: !1
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        var e = S(a, b);
        Array.isArray(e) || (e = Fb);
        var g = Q(e);
        g & 1 || Ab(e);
        if (d) g & 2 || P(e, 2), c & 1 || Object.freeze(e);
        else {
            d = !(c & 2);
            var f = g & 2;
            c & 1 || !f ? d && g & 16 && !f && zb(e, 16) : (e = Ab(Array.prototype.slice.call(e)), Kb(a, b, e));
        }
        return e;
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    function Mb(a, b) {
        var c = S(a, b);
        var d = null == c ? c : "number" === typeof c || "NaN" === c || "Infinity" === c || "-Infinity" === c ? Number(c) : void 0;
        null != d && d !== c && Kb(a, b, d);
        return d;
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    function Nb(a, b, c, d, e) {
        a.h || (a.h = {});
        var g = a.h[c], f = Lb(a, c, 3, e);
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            var h = f;
            g = [];
            var k = !!(Q(a.o) & 16);
            f = !!(Q(h) & 2);
            var l = h;
            !e && f && (h = Array.prototype.slice.call(h));
            for(var m = f, r = 0; r < h.length; r++){
                var p = h[r];
                var n = b, q = !1;
                q = void 0 === q ? !1 : q;
                p = Array.isArray(p) ? new n(p) : q ? new n : void 0;
                if (void 0 !== p) {
                    n = p.o;
                    var t = q = Q(n);
                    f && (t |= 2);
                    k && (t |= 16);
                    t != q && R(n, t);
                    n = t;
                    m = m || !!(2 & n);
                    g.push(p);
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            a.h[c] = g;
            k = Q(h);
            b = k | 33;
            b = m ? b & -9 : b | 8;
            k != b && (m = h, Object.isFrozen(m) && (m = Array.prototype.slice.call(m)), R(m, b), h = m);
            l !== h && Kb(a, c, h);
            (e || d && f) && P(g, 2);
            d && Object.freeze(g);
            return g;
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        e || (e = Object.isFrozen(g), d && !e ? Object.freeze(g) : !d && e && (g = Array.prototype.slice.call(g), a.h[c] = g));
        return g;
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    function Ob(a, b, c) {
        var d = !!(Q(a.o) & 2);
        b = Nb(a, b, c, d, d);
        a = Lb(a, c, 3, d);
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            for(d = 0; d < b.length; d++){
                c = b[d];
                if (Q(c.o) & 2) {
                    var e = Pb(c, !1);
                    e.j = c;
                } else e = c;
                c !== e && (b[d] = e, a[d] = e.o);
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            P(a, 8);
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        return b;
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    function V(a, b, c) {
        if (null != c && "number" !== typeof c) throw Error("Value of float/double field must be a number|null|undefined, found " + typeof c + ": " + c);
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        Hb(a);
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        c = null != d ? d : new c;
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        c.C() && zb(a, 8);
        return c;
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    function Rb(a, b) {
        return null == a ? b : a;
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    function W(a, b, c) {
        c = void 0 === c ? 0 : c;
        return Rb(Mb(a, b), c);
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    var Sb;
    function Tb(a) {
        switch(typeof a){
            case "number":
                return isFinite(a) ? a : String(a);
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                if (a) {
                    if (Array.isArray(a)) {
                        if (0 !== (Q(a) & 128)) return a = Array.prototype.slice.call(a), Ib(a), a;
                    } else {
                        if (Ia && null != a && a instanceof Uint8Array) return Ka(a);
                        if (a instanceof ib) {
                            var b = a.V;
                            return null == b ? "" : "string" === typeof b ? b : a.V = Ka(b);
                        }
                    }
                }
        }
        return a;
    }
    function Ub(a, b, c, d) {
        if (null != a) {
            if (Array.isArray(a)) a = Vb(a, b, c, void 0 !== d);
            else if (Eb(a)) {
                var e = {}, g;
                for(g in a)e[g] = Ub(a[g], b, c, d);
                a = e;
            } else a = b(a, d);
            return a;
        }
    }
    function Vb(a, b, c, d) {
        var e = Q(a);
        d = d ? !!(e & 16) : void 0;
        a = Array.prototype.slice.call(a);
        for(var g = 0; g < a.length; g++)a[g] = Ub(a[g], b, c, d);
        c(e, a);
        return a;
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    function Wb(a) {
        return a.ja === Db ? a.toJSON() : Tb(a);
    }
    function Xb(a, b) {
        a & 128 && Ib(b);
    }
    function Yb(a, b, c) {
        c = void 0 === c ? Cb : c;
        if (null != a) {
            if (Ia && a instanceof Uint8Array) return a.length ? new ib(new Uint8Array(a), Qa) : jb();
            if (Array.isArray(a)) {
                var d = Q(a);
                if (d & 2) return a;
                if (b && !(d & 32) && (d & 16 || 0 === d)) return R(a, d | 2), a;
                a = Vb(a, Yb, d & 4 ? Cb : c, !0);
                b = Q(a);
                b & 4 && b & 2 && Object.freeze(a);
                return a;
            }
            return a.ja === Db ? Zb(a) : a;
        }
    }
    function $b(a, b, c, d, e, g, f) {
        if (a = a.h && a.h[c]) {
            d = Q(a);
            d & 2 ? d = a : (g = Ca(a, Zb), Cb(d, g), Object.freeze(g), d = g);
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            f = null == d ? Fb : Ab([]);
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                g = !!d.length;
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                    var h = d[a];
                    g = g && !(Q(h.o) & 2);
                    f[a] = h.o;
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                g = (g ? 8 : 0) | 1;
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                (a & g) !== g && (Object.isFrozen(f) && (f = Array.prototype.slice.call(f)), R(f, a | g));
                b.h || (b.h = {});
                b.h[c] = d;
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            Kb(b, c, f, e);
        } else U(b, c, Yb(d, g, f), e);
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        if (Q(a.o) & 2) return a;
        a = Pb(a, !0);
        P(a.o, 2);
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        var c = a.o, d = [];
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        var e = a.constructor.h;
        e && d.push(e);
        e = a.B;
        if (e) {
            d.length = c.length;
            d.fill(void 0, d.length, c.length);
            var g = {};
            d[d.length - 1] = g;
        }
        0 !== (Q(c) & 128) && Ib(d);
        b = b || a.C() ? Cb : Bb;
        g = a.constructor;
        Sb = d;
        d = new g(d);
        Sb = void 0;
        a.R && (d.R = a.R.slice());
        g = !!(Q(c) & 16);
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        if (e) for(var k in e)$b(a, d, +k, e[k], !0, g, b);
        return d;
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    function X(a, b, c) {
        null == a && (a = Sb);
        Sb = void 0;
        var d = this.constructor.i || 0, e = 0 < d, g = this.constructor.h, f = !1;
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            a = g ? [
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            var h = 48;
            var k = !0;
            e && (d = 0, h |= 128);
            R(a, h);
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            if (!Array.isArray(a)) throw Error();
            if (g && g !== a[0]) throw Error();
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            if (e) {
                if (128 & l) d = 0;
                else if (0 < a.length) {
                    var m = a[a.length - 1];
                    if (Eb(m) && "g" in m) {
                        d = 0;
                        l |= 128;
                        delete m.g;
                        var r = !0, p;
                        for(p in m){
                            r = !1;
                            break;
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                        r && a.pop();
                    }
                }
            } else if (128 & l) throw Error();
            h !== l && R(a, l);
        }
        this.G = (g ? 0 : -1) - d;
        this.h = void 0;
        this.o = a;
        a: {
            g = this.o.length;
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            if (g && (g = this.o[d], Eb(g))) {
                this.B = g;
                this.i = d - this.G;
                break a;
            }
            void 0 !== b && -1 < b ? (this.i = Math.max(b, d + 1 - this.G), this.B = void 0) : this.i = Number.MAX_VALUE;
        }
        if (!e && this.B && "g" in this.B) throw Error('Unexpected "g" flag in sparse object of message that is not a group type.');
        if (c) {
            b = k && !f && !0;
            e = this.i;
            var n;
            for(k = 0; k < c.length; k++)f = c[k], f < e ? (f += this.G, (d = a[f]) ? ac(d, b) : a[f] = Fb) : (n || (n = Jb(this)), (d = n[f]) ? ac(d, b) : n[f] = Fb);
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    }
    X.prototype.toJSON = function() {
        return Vb(this.o, Wb, Xb);
    };
    X.prototype.C = function() {
        return !!(Q(this.o) & 2);
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    function ac(a, b) {
        if (Array.isArray(a)) {
            var c = Q(a), d = 1;
            !b || c & 2 || (d |= 16);
            (c & d) !== d && R(a, c | d);
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    X.prototype.toString = function() {
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    function bc(a, b, c) {
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            var d = {}, e;
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                var g = c[e], f = g.ra;
                f || (d.J = g.xa || g.oa.W, g.ia ? (d.aa = cc(g.ia), f = function(h) {
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                    };
                }(d)) : g.ka ? (d.Z = dc(g.da.P, g.ka), f = function(h) {
                    return function(k, l, m) {
                        return h.J(k, l, m, h.Z);
                    };
                }(d)) : f = d.J, g.ra = f);
                f(b, a, g.da);
                d = {
                    J: d.J,
                    aa: d.aa,
                    Z: d.Z
                };
            }
        }
        yb(b, a);
    }
    var ec = Symbol();
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        return a[ec] || (a[ec] = function(d, e) {
            return b(d, e, c);
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        var b = a[ec];
        if (!b) {
            var c = hc(a);
            b = function(d, e) {
                return ic(d, e, c);
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            a[ec] = b;
        }
        return b;
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    function jc(a) {
        var b = a.ia;
        if (b) return gc(b);
        if (b = a.wa) return fc(a.da.P, b, a.ka);
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        return b ? function(e, g) {
            return d(e, g, c, b);
        } : function(e, g) {
            return d(e, g, c);
        };
    }
    function lc(a, b) {
        var c = a[b];
        "function" == typeof c && 0 === c.length && (c = c(), a[b] = c);
        return Array.isArray(c) && (mc in c || nc in c || 0 < c.length && "function" == typeof c[0]) ? c : void 0;
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                    break;
                case 2:
                    k = f++;
                    k = lc(a, k);
                    e(b, c, h, k, a[f++]);
                    break;
                case 3:
                    g(b, c, h, a[f++], a[f++], a[f++]);
                    break;
                case 4:
                    g(b, c, h, a[f++], a[f++], a[f++], a[f++]);
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                default:
                    throw Error("unexpected number of binary field arguments: " + k);
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        return b;
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    var pc = Symbol();
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            var c = qc(a);
            b = function(d, e) {
                return rc(d, e, c);
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            a[pc] = b;
        }
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    function dc(a, b) {
        var c = a[pc];
        c || (c = function(d, e) {
            return bc(d, e, b);
        }, a[pc] = c);
        return c;
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    var nc = Symbol();
    function sc(a, b) {
        a.push(b);
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    function tc(a, b, c) {
        a.push(b, c.W);
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    function uc(a, b, c, d) {
        var e = cc(d), g = qc(d).P, f = c.W;
        a.push(b, function(h, k, l) {
            return f(h, k, l, g, e);
        });
    }
    function vc(a, b, c, d, e, g) {
        var f = dc(d, g), h = c.W;
        a.push(b, function(k, l, m) {
            return h(k, l, m, d, f);
        });
    }
    function qc(a) {
        var b = a[nc];
        if (b) return b;
        b = oc(a, a[nc] = [], sc, tc, uc, vc);
        mc in a && nc in a && (a.length = 0);
        return b;
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    var mc = Symbol();
    function wc(a, b) {
        a[0] = b;
    }
    function xc(a, b, c, d) {
        var e = c.U;
        a[b] = d ? function(g, f, h) {
            return e(g, f, h, d);
        } : e;
    }
    function yc(a, b, c, d, e) {
        var g = c.U, f = gc(d), h = hc(d).P;
        a[b] = function(k, l, m) {
            return g(k, l, m, h, f, e);
        };
    }
    function zc(a, b, c, d, e, g, f) {
        var h = c.U, k = fc(d, e, g);
        a[b] = function(l, m, r) {
            return h(l, m, r, d, k, f);
        };
    }
    function hc(a) {
        var b = a[mc];
        if (b) return b;
        b = oc(a, a[mc] = {}, wc, xc, yc, zc);
        mc in a && nc in a && (a.length = 0);
        return b;
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    function ic(a, b, c) {
        for(; ub(b) && 4 != b.i;){
            var d = b.l, e = c[d];
            if (!e) {
                var g = c[0];
                g && (g = g[d]) && (e = c[d] = kc(g));
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            if (!e || !e(b, a, d)) {
                e = b;
                d = a;
                g = e.j;
                vb(e);
                var f = e;
                if (!f.ca) {
                    e = f.h.h - g;
                    f.h.h = g;
                    f = f.h;
                    if (0 == e) e = jb();
                    else {
                        g = pb(f, e);
                        if (f.S && f.m) e = f.i.subarray(g, g + e);
                        else {
                            f = f.i;
                            var h = g;
                            e = g + e;
                            e = h === e ? Pa() : Ra ? f.slice(h, e) : new Uint8Array(f.subarray(h, e));
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                        e = 0 == e.length ? jb() : new ib(e, Qa);
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                    (g = d.R) ? g.push(e) : d.R = [
                        e
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        return a;
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    function rc(a, b, c) {
        for(var d = c.length, e = 1 == d % 2, g = e ? 1 : 0; g < d; g += 2)(0, c[g + 1])(b, a, c[g]);
        bc(a, b, e ? c[0] : void 0);
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    function Ac(a, b) {
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            U: a,
            W: b
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    var Y = Ac(function(a, b, c) {
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        a = a.h;
        var d = a.i, e = a.h, g = d[e];
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        var h = d[e + 2];
        d = d[e + 3];
        L(a, a.h + 4);
        f = (g << 0 | f << 8 | h << 16 | d << 24) >>> 0;
        a = 2 * (f >> 31) + 1;
        g = f >>> 23 & 255;
        f &= 8388607;
        U(b, c, 255 == g ? f ? NaN : Infinity * a : 0 == g ? a * Math.pow(2, -149) * f : a * Math.pow(2, g - 150) * (f + Math.pow(2, 23)));
        return !0;
    }, function(a, b, c) {
        b = Mb(b, c);
        if (null != b) {
            M(a.h, 8 * c + 5);
            a = a.h;
            var d = +b;
            0 === d ? 0 < 1 / d ? G = H = 0 : (H = 0, G = 2147483648) : isNaN(d) ? (H = 0, G = 2147483647) : (d = (c = 0 > d ? -2147483648 : 0) ? -d : d, 3.4028234663852886E38 < d ? (H = 0, G = (c | 2139095040) >>> 0) : 1.1754943508222875E-38 > d ? (d = Math.round(d / Math.pow(2, -149)), H = 0, G = (c | d) >>> 0) : (b = Math.floor(Math.log(d) / Math.LN2), d *= Math.pow(2, -b), d = Math.round(8388608 * d), 16777216 <= d && ++b, H = 0, G = (c | b + 127 << 23 | d & 8388607) >>> 0));
            c = G;
            a.h.push(c >>> 0 & 255);
            a.h.push(c >>> 8 & 255);
            a.h.push(c >>> 16 & 255);
            a.h.push(c >>> 24 & 255);
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    }), Bc = Ac(function(a, b, c) {
        if (0 !== a.i) return !1;
        var d = a.h, e = 0, g = a = 0, f = d.i, h = d.h;
        do {
            var k = f[h++];
            e |= (k & 127) << g;
            g += 7;
        }while (32 > g && k & 128);
        32 < g && (a |= (k & 127) >> 4);
        for(g = 3; 32 > g && k & 128; g += 7)k = f[h++], a |= (k & 127) << g;
        L(d, h);
        if (128 > k) {
            d = e >>> 0;
            k = a >>> 0;
            if (a = k & 2147483648) d = ~d + 1 >>> 0, k = ~k >>> 0, 0 == d && (k = k + 1 >>> 0);
            d = 4294967296 * k + (d >>> 0);
        } else throw Za();
        U(b, c, a ? -d : d);
        return !0;
    }, function(a, b, c) {
        b = S(b, c);
        null != b && ("string" === typeof b && Wa(b), null != b && (M(a.h, 8 * c), "number" === typeof b ? (a = a.h, Sa(b), sb(a, G, H)) : (c = Wa(b), sb(a.h, c.i, c.h))));
    }), Cc = Ac(function(a, b, c) {
        if (0 !== a.i) return !1;
        U(b, c, ob(a.h));
        return !0;
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        b = S(b, c);
        if (null != b && null != b) {
            if (M(a.h, 8 * c), a = a.h, c = b, 0 <= c) M(a, c);
            else {
                for(b = 0; 9 > b; b++)a.h.push(c & 127 | 128), c >>= 7;
                a.h.push(1);
            }
        }
    }), Dc = Ac(function(a, b, c) {
        if (2 !== a.i) return !1;
        var d = ob(a.h) >>> 0;
        a = a.h;
        var e = pb(a, d);
        a = a.i;
        if (db) {
            var g = a, f;
            (f = cb) || (f = cb = new TextDecoder("utf-8", {
                fatal: !0
            }));
            a = e + d;
            g = 0 === e && a === g.length ? g : g.subarray(e, a);
            try {
                var h = f.decode(g);
            } catch (r) {
                if (void 0 === bb) {
                    try {
                        f.decode(new Uint8Array([
                            128
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                    } catch (p) {}
                    try {
                        f.decode(new Uint8Array([
                            97
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                    } catch (p) {
                        bb = !1;
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                !bb && (cb = void 0);
                throw r;
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        } else {
            h = e;
            d = h + d;
            e = [];
            for(var k = null, l, m; h < d;)l = a[h++], 128 > l ? e.push(l) : 224 > l ? h >= d ? K() : (m = a[h++], 194 > l || 128 !== (m & 192) ? (h--, K()) : e.push((l & 31) << 6 | m & 63)) : 240 > l ? h >= d - 1 ? K() : (m = a[h++], 128 !== (m & 192) || 224 === l && 160 > m || 237 === l && 160 <= m || 128 !== ((g = a[h++]) & 192) ? (h--, K()) : e.push((l & 15) << 12 | (m & 63) << 6 | g & 63)) : 244 >= l ? h >= d - 2 ? K() : (m = a[h++], 128 !== (m & 192) || 0 !== (l << 28) + (m - 144) >> 30 || 128 !== ((g = a[h++]) & 192) || 128 !== ((f = a[h++]) & 192) ? (h--, K()) : (l = (l & 7) << 18 | (m & 63) << 12 | (g & 63) << 6 | f & 63, l -= 65536, e.push((l >> 10 & 1023) + 55296, (l & 1023) + 56320))) : K(), 8192 <= e.length && (k = ab(k, e), e.length = 0);
            h = ab(k, e);
        }
        U(b, c, h);
        return !0;
    }, function(a, b, c) {
        b = S(b, c);
        if (null != b) {
            var d = !1;
            d = void 0 === d ? !1 : d;
            if (fb) {
                if (d && /(?:[^\uD800-\uDBFF]|^)[\uDC00-\uDFFF]|[\uD800-\uDBFF](?![\uDC00-\uDFFF])/.test(b)) throw Error("Found an unpaired surrogate");
                b = (eb || (eb = new TextEncoder)).encode(b);
            } else {
                for(var e = 0, g = new Uint8Array(3 * b.length), f = 0; f < b.length; f++){
                    var h = b.charCodeAt(f);
                    if (128 > h) g[e++] = h;
                    else {
                        if (2048 > h) g[e++] = h >> 6 | 192;
                        else {
                            if (55296 <= h && 57343 >= h) {
                                if (56319 >= h && f < b.length) {
                                    var k = b.charCodeAt(++f);
                                    if (56320 <= k && 57343 >= k) {
                                        h = 1024 * (h - 55296) + k - 56320 + 65536;
                                        g[e++] = h >> 18 | 240;
                                        g[e++] = h >> 12 & 63 | 128;
                                        g[e++] = h >> 6 & 63 | 128;
                                        g[e++] = h & 63 | 128;
                                        continue;
                                    } else f--;
                                }
                                if (d) throw Error("Found an unpaired surrogate");
                                h = 65533;
                            }
                            g[e++] = h >> 12 | 224;
                            g[e++] = h >> 6 & 63 | 128;
                        }
                        g[e++] = h & 63 | 128;
                    }
                }
                b = e === g.length ? g : g.subarray(0, e);
            }
            M(a.h, 8 * c + 2);
            M(a.h, b.length);
            N(a, a.h.end());
            N(a, b);
        }
    }), Ec = Ac(function(a, b, c, d, e) {
        if (2 !== a.i) return !1;
        b = Qb(b, c, d);
        c = a.h.j;
        d = ob(a.h) >>> 0;
        var g = a.h.h + d, f = g - c;
        0 >= f && (a.h.j = g, e(b, a, void 0, void 0, void 0), f = g - a.h.h);
        if (f) throw Error("Message parsing ended unexpectedly. Expected to read " + (d + " bytes, instead read " + (d - f) + " bytes, either the data ended unexpectedly or the message misreported its own length"));
        a.h.h = g;
        a.h.j = c;
        return !0;
    }, function(a, b, c, d, e) {
        b = Ob(b, d, c);
        if (null != b) for(d = 0; d < b.length; d++){
            var g = a;
            M(g.h, 8 * c + 2);
            var f = g.h.end();
            N(g, f);
            f.push(g.i);
            g = f;
            e(b[d], a);
            f = a;
            var h = g.pop();
            for(h = f.i + f.h.length() - h; 127 < h;)g.push(h & 127 | 128), h >>>= 7, f.i++;
            g.push(h);
            f.i++;
        }
    });
    function Fc(a) {
        return function(b, c) {
            a: {
                if (wb.length) {
                    var d = wb.pop();
                    d.setOptions(c);
                    nb(d.h, b, c);
                    b = d;
                } else b = new tb(b, c);
                try {
                    var e = hc(a);
                    var g = ic(new e.P, b, e);
                    break a;
                } finally{
                    e = b.h, e.i = null, e.m = !1, e.l = 0, e.j = 0, e.h = 0, e.S = !1, b.l = -1, b.i = -1, 100 > wb.length && wb.push(b);
                }
                g = void 0;
            }
            return g;
        };
    }
    function Gc(a) {
        return function() {
            var b = new xb;
            rc(this, b, qc(a));
            N(b, b.h.end());
            for(var c = new Uint8Array(b.i), d = b.j, e = d.length, g = 0, f = 0; f < e; f++){
                var h = d[f];
                c.set(h, g);
                g += h.length;
            }
            b.j = [
                c
            ];
            return c;
        };
    }
    function Z(a) {
        X.call(this, a);
    }
    na(Z, X);
    var Hc = [
        Z,
        1,
        Cc,
        2,
        Y,
        3,
        Dc,
        4,
        Dc
    ];
    Z.prototype.l = Gc(Hc);
    function Ic(a) {
        X.call(this, a, -1, Jc);
    }
    na(Ic, X);
    Ic.prototype.addClassification = function(a, b) {
        Qb(this, 1, Z, a, b);
        return this;
    };
    var Jc = [
        1
    ], Kc = Fc([
        Ic,
        1,
        Ec,
        Hc
    ]);
    function Lc(a) {
        X.call(this, a);
    }
    na(Lc, X);
    var Mc = [
        Lc,
        1,
        Y,
        2,
        Y,
        3,
        Y,
        4,
        Y,
        5,
        Y
    ];
    Lc.prototype.l = Gc(Mc);
    function Nc(a) {
        X.call(this, a, -1, Oc);
    }
    na(Nc, X);
    var Oc = [
        1
    ], Pc = Fc([
        Nc,
        1,
        Ec,
        Mc
    ]);
    function Qc(a) {
        X.call(this, a);
    }
    na(Qc, X);
    var Rc = [
        Qc,
        1,
        Y,
        2,
        Y,
        3,
        Y,
        4,
        Y,
        5,
        Y,
        6,
        Bc
    ], Sc = Fc(Rc);
    Qc.prototype.l = Gc(Rc);
    function Tc(a, b, c) {
        c = a.createShader(0 === c ? a.VERTEX_SHADER : a.FRAGMENT_SHADER);
        a.shaderSource(c, b);
        a.compileShader(c);
        if (!a.getShaderParameter(c, a.COMPILE_STATUS)) throw Error("Could not compile WebGL shader.\n\n" + a.getShaderInfoLog(c));
        return c;
    }
    function Uc(a) {
        return Ob(a, Z, 1).map(function(b) {
            var c = S(b, 1);
            return {
                index: null == c ? 0 : c,
                qa: W(b, 2),
                label: null != S(b, 3) ? Rb(S(b, 3), "") : void 0,
                displayName: null != S(b, 4) ? Rb(S(b, 4), "") : void 0
            };
        });
    }
    function Vc(a) {
        return {
            x: W(a, 1),
            y: W(a, 2),
            z: W(a, 3),
            visibility: null != Mb(a, 4) ? W(a, 4) : void 0
        };
    }
    function Wc(a, b) {
        this.i = a;
        this.h = b;
        this.m = 0;
    }
    function Xc(a, b, c) {
        Yc(a, b);
        if ("function" === typeof a.h.canvas.transferToImageBitmap) return Promise.resolve(a.h.canvas.transferToImageBitmap());
        if (c) return Promise.resolve(a.h.canvas);
        if ("function" === typeof createImageBitmap) return createImageBitmap(a.h.canvas);
        void 0 === a.j && (a.j = document.createElement("canvas"));
        return new Promise(function(d) {
            a.j.height = a.h.canvas.height;
            a.j.width = a.h.canvas.width;
            a.j.getContext("2d", {}).drawImage(a.h.canvas, 0, 0, a.h.canvas.width, a.h.canvas.height);
            d(a.j);
        });
    }
    function Yc(a, b) {
        var c = a.h;
        if (void 0 === a.s) {
            var d = Tc(c, "\n  attribute vec2 aVertex;\n  attribute vec2 aTex;\n  varying vec2 vTex;\n  void main(void) {\n    gl_Position = vec4(aVertex, 0.0, 1.0);\n    vTex = aTex;\n  }", 0), e = Tc(c, "\n  precision mediump float;\n  varying vec2 vTex;\n  uniform sampler2D sampler0;\n  void main(){\n    gl_FragColor = texture2D(sampler0, vTex);\n  }", 1), g = c.createProgram();
            c.attachShader(g, d);
            c.attachShader(g, e);
            c.linkProgram(g);
            if (!c.getProgramParameter(g, c.LINK_STATUS)) throw Error("Could not compile WebGL program.\n\n" + c.getProgramInfoLog(g));
            d = a.s = g;
            c.useProgram(d);
            e = c.getUniformLocation(d, "sampler0");
            a.l = {
                O: c.getAttribLocation(d, "aVertex"),
                N: c.getAttribLocation(d, "aTex"),
                ya: e
            };
            a.v = c.createBuffer();
            c.bindBuffer(c.ARRAY_BUFFER, a.v);
            c.enableVertexAttribArray(a.l.O);
            c.vertexAttribPointer(a.l.O, 2, c.FLOAT, !1, 0, 0);
            c.bufferData(c.ARRAY_BUFFER, new Float32Array([
                -1,
                -1,
                -1,
                1,
                1,
                1,
                1,
                -1
            ]), c.STATIC_DRAW);
            c.bindBuffer(c.ARRAY_BUFFER, null);
            a.u = c.createBuffer();
            c.bindBuffer(c.ARRAY_BUFFER, a.u);
            c.enableVertexAttribArray(a.l.N);
            c.vertexAttribPointer(a.l.N, 2, c.FLOAT, !1, 0, 0);
            c.bufferData(c.ARRAY_BUFFER, new Float32Array([
                0,
                1,
                0,
                0,
                1,
                0,
                1,
                1
            ]), c.STATIC_DRAW);
            c.bindBuffer(c.ARRAY_BUFFER, null);
            c.uniform1i(e, 0);
        }
        d = a.l;
        c.useProgram(a.s);
        c.canvas.width = b.width;
        c.canvas.height = b.height;
        c.viewport(0, 0, b.width, b.height);
        c.activeTexture(c.TEXTURE0);
        a.i.bindTexture2d(b.glName);
        c.enableVertexAttribArray(d.O);
        c.bindBuffer(c.ARRAY_BUFFER, a.v);
        c.vertexAttribPointer(d.O, 2, c.FLOAT, !1, 0, 0);
        c.enableVertexAttribArray(d.N);
        c.bindBuffer(c.ARRAY_BUFFER, a.u);
        c.vertexAttribPointer(d.N, 2, c.FLOAT, !1, 0, 0);
        c.bindFramebuffer(c.DRAW_FRAMEBUFFER ? c.DRAW_FRAMEBUFFER : c.FRAMEBUFFER, null);
        c.clearColor(0, 0, 0, 0);
        c.clear(c.COLOR_BUFFER_BIT);
        c.colorMask(!0, !0, !0, !0);
        c.drawArrays(c.TRIANGLE_FAN, 0, 4);
        c.disableVertexAttribArray(d.O);
        c.disableVertexAttribArray(d.N);
        c.bindBuffer(c.ARRAY_BUFFER, null);
        a.i.bindTexture2d(0);
    }
    function Zc(a) {
        this.h = a;
    }
    var $c = new Uint8Array([
        0,
        97,
        115,
        109,
        1,
        0,
        0,
        0,
        1,
        4,
        1,
        96,
        0,
        0,
        3,
        2,
        1,
        0,
        10,
        9,
        1,
        7,
        0,
        65,
        0,
        253,
        15,
        26,
        11
    ]);
    function ad(a, b) {
        return b + a;
    }
    function bd(a, b) {
        window[a] = b;
    }
    function cd(a) {
        var b = document.createElement("script");
        b.setAttribute("src", a);
        b.setAttribute("crossorigin", "anonymous");
        return new Promise(function(c) {
            b.addEventListener("load", function() {
                c();
            }, !1);
            b.addEventListener("error", function() {
                c();
            }, !1);
            document.body.appendChild(b);
        });
    }
    function dd() {
        return E(function(a) {
            switch(a.h){
                case 1:
                    return a.s = 2, D(a, WebAssembly.instantiate($c), 4);
                case 4:
                    a.h = 3;
                    a.s = 0;
                    break;
                case 2:
                    return a.s = 0, a.l = null, a.return(!1);
                case 3:
                    return a.return(!0);
            }
        });
    }
    function ed(a) {
        this.h = a;
        this.listeners = {};
        this.l = {};
        this.L = {};
        this.s = {};
        this.v = {};
        this.M = this.u = this.ga = !0;
        this.I = Promise.resolve();
        this.fa = "";
        this.D = {};
        this.locateFile = a && a.locateFile || ad;
        if ("object" === typeof window) var b = window.location.pathname.toString().substring(0, window.location.pathname.toString().lastIndexOf("/")) + "/";
        else if ("undefined" !== typeof location) b = location.pathname.toString().substring(0, location.pathname.toString().lastIndexOf("/")) + "/";
        else throw Error("solutions can only be loaded on a web page or in a web worker");
        this.ha = b;
        if (a.options) {
            b = A(Object.keys(a.options));
            for(var c = b.next(); !c.done; c = b.next()){
                c = c.value;
                var d = a.options[c].default;
                void 0 !== d && (this.l[c] = "function" === typeof d ? d() : d);
            }
        }
    }
    x = ed.prototype;
    x.close = function() {
        this.j && this.j.delete();
        return Promise.resolve();
    };
    function fd(a) {
        var b, c, d, e, g, f, h, k, l, m, r;
        return E(function(p) {
            switch(p.h){
                case 1:
                    if (!a.ga) return p.return();
                    b = void 0 === a.h.files ? [] : "function" === typeof a.h.files ? a.h.files(a.l) : a.h.files;
                    return D(p, dd(), 2);
                case 2:
                    c = p.i;
                    if ("object" === typeof window) return bd("createMediapipeSolutionsWasm", {
                        locateFile: a.locateFile
                    }), bd("createMediapipeSolutionsPackedAssets", {
                        locateFile: a.locateFile
                    }), f = b.filter(function(n) {
                        return void 0 !== n.data;
                    }), h = b.filter(function(n) {
                        return void 0 === n.data;
                    }), k = Promise.all(f.map(function(n) {
                        var q = gd(a, n.url);
                        if (void 0 !== n.path) {
                            var t = n.path;
                            q = q.then(function(w) {
                                a.overrideFile(t, w);
                                return Promise.resolve(w);
                            });
                        }
                        return q;
                    })), l = Promise.all(h.map(function(n) {
                        return void 0 === n.simd || n.simd && c || !n.simd && !c ? cd(a.locateFile(n.url, a.ha)) : Promise.resolve();
                    })).then(function() {
                        var n, q, t;
                        return E(function(w) {
                            if (1 == w.h) return n = window.createMediapipeSolutionsWasm, q = window.createMediapipeSolutionsPackedAssets, t = a, D(w, n(q), 2);
                            t.i = w.i;
                            w.h = 0;
                        });
                    }), m = function() {
                        return E(function(n) {
                            a.h.graph && a.h.graph.url ? n = D(n, gd(a, a.h.graph.url), 0) : (n.h = 0, n = void 0);
                            return n;
                        });
                    }(), D(p, Promise.all([
                        l,
                        k,
                        m
                    ]), 7);
                    if ("function" !== typeof importScripts) throw Error("solutions can only be loaded on a web page or in a web worker");
                    d = b.filter(function(n) {
                        return void 0 === n.simd || n.simd && c || !n.simd && !c;
                    }).map(function(n) {
                        return a.locateFile(n.url, a.ha);
                    });
                    importScripts.apply(null, ea(d));
                    e = a;
                    return D(p, createMediapipeSolutionsWasm(Module), 6);
                case 6:
                    e.i = p.i;
                    a.m = new OffscreenCanvas(1, 1);
                    a.i.canvas = a.m;
                    g = a.i.GL.createContext(a.m, {
                        antialias: !1,
                        alpha: !1,
                        va: "undefined" !== typeof WebGL2RenderingContext ? 2 : 1
                    });
                    a.i.GL.makeContextCurrent(g);
                    p.h = 4;
                    break;
                case 7:
                    a.m = document.createElement("canvas");
                    r = a.m.getContext("webgl2", {});
                    if (!r && (r = a.m.getContext("webgl", {}), !r)) return alert("Failed to create WebGL canvas context when passing video frame."), p.return();
                    a.K = r;
                    a.i.canvas = a.m;
                    a.i.createContext(a.m, !0, !0, {});
                case 4:
                    a.j = new a.i.SolutionWasm, a.ga = !1, p.h = 0;
            }
        });
    }
    function hd(a) {
        var b, c, d, e, g, f, h, k;
        return E(function(l) {
            if (1 == l.h) {
                if (a.h.graph && a.h.graph.url && a.fa === a.h.graph.url) return l.return();
                a.u = !0;
                if (!a.h.graph || !a.h.graph.url) {
                    l.h = 2;
                    return;
                }
                a.fa = a.h.graph.url;
                return D(l, gd(a, a.h.graph.url), 3);
            }
            2 != l.h && (b = l.i, a.j.loadGraph(b));
            c = A(Object.keys(a.D));
            for(d = c.next(); !d.done; d = c.next())e = d.value, a.j.overrideFile(e, a.D[e]);
            a.D = {};
            if (a.h.listeners) for(g = A(a.h.listeners), f = g.next(); !f.done; f = g.next())h = f.value, id(a, h);
            k = a.l;
            a.l = {};
            a.setOptions(k);
            l.h = 0;
        });
    }
    x.reset = function() {
        var a = this;
        return E(function(b) {
            a.j && (a.j.reset(), a.s = {}, a.v = {});
            b.h = 0;
        });
    };
    x.setOptions = function(a, b) {
        var c = this;
        if (b = b || this.h.options) {
            for(var d = [], e = [], g = {}, f = A(Object.keys(a)), h = f.next(); !h.done; g = {
                X: g.X,
                Y: g.Y
            }, h = f.next())if (h = h.value, !(h in this.l && this.l[h] === a[h])) {
                this.l[h] = a[h];
                var k = b[h];
                void 0 !== k && (k.onChange && (g.X = k.onChange, g.Y = a[h], d.push(function(l) {
                    return function() {
                        var m;
                        return E(function(r) {
                            if (1 == r.h) return D(r, l.X(l.Y), 2);
                            m = r.i;
                            !0 === m && (c.u = !0);
                            r.h = 0;
                        });
                    };
                }(g))), k.graphOptionXref && (h = Object.assign({}, {
                    calculatorName: "",
                    calculatorIndex: 0
                }, k.graphOptionXref, {
                    valueNumber: 1 === k.type ? a[h] : 0,
                    valueBoolean: 0 === k.type ? a[h] : !1,
                    valueString: 2 === k.type ? a[h] : ""
                }), e.push(h)));
            }
            if (0 !== d.length || 0 !== e.length) this.u = !0, this.H = (void 0 === this.H ? [] : this.H).concat(e), this.F = (void 0 === this.F ? [] : this.F).concat(d);
        }
    };
    function jd(a) {
        var b, c, d, e, g, f, h;
        return E(function(k) {
            switch(k.h){
                case 1:
                    if (!a.u) return k.return();
                    if (!a.F) {
                        k.h = 2;
                        break;
                    }
                    b = A(a.F);
                    c = b.next();
                case 3:
                    if (c.done) {
                        k.h = 5;
                        break;
                    }
                    d = c.value;
                    return D(k, d(), 4);
                case 4:
                    c = b.next();
                    k.h = 3;
                    break;
                case 5:
                    a.F = void 0;
                case 2:
                    if (a.H) {
                        e = new a.i.GraphOptionChangeRequestList;
                        g = A(a.H);
                        for(f = g.next(); !f.done; f = g.next())h = f.value, e.push_back(h);
                        a.j.changeOptions(e);
                        e.delete();
                        a.H = void 0;
                    }
                    a.u = !1;
                    k.h = 0;
            }
        });
    }
    x.initialize = function() {
        var a = this;
        return E(function(b) {
            return 1 == b.h ? D(b, fd(a), 2) : 3 != b.h ? D(b, hd(a), 3) : D(b, jd(a), 0);
        });
    };
    function gd(a, b) {
        var c, d;
        return E(function(e) {
            if (b in a.L) return e.return(a.L[b]);
            c = a.locateFile(b, "");
            d = fetch(c).then(function(g) {
                return g.arrayBuffer();
            });
            a.L[b] = d;
            return e.return(d);
        });
    }
    x.overrideFile = function(a, b) {
        this.j ? this.j.overrideFile(a, b) : this.D[a] = b;
    };
    x.clearOverriddenFiles = function() {
        this.D = {};
        this.j && this.j.clearOverriddenFiles();
    };
    x.send = function(a, b) {
        var c = this, d, e, g, f, h, k, l, m, r;
        return E(function(p) {
            switch(p.h){
                case 1:
                    if (!c.h.inputs) return p.return();
                    d = 1E3 * (void 0 === b || null === b ? performance.now() : b);
                    return D(p, c.I, 2);
                case 2:
                    return D(p, c.initialize(), 3);
                case 3:
                    e = new c.i.PacketDataList;
                    g = A(Object.keys(a));
                    for(f = g.next(); !f.done; f = g.next())if (h = f.value, k = c.h.inputs[h]) {
                        a: {
                            var n = a[h];
                            switch(k.type){
                                case "video":
                                    var q = c.s[k.stream];
                                    q || (q = new Wc(c.i, c.K), c.s[k.stream] = q);
                                    0 === q.m && (q.m = q.i.createTexture());
                                    if ("undefined" !== typeof HTMLVideoElement && n instanceof HTMLVideoElement) {
                                        var t = n.videoWidth;
                                        var w = n.videoHeight;
                                    } else "undefined" !== typeof HTMLImageElement && n instanceof HTMLImageElement ? (t = n.naturalWidth, w = n.naturalHeight) : (t = n.width, w = n.height);
                                    w = {
                                        glName: q.m,
                                        width: t,
                                        height: w
                                    };
                                    t = q.h;
                                    t.canvas.width = w.width;
                                    t.canvas.height = w.height;
                                    t.activeTexture(t.TEXTURE0);
                                    q.i.bindTexture2d(q.m);
                                    t.texImage2D(t.TEXTURE_2D, 0, t.RGBA, t.RGBA, t.UNSIGNED_BYTE, n);
                                    q.i.bindTexture2d(0);
                                    q = w;
                                    break a;
                                case "detections":
                                    q = c.s[k.stream];
                                    q || (q = new Zc(c.i), c.s[k.stream] = q);
                                    q.data || (q.data = new q.h.DetectionListData);
                                    q.data.reset(n.length);
                                    for(w = 0; w < n.length; ++w){
                                        t = n[w];
                                        var v = q.data, B = v.setBoundingBox, J = w;
                                        var I = t.la;
                                        var u = new Qc;
                                        V(u, 1, I.sa);
                                        V(u, 2, I.ta);
                                        V(u, 3, I.height);
                                        V(u, 4, I.width);
                                        V(u, 5, I.rotation);
                                        U(u, 6, I.pa);
                                        I = u.l();
                                        B.call(v, J, I);
                                        if (t.ea) for(v = 0; v < t.ea.length; ++v){
                                            u = t.ea[v];
                                            B = q.data;
                                            J = B.addNormalizedLandmark;
                                            I = w;
                                            u = Object.assign({}, u, {
                                                visibility: u.visibility ? u.visibility : 0
                                            });
                                            var C = new Lc;
                                            V(C, 1, u.x);
                                            V(C, 2, u.y);
                                            V(C, 3, u.z);
                                            u.visibility && V(C, 4, u.visibility);
                                            u = C.l();
                                            J.call(B, I, u);
                                        }
                                        if (t.ba) for(v = 0; v < t.ba.length; ++v)B = q.data, J = B.addClassification, I = w, u = t.ba[v], C = new Z, V(C, 2, u.qa), u.index && U(C, 1, u.index), u.label && U(C, 3, u.label), u.displayName && U(C, 4, u.displayName), u = C.l(), J.call(B, I, u);
                                    }
                                    q = q.data;
                                    break a;
                                default:
                                    q = {};
                            }
                        }
                        l = q;
                        m = k.stream;
                        switch(k.type){
                            case "video":
                                e.pushTexture2d(Object.assign({}, l, {
                                    stream: m,
                                    timestamp: d
                                }));
                                break;
                            case "detections":
                                r = l;
                                r.stream = m;
                                r.timestamp = d;
                                e.pushDetectionList(r);
                                break;
                            default:
                                throw Error("Unknown input config type: '" + k.type + "'");
                        }
                    }
                    c.j.send(e);
                    return D(p, c.I, 4);
                case 4:
                    e.delete(), p.h = 0;
            }
        });
    };
    function kd(a, b, c) {
        var d, e, g, f, h, k, l, m, r, p, n, q, t, w;
        return E(function(v) {
            switch(v.h){
                case 1:
                    if (!c) return v.return(b);
                    d = {};
                    e = 0;
                    g = A(Object.keys(c));
                    for(f = g.next(); !f.done; f = g.next())h = f.value, k = c[h], "string" !== typeof k && "texture" === k.type && void 0 !== b[k.stream] && ++e;
                    1 < e && (a.M = !1);
                    l = A(Object.keys(c));
                    f = l.next();
                case 2:
                    if (f.done) {
                        v.h = 4;
                        break;
                    }
                    m = f.value;
                    r = c[m];
                    if ("string" === typeof r) return t = d, w = m, D(v, ld(a, m, b[r]), 14);
                    p = b[r.stream];
                    if ("detection_list" === r.type) {
                        if (p) {
                            var B = p.getRectList();
                            for(var J = p.getLandmarksList(), I = p.getClassificationsList(), u = [], C = 0; C < B.size(); ++C){
                                var T = Sc(B.get(C)), od = W(T, 1), pd = W(T, 2), qd = W(T, 3), rd = W(T, 4), sd = W(T, 5, 0), za = void 0;
                                za = void 0 === za ? 0 : za;
                                T = {
                                    la: {
                                        sa: od,
                                        ta: pd,
                                        height: qd,
                                        width: rd,
                                        rotation: sd,
                                        pa: Rb(S(T, 6), za)
                                    },
                                    ea: Ob(Pc(J.get(C)), Lc, 1).map(Vc),
                                    ba: Uc(Kc(I.get(C)))
                                };
                                u.push(T);
                            }
                            B = u;
                        } else B = [];
                        d[m] = B;
                        v.h = 7;
                        break;
                    }
                    if ("proto_list" === r.type) {
                        if (p) {
                            B = Array(p.size());
                            for(J = 0; J < p.size(); J++)B[J] = p.get(J);
                            p.delete();
                        } else B = [];
                        d[m] = B;
                        v.h = 7;
                        break;
                    }
                    if (void 0 === p) {
                        v.h = 3;
                        break;
                    }
                    if ("float_list" === r.type) {
                        d[m] = p;
                        v.h = 7;
                        break;
                    }
                    if ("proto" === r.type) {
                        d[m] = p;
                        v.h = 7;
                        break;
                    }
                    if ("texture" !== r.type) throw Error("Unknown output config type: '" + r.type + "'");
                    n = a.v[m];
                    n || (n = new Wc(a.i, a.K), a.v[m] = n);
                    return D(v, Xc(n, p, a.M), 13);
                case 13:
                    q = v.i, d[m] = q;
                case 7:
                    r.transform && d[m] && (d[m] = r.transform(d[m]));
                    v.h = 3;
                    break;
                case 14:
                    t[w] = v.i;
                case 3:
                    f = l.next();
                    v.h = 2;
                    break;
                case 4:
                    return v.return(d);
            }
        });
    }
    function ld(a, b, c) {
        var d;
        return E(function(e) {
            return "number" === typeof c || c instanceof Uint8Array || c instanceof a.i.Uint8BlobList ? e.return(c) : c instanceof a.i.Texture2dDataOut ? (d = a.v[b], d || (d = new Wc(a.i, a.K), a.v[b] = d), e.return(Xc(d, c, a.M))) : e.return(void 0);
        });
    }
    function id(a, b) {
        for(var c = b.name || "$", d = [].concat(ea(b.wants)), e = new a.i.StringList, g = A(b.wants), f = g.next(); !f.done; f = g.next())e.push_back(f.value);
        g = a.i.PacketListener.implement({
            onResults: function(h) {
                for(var k = {}, l = 0; l < b.wants.length; ++l)k[d[l]] = h.get(l);
                var m = a.listeners[c];
                m && (a.I = kd(a, k, b.outs).then(function(r) {
                    r = m(r);
                    for(var p = 0; p < b.wants.length; ++p){
                        var n = k[d[p]];
                        "object" === typeof n && n.hasOwnProperty && n.hasOwnProperty("delete") && n.delete();
                    }
                    r && (a.I = r);
                }));
            }
        });
        a.j.attachMultiListener(e, g);
        e.delete();
    }
    x.onResults = function(a, b) {
        this.listeners[b || "$"] = a;
    };
    Aa("Solution", ed);
    Aa("OptionType", {
        BOOL: 0,
        NUMBER: 1,
        ua: 2,
        0: "BOOL",
        1: "NUMBER",
        2: "STRING"
    });
    function md(a) {
        void 0 === a && (a = 0);
        switch(a){
            case 1:
                return "selfie_segmentation_landscape.tflite";
            default:
                return "selfie_segmentation.tflite";
        }
    }
    function nd(a) {
        var b = this;
        a = a || {};
        this.h = new ed({
            locateFile: a.locateFile,
            files: function(c) {
                return [
                    {
                        simd: !0,
                        url: "selfie_segmentation_solution_simd_wasm_bin.js"
                    },
                    {
                        simd: !1,
                        url: "selfie_segmentation_solution_wasm_bin.js"
                    },
                    {
                        data: !0,
                        url: md(c.modelSelection)
                    }
                ];
            },
            graph: {
                url: "selfie_segmentation.binarypb"
            },
            listeners: [
                {
                    wants: [
                        "segmentation_mask",
                        "image_transformed"
                    ],
                    outs: {
                        image: {
                            type: "texture",
                            stream: "image_transformed"
                        },
                        segmentationMask: {
                            type: "texture",
                            stream: "segmentation_mask"
                        }
                    }
                }
            ],
            inputs: {
                image: {
                    type: "video",
                    stream: "input_frames_gpu"
                }
            },
            options: {
                useCpuInference: {
                    type: 0,
                    graphOptionXref: {
                        calculatorType: "InferenceCalculator",
                        fieldName: "use_cpu_inference"
                    },
                    default: "object" !== typeof window || void 0 === window.navigator ? !1 : "iPad Simulator;iPhone Simulator;iPod Simulator;iPad;iPhone;iPod".split(";").includes(navigator.platform) || navigator.userAgent.includes("Mac") && "ontouchend" in document
                },
                selfieMode: {
                    type: 0,
                    graphOptionXref: {
                        calculatorType: "GlScalerCalculator",
                        calculatorIndex: 1,
                        fieldName: "flip_horizontal"
                    }
                },
                modelSelection: {
                    type: 1,
                    graphOptionXref: {
                        calculatorType: "ConstantSidePacketCalculator",
                        calculatorName: "ConstantSidePacketCalculatorModelSelection",
                        fieldName: "int_value"
                    },
                    onChange: function(c) {
                        var d, e, g;
                        return E(function(f) {
                            if (1 == f.h) return d = md(c), e = "third_party/mediapipe/modules/selfie_segmentation/" + d, D(f, gd(b.h, d), 2);
                            g = f.i;
                            b.h.overrideFile(e, g);
                            return f.return(!0);
                        });
                    }
                }
            }
        });
    }
    x = nd.prototype;
    x.close = function() {
        this.h.close();
        return Promise.resolve();
    };
    x.onResults = function(a) {
        this.h.onResults(a);
    };
    x.initialize = function() {
        var a = this;
        return E(function(b) {
            return D(b, a.h.initialize(), 0);
        });
    };
    x.reset = function() {
        this.h.reset();
    };
    x.send = function(a) {
        var b = this;
        return E(function(c) {
            return D(c, b.h.send(a), 0);
        });
    };
    x.setOptions = function(a) {
        this.h.setOptions(a);
    };
    Aa("SelfieSegmentation", nd);
    Aa("VERSION", "0.1.1675465747");
}).call(this);

},{}],"bzQr6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // All exports from this package should be in base.
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
var _base = require("./base");
parcelHelpers.exportAll(_base, exports);
var _registerAllKernels = require("./register_all_kernels");

},{"./base":"eS7ei","./register_all_kernels":"89NZo","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eS7ei":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ /*
 * base.ts contains all the exports from tfjs-backend-cpu
 * without auto-kernel registration
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MathBackendCPU", ()=>(0, _backendCpu.MathBackendCPU));
parcelHelpers.export(exports, "version_cpu", ()=>(0, _version.version));
parcelHelpers.export(exports, "shared", ()=>_shared);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _backendCpu = require("./backend_cpu");
var _shared = require("./shared");
var _version = require("./version");
// Side effects for default initialization of MathBackendCPU
(0, _tfjsCore.registerBackend)("cpu", ()=>new (0, _backendCpu.MathBackendCPU)(), 1 /* priority */ );

},{"@tensorflow/tfjs-core":"21Ftl","./backend_cpu":"argpD","./shared":"YbUp7","./version":"fow87","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"argpD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "MathBackendCPU", ()=>MathBackendCPU);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("./cpu_util");
const whereImpl = (0, _tfjsCore.kernel_impls).whereImpl;
class MathBackendCPU extends (0, _tfjsCore.KernelBackend) {
    nextDataId() {
        return MathBackendCPU.nextDataId++;
    }
    constructor(){
        super();
        this.blockSize = 48;
        this.firstUse = true;
        this.data = new (0, _tfjsCore.DataStorage)(this, (0, _tfjsCore.engine)());
    }
    write(values, shape, dtype) {
        if (this.firstUse) {
            this.firstUse = false;
            if ((0, _tfjsCore.env)().get("IS_NODE")) (0, _tfjsCore.backend_util).warn("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================");
        }
        const dataId = {
            id: this.nextDataId()
        };
        this.data.set(dataId, {
            values,
            dtype,
            refCount: 1
        });
        return dataId;
    }
    /**
     * Create a data bucket in cpu backend.
     * @param shape Shape of the `TensorInfo`.
     * @param dtype DType of the `TensorInfo`.
     * @param values The value of the `TensorInfo` stored as a flattened array.
     */ makeTensorInfo(shape, dtype, values) {
        let outId;
        if (dtype === "string" && values != null && values.length > 0 && (0, _tfjsCore.util).isString(values[0])) {
            const encodedValues = values.map((d)=>(0, _tfjsCore.util).encodeString(d));
            outId = this.write(encodedValues, shape, dtype);
        } else outId = this.write(values, shape, dtype);
        return {
            dataId: outId,
            shape,
            dtype
        };
    }
    /** Return refCount of a `TensorData`. */ refCount(dataId) {
        if (this.data.has(dataId)) {
            const tensorData = this.data.get(dataId);
            return tensorData.refCount;
        }
        return 0;
    }
    /** Increase refCount of a `TensorData`. */ incRef(dataId) {
        const tensorData = this.data.get(dataId);
        tensorData.refCount++;
    }
    /** Decrease refCount of a `TensorData`. */ decRef(dataId) {
        if (this.data.has(dataId)) {
            const tensorData = this.data.get(dataId);
            tensorData.refCount--;
        }
    }
    move(dataId, values, shape, dtype, refCount) {
        this.data.set(dataId, {
            values,
            dtype,
            refCount
        });
    }
    numDataIds() {
        return this.data.numDataIds();
    }
    async read(dataId) {
        return this.readSync(dataId);
    }
    readSync(dataId) {
        const { dtype, complexTensorInfos } = this.data.get(dataId);
        if (dtype === "complex64") {
            const realValues = this.readSync(complexTensorInfos.real.dataId);
            const imagValues = this.readSync(complexTensorInfos.imag.dataId);
            return (0, _tfjsCore.backend_util).mergeRealAndImagArrays(realValues, imagValues);
        }
        return (0, _tfjsCore.util).convertBackendValuesAndArrayBuffer(this.data.get(dataId).values, dtype);
    }
    bufferSync(t) {
        const data = this.readSync(t.dataId);
        if (t.dtype === "string") try {
            // Decode the bytes into string.
            const strings = data.map((d)=>(0, _tfjsCore.util).decodeString(d));
            return (0, _tfjsCore.buffer)(t.shape, t.dtype, strings);
        } catch (_a) {
            throw new Error("Failed to decode encoded string bytes into utf-8");
        }
        return (0, _tfjsCore.buffer)(t.shape, t.dtype, data);
    }
    makeOutput(values, shape, dtype) {
        return (0, _tfjsCore.engine)().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
    }
    /**
     * Dispose the memory if the dataId has 0 refCount. Return true if the memory
     * is released or memory is not managed in this backend, false if memory is
     * not cleared.
     * @param dataId
     * @oaram force Optional, remove the data regardless of refCount
     */ disposeData(dataId, force = false) {
        if (this.data.has(dataId)) {
            this.data.get(dataId).refCount--;
            if (!force && this.data.get(dataId).refCount > 0) return false;
            const { complexTensorInfos } = this.data.get(dataId);
            if (complexTensorInfos != null) {
                this.disposeData(complexTensorInfos.real.dataId, true);
                this.disposeData(complexTensorInfos.imag.dataId, true);
            }
            this.data.delete(dataId);
        }
        return true;
    }
    disposeIntermediateTensorInfo(tensorInfo) {
        this.disposeData(tensorInfo.dataId);
    }
    async time(f) {
        const start = (0, _tfjsCore.util).now();
        f();
        const kernelMs = (0, _tfjsCore.util).now() - start;
        return {
            kernelMs
        };
    }
    memory() {
        return {
            // Unreliable due to automatic gc. The numbers above are cumulative.
            unreliable: true,
            reasons: [
                "The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."
            ]
        };
    }
    where(condition) {
        (0, _cpuUtil.assertNotComplex)([
            condition
        ], "where");
        const condVals = this.readSync(condition.dataId);
        return whereImpl(condition.shape, condVals);
    }
    dispose() {}
    floatPrecision() {
        return 32;
    }
    /** Returns the smallest representable number.  */ epsilon() {
        return super.epsilon();
    }
}
MathBackendCPU.nextDataId = 0;

},{"@tensorflow/tfjs-core":"21Ftl","./cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fow87":[function(require,module,exports) {
/** @license See the LICENSE file. */ // This code is auto-generated, do not modify this file!
var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "version", ()=>version);
const version = "4.21.0";

},{"@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"89NZo":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ // We explicitly import the modular kernels so they get registered in the
// global registry when we compile the library. A modular build would replace
// the contents of this file and import only the kernels that are needed.
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fusedMatMul = require("./kernels/_FusedMatMul");
var _abs = require("./kernels/Abs");
var _acos = require("./kernels/Acos");
var _acosh = require("./kernels/Acosh");
var _add = require("./kernels/Add");
var _addN = require("./kernels/AddN");
var _all = require("./kernels/All");
var _any = require("./kernels/Any");
var _argMax = require("./kernels/ArgMax");
var _argMin = require("./kernels/ArgMin");
var _asin = require("./kernels/Asin");
var _asinh = require("./kernels/Asinh");
var _atan = require("./kernels/Atan");
var _atan2 = require("./kernels/Atan2");
var _atanh = require("./kernels/Atanh");
var _avgPool = require("./kernels/AvgPool");
var _avgPool3D = require("./kernels/AvgPool3D");
var _avgPool3DGrad = require("./kernels/AvgPool3DGrad");
var _avgPoolGrad = require("./kernels/AvgPoolGrad");
var _batchMatMul = require("./kernels/BatchMatMul");
var _batchNorm = require("./kernels/BatchNorm");
var _batchToSpaceND = require("./kernels/BatchToSpaceND");
var _bincount = require("./kernels/Bincount");
var _bitwiseAnd = require("./kernels/BitwiseAnd");
var _broadcastArgs = require("./kernels/BroadcastArgs");
var _cast = require("./kernels/Cast");
var _ceil = require("./kernels/Ceil");
var _clipByValue = require("./kernels/ClipByValue");
var _complex = require("./kernels/Complex");
var _complexAbs = require("./kernels/ComplexAbs");
var _concat = require("./kernels/Concat");
var _conv2D = require("./kernels/Conv2D");
var _conv2DBackpropFilter = require("./kernels/Conv2DBackpropFilter");
var _conv2DBackpropInput = require("./kernels/Conv2DBackpropInput");
var _conv3D = require("./kernels/Conv3D");
var _conv3DBackpropFilterV2 = require("./kernels/Conv3DBackpropFilterV2");
var _conv3DBackpropInputV2 = require("./kernels/Conv3DBackpropInputV2");
var _cos = require("./kernels/Cos");
var _cosh = require("./kernels/Cosh");
var _cropAndResize = require("./kernels/CropAndResize");
var _cumprod = require("./kernels/Cumprod");
var _cumsum = require("./kernels/Cumsum");
var _denseBincount = require("./kernels/DenseBincount");
var _depthToSpace = require("./kernels/DepthToSpace");
var _depthwiseConv2DNative = require("./kernels/DepthwiseConv2dNative");
var _depthwiseConv2DNativeBackpropFilter = require("./kernels/DepthwiseConv2dNativeBackpropFilter");
var _depthwiseConv2DNativeBackpropInput = require("./kernels/DepthwiseConv2dNativeBackpropInput");
var _diag = require("./kernels/Diag");
var _dilation2D = require("./kernels/Dilation2D");
var _dilation2DBackpropFilter = require("./kernels/Dilation2DBackpropFilter");
var _dilation2DBackpropInput = require("./kernels/Dilation2DBackpropInput");
var _draw = require("./kernels/Draw");
var _einsum = require("./kernels/Einsum");
var _elu = require("./kernels/Elu");
var _eluGrad = require("./kernels/EluGrad");
var _equal = require("./kernels/Equal");
var _erf = require("./kernels/Erf");
var _exp = require("./kernels/Exp");
var _expandDims = require("./kernels/ExpandDims");
var _expm1 = require("./kernels/Expm1");
var _fft = require("./kernels/FFT");
var _fill = require("./kernels/Fill");
var _flipLeftRight = require("./kernels/FlipLeftRight");
var _floor = require("./kernels/Floor");
var _floorDiv = require("./kernels/FloorDiv");
var _fusedConv2D = require("./kernels/FusedConv2D");
var _fusedDepthwiseConv2D = require("./kernels/FusedDepthwiseConv2D");
var _gatherNd = require("./kernels/GatherNd");
var _gatherV2 = require("./kernels/GatherV2");
var _greater = require("./kernels/Greater");
var _greaterEqual = require("./kernels/GreaterEqual");
var _identity = require("./kernels/Identity");
var _ifft = require("./kernels/IFFT");
var _imag = require("./kernels/Imag");
var _isFinite = require("./kernels/IsFinite");
var _isInf = require("./kernels/IsInf");
var _isNaN = require("./kernels/IsNaN");
var _leakyRelu = require("./kernels/LeakyRelu");
var _less = require("./kernels/Less");
var _lessEqual = require("./kernels/LessEqual");
var _linSpace = require("./kernels/LinSpace");
var _log = require("./kernels/Log");
var _log1P = require("./kernels/Log1p");
var _logicalAnd = require("./kernels/LogicalAnd");
var _logicalNot = require("./kernels/LogicalNot");
var _logicalOr = require("./kernels/LogicalOr");
var _lrn = require("./kernels/LRN");
var _lrngrad = require("./kernels/LRNGrad");
var _max = require("./kernels/Max");
var _maximum = require("./kernels/Maximum");
var _maxPool = require("./kernels/MaxPool");
var _maxPool3D = require("./kernels/MaxPool3D");
var _maxPool3DGrad = require("./kernels/MaxPool3DGrad");
var _maxPoolGrad = require("./kernels/MaxPoolGrad");
var _maxPoolWithArgmax = require("./kernels/MaxPoolWithArgmax");
var _mean = require("./kernels/Mean");
var _min = require("./kernels/Min");
var _minimum = require("./kernels/Minimum");
var _mirrorPad = require("./kernels/MirrorPad");
var _mod = require("./kernels/Mod");
var _multinomial = require("./kernels/Multinomial");
var _multiply = require("./kernels/Multiply");
var _neg = require("./kernels/Neg");
var _nonMaxSuppressionV3 = require("./kernels/NonMaxSuppressionV3");
var _nonMaxSuppressionV4 = require("./kernels/NonMaxSuppressionV4");
var _nonMaxSuppressionV5 = require("./kernels/NonMaxSuppressionV5");
var _notEqual = require("./kernels/NotEqual");
var _oneHot = require("./kernels/OneHot");
var _onesLike = require("./kernels/OnesLike");
var _pack = require("./kernels/Pack");
var _padV2 = require("./kernels/PadV2");
var _pow = require("./kernels/Pow");
var _prelu = require("./kernels/Prelu");
var _prod = require("./kernels/Prod");
var _raggedGather = require("./kernels/RaggedGather");
var _raggedRange = require("./kernels/RaggedRange");
var _raggedTensorToTensor = require("./kernels/RaggedTensorToTensor");
var _range = require("./kernels/Range");
var _real = require("./kernels/Real");
var _realDiv = require("./kernels/RealDiv");
var _reciprocal = require("./kernels/Reciprocal");
var _relu = require("./kernels/Relu");
var _relu6 = require("./kernels/Relu6");
var _reshape = require("./kernels/Reshape");
var _resizeBilinear = require("./kernels/ResizeBilinear");
var _resizeBilinearGrad = require("./kernels/ResizeBilinearGrad");
var _resizeNearestNeighbor = require("./kernels/ResizeNearestNeighbor");
var _resizeNearestNeighborGrad = require("./kernels/ResizeNearestNeighborGrad");
var _reverse = require("./kernels/Reverse");
var _rotateWithOffset = require("./kernels/RotateWithOffset");
var _round = require("./kernels/Round");
var _rsqrt = require("./kernels/Rsqrt");
var _scatterNd = require("./kernels/ScatterNd");
var _searchSorted = require("./kernels/SearchSorted");
var _select = require("./kernels/Select");
var _selu = require("./kernels/Selu");
var _sigmoid = require("./kernels/Sigmoid");
var _sign = require("./kernels/Sign");
var _sin = require("./kernels/Sin");
var _sinh = require("./kernels/Sinh");
var _slice = require("./kernels/Slice");
var _softmax = require("./kernels/Softmax");
var _softplus = require("./kernels/Softplus");
var _spaceToBatchND = require("./kernels/SpaceToBatchND");
var _sparseFillEmptyRows = require("./kernels/SparseFillEmptyRows");
var _sparseReshape = require("./kernels/SparseReshape");
var _sparseSegmentMean = require("./kernels/SparseSegmentMean");
var _sparseSegmentSum = require("./kernels/SparseSegmentSum");
var _sparseToDense = require("./kernels/SparseToDense");
var _splitV = require("./kernels/SplitV");
var _sqrt = require("./kernels/Sqrt");
var _square = require("./kernels/Square");
var _squaredDifference = require("./kernels/SquaredDifference");
var _staticRegexReplace = require("./kernels/StaticRegexReplace");
var _step = require("./kernels/Step");
var _stridedSlice = require("./kernels/StridedSlice");
var _stringNGrams = require("./kernels/StringNGrams");
var _stringSplit = require("./kernels/StringSplit");
var _stringToHashBucketFast = require("./kernels/StringToHashBucketFast");
var _sub = require("./kernels/Sub");
var _sum = require("./kernels/Sum");
var _tan = require("./kernels/Tan");
var _tanh = require("./kernels/Tanh");
var _tensorScatterUpdate = require("./kernels/TensorScatterUpdate");
var _tile = require("./kernels/Tile");
var _topK = require("./kernels/TopK");
var _transform = require("./kernels/Transform");
var _transpose = require("./kernels/Transpose");
var _unique = require("./kernels/Unique");
var _unpack = require("./kernels/Unpack");
var _unsortedSegmentSum = require("./kernels/UnsortedSegmentSum");
var _zerosLike = require("./kernels/ZerosLike");
// List all kernel configs here
const kernelConfigs = [
    (0, _fusedMatMul._fusedMatMulConfig),
    (0, _abs.absConfig),
    (0, _acos.acosConfig),
    (0, _acosh.acoshConfig),
    (0, _add.addConfig),
    (0, _addN.addNConfig),
    (0, _all.allConfig),
    (0, _any.anyConfig),
    (0, _argMax.argMaxConfig),
    (0, _argMin.argMinConfig),
    (0, _asin.asinConfig),
    (0, _asinh.asinhConfig),
    (0, _atan.atanConfig),
    (0, _atan2.atan2Config),
    (0, _atanh.atanhConfig),
    (0, _avgPool.avgPoolConfig),
    (0, _avgPool3D.avgPool3DConfig),
    (0, _avgPool3DGrad.avgPool3DGradConfig),
    (0, _avgPoolGrad.avgPoolGradConfig),
    (0, _batchMatMul.batchMatMulConfig),
    (0, _batchNorm.batchNormConfig),
    (0, _batchToSpaceND.batchToSpaceNDConfig),
    (0, _bincount.bincountConfig),
    (0, _bitwiseAnd.bitwiseAndConfig),
    (0, _broadcastArgs.broadcastArgsConfig),
    (0, _cast.castConfig),
    (0, _ceil.ceilConfig),
    (0, _clipByValue.clipByValueConfig),
    (0, _complex.complexConfig),
    (0, _complexAbs.complexAbsConfig),
    (0, _concat.concatConfig),
    (0, _conv2D.conv2DConfig),
    (0, _conv2DBackpropFilter.conv2DBackpropFilterConfig),
    (0, _conv2DBackpropInput.conv2DBackpropInputConfig),
    (0, _conv3D.conv3DConfig),
    (0, _conv3DBackpropFilterV2.conv3DBackpropFilterV2Config),
    (0, _conv3DBackpropInputV2.conv3DBackpropInputV2Config),
    (0, _cos.cosConfig),
    (0, _cosh.coshConfig),
    (0, _cropAndResize.cropAndResizeConfig),
    (0, _cumprod.cumprodConfig),
    (0, _cumsum.cumsumConfig),
    (0, _denseBincount.denseBincountConfig),
    (0, _depthToSpace.depthToSpaceConfig),
    (0, _depthwiseConv2DNative.depthwiseConv2dNativeConfig),
    (0, _depthwiseConv2DNativeBackpropFilter.depthwiseConv2dNativeBackpropFilterConfig),
    (0, _depthwiseConv2DNativeBackpropInput.depthwiseConv2dNativeBackpropInputConfig),
    (0, _diag.diagConfig),
    (0, _dilation2D.dilation2DConfig),
    (0, _dilation2DBackpropFilter.dilation2DBackpropFilterConfig),
    (0, _dilation2DBackpropInput.dilation2DBackpropInputConfig),
    (0, _draw.drawConfig),
    (0, _einsum.einsumConfig),
    (0, _elu.eluConfig),
    (0, _eluGrad.eluGradConfig),
    (0, _equal.equalConfig),
    (0, _erf.erfConfig),
    (0, _exp.expConfig),
    (0, _expandDims.expandDimsConfig),
    (0, _expm1.expm1Config),
    (0, _fft.fftConfig),
    (0, _fill.fillConfig),
    (0, _flipLeftRight.flipLeftRightConfig),
    (0, _floor.floorConfig),
    (0, _floorDiv.floorDivConfig),
    (0, _fusedConv2D.fusedConv2DConfig),
    (0, _fusedDepthwiseConv2D.fusedDepthwiseConv2DConfig),
    (0, _gatherNd.gatherNdConfig),
    (0, _gatherV2.gatherV2Config),
    (0, _greater.greaterConfig),
    (0, _greaterEqual.greaterEqualConfig),
    (0, _identity.identityConfig),
    (0, _ifft.ifftConfig),
    (0, _imag.imagConfig),
    (0, _isFinite.isFiniteConfig),
    (0, _isInf.isInfConfig),
    (0, _isNaN.isNaNConfig),
    (0, _leakyRelu.leakyReluConfig),
    (0, _less.lessConfig),
    (0, _lessEqual.lessEqualConfig),
    (0, _linSpace.linSpaceConfig),
    (0, _log.logConfig),
    (0, _log1P.log1pConfig),
    (0, _logicalAnd.logicalAndConfig),
    (0, _logicalNot.logicalNotConfig),
    (0, _logicalOr.logicalOrConfig),
    (0, _lrn.LRNConfig),
    (0, _lrngrad.LRNGradConfig),
    (0, _max.maxConfig),
    (0, _maximum.maximumConfig),
    (0, _maxPool.maxPoolConfig),
    (0, _maxPool3D.maxPool3DConfig),
    (0, _maxPool3DGrad.maxPool3DGradConfig),
    (0, _maxPoolGrad.maxPoolGradConfig),
    (0, _maxPoolWithArgmax.maxPoolWithArgmaxConfig),
    (0, _mean.meanConfig),
    (0, _min.minConfig),
    (0, _minimum.minimumConfig),
    (0, _mirrorPad.mirrorPadConfig),
    (0, _mod.modConfig),
    (0, _multinomial.multinomialConfig),
    (0, _multiply.multiplyConfig),
    (0, _neg.negConfig),
    (0, _nonMaxSuppressionV3.nonMaxSuppressionV3Config),
    (0, _nonMaxSuppressionV4.nonMaxSuppressionV4Config),
    (0, _nonMaxSuppressionV5.nonMaxSuppressionV5Config),
    (0, _notEqual.notEqualConfig),
    (0, _oneHot.oneHotConfig),
    (0, _onesLike.onesLikeConfig),
    (0, _pack.packConfig),
    (0, _padV2.padV2Config),
    (0, _pow.powConfig),
    (0, _prelu.preluConfig),
    (0, _prod.prodConfig),
    (0, _raggedGather.raggedGatherConfig),
    (0, _raggedRange.raggedRangeConfig),
    (0, _raggedTensorToTensor.raggedTensorToTensorConfig),
    (0, _range.rangeConfig),
    (0, _real.realConfig),
    (0, _realDiv.realDivConfig),
    (0, _reciprocal.reciprocalConfig),
    (0, _relu.reluConfig),
    (0, _relu6.relu6Config),
    (0, _reshape.reshapeConfig),
    (0, _resizeBilinear.resizeBilinearConfig),
    (0, _resizeBilinearGrad.resizeBilinearGradConfig),
    (0, _resizeNearestNeighbor.resizeNearestNeighborConfig),
    (0, _resizeNearestNeighborGrad.resizeNearestNeighborGradConfig),
    (0, _reverse.reverseConfig),
    (0, _rotateWithOffset.rotateWithOffsetConfig),
    (0, _round.roundConfig),
    (0, _rsqrt.rsqrtConfig),
    (0, _scatterNd.scatterNdConfig),
    (0, _searchSorted.searchSortedConfig),
    (0, _select.selectConfig),
    (0, _selu.seluConfig),
    (0, _sigmoid.sigmoidConfig),
    (0, _sign.signConfig),
    (0, _sin.sinConfig),
    (0, _sinh.sinhConfig),
    (0, _slice.sliceConfig),
    (0, _softmax.softmaxConfig),
    (0, _softplus.softplusConfig),
    (0, _spaceToBatchND.spaceToBatchNDConfig),
    (0, _sparseFillEmptyRows.sparseFillEmptyRowsConfig),
    (0, _sparseReshape.sparseReshapeConfig),
    (0, _sparseSegmentMean.sparseSegmentMeanConfig),
    (0, _sparseSegmentSum.sparseSegmentSumConfig),
    (0, _sparseToDense.sparseToDenseConfig),
    (0, _splitV.splitVConfig),
    (0, _sqrt.sqrtConfig),
    (0, _square.squareConfig),
    (0, _squaredDifference.squaredDifferenceConfig),
    (0, _staticRegexReplace.staticRegexReplaceConfig),
    (0, _step.stepConfig),
    (0, _stridedSlice.stridedSliceConfig),
    (0, _stringNGrams.stringNGramsConfig),
    (0, _stringSplit.stringSplitConfig),
    (0, _stringToHashBucketFast.stringToHashBucketFastConfig),
    (0, _sub.subConfig),
    (0, _sum.sumConfig),
    (0, _tan.tanConfig),
    (0, _tanh.tanhConfig),
    (0, _tensorScatterUpdate.tensorScatterUpdateConfig),
    (0, _tile.tileConfig),
    (0, _topK.topKConfig),
    (0, _transform.transformConfig),
    (0, _transpose.transposeConfig),
    (0, _unique.uniqueConfig),
    (0, _unpack.unpackConfig),
    (0, _unsortedSegmentSum.unsortedSegmentSumConfig),
    (0, _zerosLike.zerosLikeConfig)
];
for (const kernelConfig of kernelConfigs)(0, _tfjsCore.registerKernel)(kernelConfig);

},{"@tensorflow/tfjs-core":"21Ftl","./kernels/_FusedMatMul":"2uXSk","./kernels/Abs":"2vVaE","./kernels/Acos":"2M2i0","./kernels/Acosh":"bYe62","./kernels/Add":"dippY","./kernels/AddN":"dmcG0","./kernels/All":"6pIZf","./kernels/Any":"5AV1Z","./kernels/ArgMax":"6P1xW","./kernels/ArgMin":"1cyKX","./kernels/Asin":"kRHNu","./kernels/Asinh":"bYcdN","./kernels/Atan":"a4d7R","./kernels/Atan2":"gUrSR","./kernels/Atanh":"dsEHm","./kernels/AvgPool":"8IIie","./kernels/AvgPool3D":"eu5qc","./kernels/AvgPool3DGrad":"iGPNS","./kernels/AvgPoolGrad":"kCJ3l","./kernels/BatchMatMul":"i8QFX","./kernels/BatchNorm":"a3KQN","./kernels/BatchToSpaceND":"7Jug2","./kernels/Bincount":"hE7iz","./kernels/BitwiseAnd":"lFHUr","./kernels/BroadcastArgs":"4GYx6","./kernels/Cast":"fAtIw","./kernels/Ceil":"dPS5d","./kernels/ClipByValue":"ck0lb","./kernels/Complex":"3DPmh","./kernels/ComplexAbs":"kThBM","./kernels/Concat":"iYEAT","./kernels/Conv2D":"4bj3X","./kernels/Conv2DBackpropFilter":"jHKF1","./kernels/Conv2DBackpropInput":"fBpei","./kernels/Conv3D":"iJ4dh","./kernels/Conv3DBackpropFilterV2":"g7cYG","./kernels/Conv3DBackpropInputV2":"l6kPr","./kernels/Cos":"hkcOn","./kernels/Cosh":"eg9u0","./kernels/CropAndResize":"8K1SK","./kernels/Cumprod":"bN0pp","./kernels/Cumsum":"8YA18","./kernels/DenseBincount":"cpFc0","./kernels/DepthToSpace":"l4MfW","./kernels/DepthwiseConv2dNative":"2iRpa","./kernels/DepthwiseConv2dNativeBackpropFilter":"j6FA2","./kernels/DepthwiseConv2dNativeBackpropInput":"4Kyp1","./kernels/Diag":"c5zdj","./kernels/Dilation2D":"8CglA","./kernels/Dilation2DBackpropFilter":"5GOEE","./kernels/Dilation2DBackpropInput":"fnjGq","./kernels/Draw":"9ZCFV","./kernels/Einsum":"lXbVv","./kernels/Elu":"7XTZG","./kernels/EluGrad":"cZudI","./kernels/Equal":"jK2O7","./kernels/Erf":"g7iiK","./kernels/Exp":"6eexr","./kernels/ExpandDims":"4z77l","./kernels/Expm1":"pXtet","./kernels/FFT":"7gqPz","./kernels/Fill":"4BMgR","./kernels/FlipLeftRight":"8d8ZF","./kernels/Floor":"ftFzk","./kernels/FloorDiv":"kMZnS","./kernels/FusedConv2D":"1OWT7","./kernels/FusedDepthwiseConv2D":"5SkmJ","./kernels/GatherNd":"6Owm3","./kernels/GatherV2":"dUokB","./kernels/Greater":"94zPb","./kernels/GreaterEqual":"a9aqR","./kernels/Identity":"btREJ","./kernels/IFFT":"hHvJu","./kernels/Imag":"af3Tx","./kernels/IsFinite":"a8CWT","./kernels/IsInf":"342Gs","./kernels/IsNaN":"gKXUy","./kernels/LeakyRelu":"hDqHI","./kernels/Less":"anwWy","./kernels/LessEqual":"nFlBF","./kernels/LinSpace":"3aMpD","./kernels/Log":"dNlq0","./kernels/Log1p":"bNfse","./kernels/LogicalAnd":"9q52j","./kernels/LogicalNot":"929wA","./kernels/LogicalOr":"4bpjj","./kernels/LRN":"1ZDWc","./kernels/LRNGrad":"01nUa","./kernels/Max":"ceCEC","./kernels/Maximum":"73P1X","./kernels/MaxPool":"2HK6O","./kernels/MaxPool3D":"guFsS","./kernels/MaxPool3DGrad":"bsOhi","./kernels/MaxPoolGrad":"esvnZ","./kernels/MaxPoolWithArgmax":"aBn34","./kernels/Mean":"hu1l4","./kernels/Min":"h3WES","./kernels/Minimum":"2gO4x","./kernels/MirrorPad":"1HbpJ","./kernels/Mod":"iD4b6","./kernels/Multinomial":"dy4Sw","./kernels/Multiply":"FrRi2","./kernels/Neg":"4sfM4","./kernels/NonMaxSuppressionV3":"cU4dv","./kernels/NonMaxSuppressionV4":"chYiL","./kernels/NonMaxSuppressionV5":"bVnt1","./kernels/NotEqual":"i62sj","./kernels/OneHot":"9Evwj","./kernels/OnesLike":"kWIOw","./kernels/Pack":"3NDYm","./kernels/PadV2":"dssNb","./kernels/Pow":"jx4g8","./kernels/Prelu":"c057T","./kernels/Prod":"8NwGT","./kernels/RaggedGather":"4jEHO","./kernels/RaggedRange":"kFVpX","./kernels/RaggedTensorToTensor":"bJlTR","./kernels/Range":"1Dov9","./kernels/Real":"eZ7aJ","./kernels/RealDiv":"9Ly75","./kernels/Reciprocal":"3BhEV","./kernels/Relu":"iWdDO","./kernels/Relu6":"C9OA6","./kernels/Reshape":"9350i","./kernels/ResizeBilinear":"kU0RB","./kernels/ResizeBilinearGrad":"76Mq3","./kernels/ResizeNearestNeighbor":"5c1lG","./kernels/ResizeNearestNeighborGrad":"4hhu6","./kernels/Reverse":"ff3xk","./kernels/RotateWithOffset":"d9nWG","./kernels/Round":"gnp2v","./kernels/Rsqrt":"gcVCI","./kernels/ScatterNd":"bb9Cv","./kernels/SearchSorted":"f7WfM","./kernels/Select":"lDXuK","./kernels/Selu":"cJWbo","./kernels/Sigmoid":"1a9v3","./kernels/Sign":"erXNP","./kernels/Sin":"c9it7","./kernels/Sinh":"9tr3d","./kernels/Slice":"9L9GN","./kernels/Softmax":"1UnfO","./kernels/Softplus":"bojBS","./kernels/SpaceToBatchND":"cO9CD","./kernels/SparseFillEmptyRows":"cLZdR","./kernels/SparseReshape":"bVjgS","./kernels/SparseSegmentMean":"7zRqG","./kernels/SparseSegmentSum":"dMeLQ","./kernels/SparseToDense":"kfHsN","./kernels/SplitV":"j2Sbu","./kernels/Sqrt":"cnnoj","./kernels/Square":"jjLIw","./kernels/SquaredDifference":"77Ql7","./kernels/StaticRegexReplace":"9G2f2","./kernels/Step":"2cqHx","./kernels/StridedSlice":"l76iO","./kernels/StringNGrams":"7Beq9","./kernels/StringSplit":"6TsmC","./kernels/StringToHashBucketFast":"irRRx","./kernels/Sub":"4DByI","./kernels/Sum":"6gbbV","./kernels/Tan":"aMIl2","./kernels/Tanh":"fdqiG","./kernels/TensorScatterUpdate":"jFIwa","./kernels/Tile":"cYsKW","./kernels/TopK":"9RDRk","./kernels/Transform":"hKZ0R","./kernels/Transpose":"61ywv","./kernels/Unique":"9nca3","./kernels/Unpack":"iIwqM","./kernels/UnsortedSegmentSum":"eWcTc","./kernels/ZerosLike":"hfVJr"}],"2uXSk":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "_fusedMatMul", ()=>_fusedMatMul);
parcelHelpers.export(exports, "_fusedMatMulConfig", ()=>_fusedMatMulConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fusedUtils = require("../utils/fused_utils");
var _add = require("./Add");
var _batchMatMul = require("./BatchMatMul");
function _fusedMatMul(args) {
    const { inputs, backend, attrs } = args;
    const { a, b, bias, preluActivationWeights } = inputs;
    const { transposeA, transposeB, activation, leakyreluAlpha } = attrs;
    let current;
    let addRes;
    let activationRes;
    const intermediates = [];
    const matMulRes = (0, _batchMatMul.batchMatMul)({
        inputs: {
            a,
            b
        },
        attrs: {
            transposeA,
            transposeB
        },
        backend
    });
    current = matMulRes;
    if (bias) {
        addRes = (0, _add.add)({
            inputs: {
                a: current,
                b: bias
            },
            backend
        });
        intermediates.push(current);
        current = addRes;
    }
    if (activation) {
        activationRes = (0, _fusedUtils.applyActivation)(backend, current, activation, preluActivationWeights, leakyreluAlpha);
        intermediates.push(current);
        current = activationRes;
    }
    for (const i of intermediates)backend.disposeIntermediateTensorInfo(i);
    return current;
}
const _fusedMatMulConfig = {
    kernelName: (0, _tfjsCore._FusedMatMul),
    backendName: "cpu",
    kernelFunc: _fusedMatMul
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/fused_utils":"6DUEF","./Add":"dippY","./BatchMatMul":"i8QFX","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6DUEF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "applyActivation", ()=>applyActivation);
var _elu = require("../kernels/Elu");
var _identity = require("../kernels/Identity");
var _leakyRelu = require("../kernels/LeakyRelu");
var _prelu = require("../kernels/Prelu");
var _relu = require("../kernels/Relu");
var _relu6 = require("../kernels/Relu6");
var _sigmoid = require("../kernels/Sigmoid");
function applyActivation(backend, x, activation, preluActivationWeights, leakyreluAlpha) {
    if (activation === "linear") return (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    else if (activation === "relu") return (0, _relu.relu)({
        inputs: {
            x
        },
        backend
    });
    else if (activation === "elu") return (0, _elu.elu)({
        inputs: {
            x
        },
        backend
    });
    else if (activation === "relu6") return (0, _relu6.relu6)({
        inputs: {
            x
        },
        backend
    });
    else if (activation === "prelu") return (0, _prelu.prelu)({
        inputs: {
            x,
            alpha: preluActivationWeights
        },
        backend
    });
    else if (activation === "leakyrelu") return (0, _leakyRelu.leakyRelu)({
        inputs: {
            x
        },
        backend,
        attrs: {
            alpha: leakyreluAlpha
        }
    });
    else if (activation === "sigmoid") return (0, _sigmoid.sigmoid)({
        inputs: {
            x
        },
        backend
    });
    throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);
}

},{"../kernels/Elu":"7XTZG","../kernels/Identity":"btREJ","../kernels/LeakyRelu":"hDqHI","../kernels/Prelu":"c057T","../kernels/Relu":"iWdDO","../kernels/Relu6":"C9OA6","../kernels/Sigmoid":"1a9v3","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7XTZG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "elu", ()=>elu);
parcelHelpers.export(exports, "eluConfig", ()=>eluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const elu = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Elu), (xi)=>xi >= 0 ? xi : Math.exp(xi) - 1);
const eluConfig = {
    kernelName: (0, _tfjsCore.Elu),
    backendName: "cpu",
    kernelFunc: elu
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hDqHI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "leakyRelu", ()=>leakyRelu);
parcelHelpers.export(exports, "leakyReluConfig", ()=>leakyReluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function leakyRelu(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { alpha } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x
    ], "leakyRelu");
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const xVals = backend.data.get(x.dataId).values;
    const outVals = (0, _tfjsCore.util).getTypedArrayFromDType("float32", xSize);
    for(let i = 0; i < xVals.length; i++)outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];
    return backend.makeTensorInfo(x.shape, "float32", outVals);
}
const leakyReluConfig = {
    kernelName: (0, _tfjsCore.LeakyRelu),
    backendName: "cpu",
    kernelFunc: leakyRelu
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c057T":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "prelu", ()=>prelu);
parcelHelpers.export(exports, "preluConfig", ()=>preluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _binaryImpl = require("../utils/binary_impl");
const preluImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((xValue, aValue)=>xValue < 0 ? aValue * xValue : xValue);
function prelu(args) {
    const { inputs, backend } = args;
    const { x, alpha } = inputs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        alpha
    ], "prelu");
    const aVals = backend.data.get(x.dataId).values;
    const bVals = backend.data.get(alpha.dataId).values;
    const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, "float32");
    return backend.makeTensorInfo(resultShape, "float32", resultData);
}
const preluConfig = {
    kernelName: (0, _tfjsCore.Prelu),
    backendName: "cpu",
    kernelFunc: prelu
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/binary_impl":"7WPlP","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iWdDO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "relu", ()=>relu);
parcelHelpers.export(exports, "reluConfig", ()=>reluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const relu = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Relu), (xi)=>Math.max(0, xi));
const reluConfig = {
    kernelName: (0, _tfjsCore.Relu),
    backendName: "cpu",
    kernelFunc: relu
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"C9OA6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "relu6", ()=>relu6);
parcelHelpers.export(exports, "relu6Config", ()=>relu6Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const relu6 = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Relu6), (xi)=>Math.min(Math.max(0, xi), 6));
const relu6Config = {
    kernelName: (0, _tfjsCore.Relu6),
    backendName: "cpu",
    kernelFunc: relu6
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"i8QFX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchMatMul", ()=>batchMatMul);
parcelHelpers.export(exports, "batchMatMulConfig", ()=>batchMatMulConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _reshape = require("./Reshape");
function batchMatMul(args) {
    const { inputs, backend, attrs } = args;
    const { a, b } = inputs;
    const { transposeA, transposeB } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        a,
        b
    ], "matMul");
    const aRank = a.shape.length;
    const bRank = b.shape.length;
    const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
    const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
    const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
    const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
    const outerDimsA = a.shape.slice(0, -2);
    const outerDimsB = b.shape.slice(0, -2);
    const batchDimA = (0, _tfjsCore.util).sizeFromShape(outerDimsA);
    const batchDimB = (0, _tfjsCore.util).sizeFromShape(outerDimsB);
    const outShapeOuterDims = (0, _tfjsCore.broadcast_util).assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));
    const outShape = outShapeOuterDims.concat([
        outerShapeA,
        outerShapeB
    ]);
    (0, _tfjsCore.util).assert(innerShapeA === innerShapeB, ()=>`Error in matMul: inner shapes (${innerShapeA}) and (` + `${innerShapeB}) of Tensors with shapes ${a.shape} and ` + `${b.shape} and transposeA=${transposeA}` + ` and transposeB=${transposeB} must match.`);
    const a3dShape = transposeA ? [
        batchDimA,
        innerShapeA,
        outerShapeA
    ] : [
        batchDimA,
        outerShapeA,
        innerShapeA
    ];
    const b3dShape = transposeB ? [
        batchDimB,
        outerShapeB,
        innerShapeB
    ] : [
        batchDimB,
        innerShapeB,
        outerShapeB
    ];
    // The rest of the implementation is designed to operate on rank-3 tensors
    const a3d = (0, _reshape.reshape)({
        inputs: {
            x: a
        },
        backend,
        attrs: {
            shape: a3dShape
        }
    });
    const b3d = (0, _reshape.reshape)({
        inputs: {
            x: b
        },
        backend,
        attrs: {
            shape: b3dShape
        }
    });
    const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
    const leftDim = transposeA ? a3d.shape[2] : a3d.shape[1];
    const rightDim = transposeB ? b3d.shape[1] : b3d.shape[2];
    const batchDim = Math.max(batchDimA, batchDimB);
    const a3dValues = backend.data.get(a3d.dataId).values;
    const b3dValues = backend.data.get(b3d.dataId).values;
    const a3dStrides = (0, _tfjsCore.util).computeStrides(a3d.shape);
    const b3dStrides = (0, _tfjsCore.util).computeStrides(b3d.shape);
    const [aBatch, aOuterStep, aInnerStep] = transposeA ? [
        a3dStrides[0],
        1,
        a3dStrides[1]
    ] : [
        a3dStrides[0],
        a3dStrides[1],
        1
    ];
    const [bInnerStep, bOuterStep, bBatch] = transposeB ? [
        1,
        b3dStrides[1],
        b3dStrides[0]
    ] : [
        b3dStrides[1],
        1,
        b3dStrides[0]
    ];
    const size = leftDim * rightDim;
    const result = (0, _tfjsCore.buffer)([
        batchDim,
        leftDim,
        rightDim
    ], a3d.dtype);
    const resVals = result.values;
    const blockSize = backend.blockSize;
    for(let bi = 0; bi < batchDim; bi++){
        const batchIndexA = bi % batchDimA;
        const batchIndexB = bi % batchDimB;
        for(let i0 = 0; i0 < leftDim; i0 += blockSize){
            // for when blockSize doesn't evenly divide the input
            const iBlock = Math.min(i0 + blockSize, leftDim);
            for(let j0 = 0; j0 < rightDim; j0 += blockSize){
                const jBlock = Math.min(j0 + blockSize, rightDim);
                for(let k0 = 0; k0 < sharedDim; k0 += blockSize){
                    const kBlock = Math.min(k0 + blockSize, sharedDim);
                    for(let i = i0; i < iBlock; i++)for(let j = j0; j < jBlock; j++){
                        let sum = 0.0;
                        for(let k = k0; k < kBlock; k++){
                            const aVal = // tslint:disable-next-line: max-line-length
                            a3dValues[batchIndexA * aBatch + i * aOuterStep + k * aInnerStep];
                            const bVal = // tslint:disable-next-line: max-line-length
                            b3dValues[k * bInnerStep + j * bOuterStep + batchIndexB * bBatch];
                            sum += aVal * bVal;
                        }
                        resVals[bi * size + (i * rightDim + j)] += sum;
                    }
                }
            }
        }
    }
    backend.disposeIntermediateTensorInfo(a3d);
    backend.disposeIntermediateTensorInfo(b3d);
    // set correct shape on output.
    return backend.makeTensorInfo(outShape, result.dtype, result.values);
}
const batchMatMulConfig = {
    kernelName: (0, _tfjsCore.BatchMatMul),
    backendName: "cpu",
    kernelFunc: batchMatMul
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Reshape":"9350i","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9350i":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reshape", ()=>reshape);
parcelHelpers.export(exports, "reshapeConfig", ()=>reshapeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function reshape(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { shape } = attrs;
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const $shape = (0, _tfjsCore.util).inferFromImplicitShape(shape, xSize);
    const $xSize = (0, _tfjsCore.util).sizeFromShape($shape);
    (0, _tfjsCore.util).assert(xSize === $xSize, ()=>`The new shape (${$shape}) has ${$xSize} elements and the old ` + `shape (${x.shape}) has ${xSize} elements. The new shape and old ` + `shape must have the same number of elements.`);
    backend.incRef(x.dataId);
    const xData = backend.data.get(x.dataId);
    if (xData.complexTensorInfos != null) {
        const real = xData.complexTensorInfos.real;
        const imag = xData.complexTensorInfos.imag;
        real.shape = $shape;
        imag.shape = $shape;
    }
    return {
        dataId: x.dataId,
        shape: $shape,
        dtype: x.dtype
    };
}
const reshapeConfig = {
    kernelName: (0, _tfjsCore.Reshape),
    backendName: "cpu",
    kernelFunc: reshape
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2M2i0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "acos", ()=>acos);
parcelHelpers.export(exports, "acosConfig", ()=>acosConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const acos = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Acos), (xi)=>Math.acos(xi));
const acosConfig = {
    kernelName: (0, _tfjsCore.Acos),
    backendName: "cpu",
    kernelFunc: acos
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bYe62":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "acosh", ()=>acosh);
parcelHelpers.export(exports, "acoshConfig", ()=>acoshConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const acosh = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Acosh), (xi)=>Math.acosh(xi));
const acoshConfig = {
    kernelName: (0, _tfjsCore.Acosh),
    backendName: "cpu",
    kernelFunc: acosh
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dmcG0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "addN", ()=>addN);
parcelHelpers.export(exports, "addNConfig", ()=>addNConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function addN(args) {
    const { inputs, backend } = args;
    const tensors = inputs;
    (0, _cpuUtil.assertNotComplex)(inputs, "addN");
    const vals = tensors.map((t)=>backend.data.get(t.dataId).values);
    const outBuf = (0, _tfjsCore.buffer)(tensors[0].shape, tensors[0].dtype);
    const outVals = outBuf.values;
    for(let i = 0; i < tensors.length; i++){
        const currVals = vals[i];
        for(let j = 0; j < outVals.length; j++)outVals[j] += currVals[j];
    }
    return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
}
const addNConfig = {
    kernelName: (0, _tfjsCore.AddN),
    backendName: "cpu",
    kernelFunc: addN
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6pIZf":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "all", ()=>all);
parcelHelpers.export(exports, "allConfig", ()=>allConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function all(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "all");
    const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, x.shape.length);
    let $x = x;
    if (permutedAxes != null) {
        $x = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, x.shape.length);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("all", axes, $x.shape.length);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes($x.shape, axes);
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const vals = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape(outShape), $x.dtype);
    const aVals = backend.data.get($x.dataId).values;
    for(let i = 0; i < vals.length; ++i){
        const offset = i * reduceSize;
        let all = aVals[offset];
        for(let j = 0; j < reduceSize; ++j){
            const value = aVals[offset + j];
            all = all && value;
        }
        vals[i] = all;
    }
    if (permutedAxes != null) backend.disposeIntermediateTensorInfo($x);
    const result = backend.makeTensorInfo(outShape, $x.dtype, vals);
    if (keepDims) {
        const expandedShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(outShape, origAxes);
        const reshapedResult = (0, _reshape.reshape)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                shape: expandedShape
            }
        });
        backend.disposeIntermediateTensorInfo(result);
        return reshapedResult;
    }
    return result;
}
const allConfig = {
    kernelName: (0, _tfjsCore.All),
    backendName: "cpu",
    kernelFunc: all
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Reshape":"9350i","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5AV1Z":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "any", ()=>any);
parcelHelpers.export(exports, "anyConfig", ()=>anyConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function any(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "any");
    const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, x.shape.length);
    let $x = x;
    if (permutedAxes != null) {
        $x = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, x.shape.length);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("any", axes, $x.shape.length);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes($x.shape, axes);
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const vals = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape(outShape), $x.dtype);
    const aVals = backend.data.get($x.dataId).values;
    for(let i = 0; i < vals.length; ++i){
        const offset = i * reduceSize;
        let anyVal = aVals[offset];
        for(let j = 0; j < reduceSize; ++j){
            const value = aVals[offset + j];
            anyVal = anyVal || value;
        }
        vals[i] = anyVal;
    }
    if (permutedAxes != null) backend.disposeIntermediateTensorInfo($x);
    const result = backend.makeTensorInfo(outShape, $x.dtype, vals);
    if (keepDims) {
        const expandedShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(outShape, origAxes);
        const reshapedResult = (0, _reshape.reshape)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                shape: expandedShape
            }
        });
        backend.disposeIntermediateTensorInfo(result);
        return reshapedResult;
    }
    return result;
}
const anyConfig = {
    kernelName: (0, _tfjsCore.Any),
    backendName: "cpu",
    kernelFunc: any
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Reshape":"9350i","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6P1xW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "argMax", ()=>argMax);
parcelHelpers.export(exports, "argMaxConfig", ()=>argMaxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _transpose = require("./Transpose");
function argMax(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "argMax");
    let axes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
        $x = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        intermediateTensorInfos.push($x);
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, $x.shape.length);
    }
    axes = [
        axes[0]
    ];
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("argMax", axes, $x.shape.length);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes($x.shape, axes);
    const outSize = (0, _tfjsCore.util).sizeFromShape(outShape);
    const vals = (0, _tfjsCore.util).makeZerosTypedArray(outSize, "int32");
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const aVals = backend.data.get($x.dataId).values;
    for(let i = 0; i < vals.length; ++i){
        const offset = i * reduceSize;
        let max = aVals[offset];
        let maxIndex = 0;
        for(let j = 0; j < reduceSize; ++j){
            const value = aVals[offset + j];
            if (value > max) {
                max = value;
                maxIndex = j;
            }
        }
        vals[i] = maxIndex;
    }
    intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return backend.makeTensorInfo(outShape, "int32", vals);
}
const argMaxConfig = {
    kernelName: (0, _tfjsCore.ArgMax),
    backendName: "cpu",
    kernelFunc: argMax
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1cyKX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "argMin", ()=>argMin);
parcelHelpers.export(exports, "argMinConfig", ()=>argMinConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _transpose = require("./Transpose");
function argMin(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "argMin");
    let axes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
        $x = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        intermediateTensorInfos.push($x);
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, $x.shape.length);
    }
    axes = [
        axes[0]
    ];
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("argMin", axes, $x.shape.length);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes($x.shape, axes);
    const outSize = (0, _tfjsCore.util).sizeFromShape(outShape);
    const vals = (0, _tfjsCore.util).makeZerosTypedArray(outSize, "int32");
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const aVals = backend.data.get($x.dataId).values;
    for(let i = 0; i < vals.length; ++i){
        const offset = i * reduceSize;
        let min = aVals[offset];
        let minIndex = 0;
        for(let j = 0; j < reduceSize; ++j){
            const value = aVals[offset + j];
            if (value < min) {
                min = value;
                minIndex = j;
            }
        }
        vals[i] = minIndex;
    }
    intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return backend.makeTensorInfo(outShape, "int32", vals);
}
const argMinConfig = {
    kernelName: (0, _tfjsCore.ArgMin),
    backendName: "cpu",
    kernelFunc: argMin
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kRHNu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "asin", ()=>asin);
parcelHelpers.export(exports, "asinConfig", ()=>asinConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const asin = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Asin), (xi)=>Math.asin(xi));
const asinConfig = {
    kernelName: (0, _tfjsCore.Asin),
    backendName: "cpu",
    kernelFunc: asin
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bYcdN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "asinh", ()=>asinh);
parcelHelpers.export(exports, "asinhConfig", ()=>asinhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const asinh = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Asinh), (xi)=>Math.asinh(xi));
const asinhConfig = {
    kernelName: (0, _tfjsCore.Asinh),
    backendName: "cpu",
    kernelFunc: asinh
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a4d7R":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atan", ()=>atan);
parcelHelpers.export(exports, "atanConfig", ()=>atanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const atan = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Atan), (xi)=>Math.atan(xi));
const atanConfig = {
    kernelName: (0, _tfjsCore.Atan),
    backendName: "cpu",
    kernelFunc: atan
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gUrSR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atan2Impl", ()=>atan2Impl);
parcelHelpers.export(exports, "atan2", ()=>atan2);
parcelHelpers.export(exports, "atan2Config", ()=>atan2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const atan2Impl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((aValue, bValue)=>Math.atan2(aValue, bValue));
const atan2 = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Atan2), atan2Impl);
const atan2Config = {
    kernelName: (0, _tfjsCore.Atan2),
    backendName: "cpu",
    kernelFunc: atan2
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dsEHm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "atanh", ()=>atanh);
parcelHelpers.export(exports, "atanhConfig", ()=>atanhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const atanh = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Atanh), (xi)=>Math.atanh(xi));
const atanhConfig = {
    kernelName: (0, _tfjsCore.Atanh),
    backendName: "cpu",
    kernelFunc: atanh
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8IIie":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool", ()=>avgPool);
parcelHelpers.export(exports, "avgPoolConfig", ()=>avgPoolConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _poolUtils = require("../utils/pool_utils");
var _identity = require("./Identity");
function avgPool(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    (0, _cpuUtil.assertNotComplex)(x, "avgPool");
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const dilations = 1;
    (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in avgPool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
    let res;
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && (0, _tfjsCore.util).arraysEqual(convInfo.inShape, convInfo.outShape)) res = (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    else {
        const xValues = backend.data.get(x.dataId).values;
        const strides = (0, _tfjsCore.util).computeStrides(x.shape);
        const buffer = (0, _poolUtils.pool)(xValues, x.shape, x.dtype, strides, convInfo, "avg");
        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);
    }
    return res;
}
const avgPoolConfig = {
    kernelName: (0, _tfjsCore.AvgPool),
    backendName: "cpu",
    kernelFunc: avgPool
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/pool_utils":"fzyhw","./Identity":"btREJ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fzyhw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pool", ()=>pool);
parcelHelpers.export(exports, "maxPoolPositions", ()=>maxPoolPositions);
parcelHelpers.export(exports, "pool3d", ()=>pool3d);
parcelHelpers.export(exports, "maxPool3dPositions", ()=>maxPool3dPositions);
var _tfjsCore = require("@tensorflow/tfjs-core");
function pool(xValues, xShape, dtype, strides, convInfo, poolType) {
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
    const output = (0, _tfjsCore.buffer)(convInfo.outShape, dtype);
    const outputVals = output.values;
    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
    const outputColStrides = convInfo.outShape[3];
    for(let b = 0; b < convInfo.batchSize; ++b){
        const outputBatchOffset = b * outputBatchStrides;
        const inputBatchOffset = b * strides[0];
        for(let d = 0; d < convInfo.inChannels; ++d)for(let yR = 0; yR < convInfo.outHeight; ++yR){
            const xRCorner = yR * strideHeight - padTop;
            const xRMin = Math.max(0, xRCorner);
            const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
            const outputRowOffset = outputBatchOffset + yR * outputRowStrides;
            for(let yC = 0; yC < convInfo.outWidth; ++yC){
                const xCCorner = yC * strideWidth - padLeft;
                const xCMin = Math.max(0, xCCorner);
                const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
                let minMaxValue = initialValue;
                let avgValue = 0;
                let count = 0;
                for(let xR = xRMin; xR < xRMax; xR += dilationHeight){
                    const xROffset = inputBatchOffset + xR * strides[1];
                    for(let xC = xCMin; xC < xCMax; xC += dilationWidth){
                        const xCOffset = xROffset + xC * strides[2];
                        const pixel = xValues[xCOffset + d];
                        if (poolType === "max" && pixel > minMaxValue) minMaxValue = pixel;
                        else if (poolType === "avg") {
                            avgValue += pixel;
                            count++;
                        }
                    }
                    if (isNaN(minMaxValue)) break;
                }
                const outputOffset = outputRowOffset + yC * outputColStrides + d;
                outputVals[outputOffset] = poolType === "avg" ? avgValue / count : minMaxValue;
            }
        }
    }
    return output;
}
function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {
    const maxPositions = (0, _tfjsCore.buffer)(convInfo.outShape, "int32");
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    const xBuf = (0, _tfjsCore.buffer)(xShape, dtype, xValues);
    for(let b = 0; b < convInfo.batchSize; ++b){
        for(let d = 0; d < convInfo.inChannels; ++d)for(let yR = 0; yR < convInfo.outHeight; ++yR){
            const xRCorner = yR * strideHeight - padTop;
            let xRMin = xRCorner;
            while(xRMin < 0)xRMin += dilationHeight;
            // const xRMin = Math.max(0, xRCorner);
            const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
            for(let yC = 0; yC < convInfo.outWidth; ++yC){
                const xCCorner = yC * strideWidth - padLeft;
                let xCMin = xCCorner;
                while(xCMin < 0)xCMin += dilationWidth;
                const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
                let maxValue = Number.NEGATIVE_INFINITY;
                let maxPosition = -1;
                for(let xR = xRMin; xR < xRMax; xR += dilationHeight){
                    const wR = xR - xRCorner;
                    for(let xC = xCMin; xC < xCMax; xC += dilationWidth){
                        const wC = xC - xCCorner;
                        // For some reason, disable-next-line is not working
                        // TODO(mattsoulanille): Remove this when switching to TS5.
                        /* tslint:disable: no-unnecessary-type-assertion */ const pixel = xBuf.get(b, xR, xC, d);
                        if (pixel > maxValue) {
                            maxValue = pixel;
                            if (flattenPositions) maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;
                            else maxPosition = wR * effectiveFilterWidth + wC;
                        }
                    }
                }
                maxPositions.set(maxPosition, b, yR, yC, d);
            }
        }
    }
    return maxPositions;
}
function pool3d(xValues, xShape, dtype, strides, convInfo, poolType) {
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = convInfo.padInfo.front;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
    const output = (0, _tfjsCore.buffer)(convInfo.outShape, dtype);
    const outputVals = output.values;
    const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
    const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
    const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];
    const outputColStrides = convInfo.outShape[4];
    for(let batch = 0; batch < convInfo.batchSize; ++batch){
        const outputBatchOffset = batch * outputBatchStrides;
        const inputBatchOffset = batch * strides[0];
        for(let channel = 0; channel < convInfo.inChannels; ++channel)for(let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth){
            const xDepthCorner = yDepth * strideDepth - padFront;
            let xDepthMin = xDepthCorner;
            while(xDepthMin < 0)xDepthMin += dilationDepth;
            const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
            const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;
            for(let yRow = 0; yRow < convInfo.outHeight; ++yRow){
                const xRowCorner = yRow * strideHeight - padTop;
                let xRowMin = xRowCorner;
                while(xRowMin < 0)xRowMin += dilationHeight;
                const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
                const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;
                for(let yCol = 0; yCol < convInfo.outWidth; ++yCol){
                    const xColCorner = yCol * strideWidth - padLeft;
                    let xColMin = xColCorner;
                    while(xColMin < 0)xColMin += dilationWidth;
                    const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
                    // Shader code begins
                    const outputColOffset = outputRowOffset + yCol * outputColStrides;
                    let minMaxValue = initialValue;
                    let avgValue = 0;
                    let count = 0;
                    for(let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth){
                        const xDepthOffset = inputBatchOffset + xDepth * strides[1];
                        for(let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight){
                            const xRowOffset = xDepthOffset + xRow * strides[2];
                            for(let xCol = xColMin; xCol < xColMax; xCol += dilationWidth){
                                const xColOffset = xRowOffset + xCol * strides[3];
                                const pixel = xValues[xColOffset + channel];
                                if (poolType === "max" && pixel > minMaxValue) minMaxValue = pixel;
                                else if (poolType === "avg") {
                                    avgValue += pixel;
                                    count++;
                                }
                                if (isNaN(minMaxValue)) break;
                            }
                            if (isNaN(minMaxValue)) break;
                        }
                        if (isNaN(minMaxValue)) break;
                    }
                    const outputOffset = outputColOffset + channel;
                    outputVals[outputOffset] = poolType === "avg" ? avgValue / Math.max(count, 1) : minMaxValue;
                }
            }
        }
    }
    return output;
}
function maxPool3dPositions(xBuf, convInfo) {
    const maxPositions = (0, _tfjsCore.buffer)(convInfo.outShape, "int32");
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = convInfo.padInfo.front;
    const padTop = convInfo.padInfo.top;
    const padLeft = convInfo.padInfo.left;
    for(let batch = 0; batch < convInfo.batchSize; ++batch){
        for(let channel = 0; channel < convInfo.inChannels; ++channel)for(let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth){
            const xDepthCorner = yDepth * strideDepth - padFront;
            let xDepthMin = xDepthCorner;
            while(xDepthMin < 0)xDepthMin += dilationDepth;
            const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
            for(let yRow = 0; yRow < convInfo.outHeight; ++yRow){
                const xRowCorner = yRow * strideHeight - padTop;
                let xRowMin = xRowCorner;
                while(xRowMin < 0)xRowMin += dilationHeight;
                const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
                for(let yCol = 0; yCol < convInfo.outWidth; ++yCol){
                    const xColCorner = yCol * strideWidth - padLeft;
                    let xColMin = xColCorner;
                    while(xColMin < 0)xColMin += dilationWidth;
                    const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
                    // Shader code begins
                    let maxValue = Number.NEGATIVE_INFINITY;
                    let maxPosition = -1;
                    for(let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth){
                        const wDepth = xDepth - xDepthCorner;
                        for(let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight){
                            const wRow = xRow - xRowCorner;
                            for(let xCol = xColMin; xCol < xColMax; xCol += dilationWidth){
                                const wCol = xCol - xColCorner;
                                const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);
                                if (pixel >= maxValue) {
                                    maxValue = pixel;
                                    maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;
                                }
                            }
                        }
                    }
                    maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);
                }
            }
        }
    }
    return maxPositions;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eu5qc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool3D", ()=>avgPool3D);
parcelHelpers.export(exports, "avgPool3DConfig", ()=>avgPool3DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _poolUtils = require("../utils/pool_utils");
function avgPool3D(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { filterSize, strides, pad, dimRoundingMode, dataFormat } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "avgPool3d");
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(x.shape, filterSize, strides, 1 /* dilations */ , pad, dimRoundingMode, dataFormat);
    const xValues = backend.data.get(x.dataId).values;
    const outBuf = (0, _poolUtils.pool3d)(xValues, x.shape, x.dtype, (0, _tfjsCore.util).computeStrides(x.shape), convInfo, "avg");
    return backend.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
}
const avgPool3DConfig = {
    kernelName: (0, _tfjsCore.AvgPool3D),
    backendName: "cpu",
    kernelFunc: avgPool3D
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/pool_utils":"fzyhw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iGPNS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPool3DGrad", ()=>avgPool3DGrad);
parcelHelpers.export(exports, "avgPool3DGradConfig", ()=>avgPool3DGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function avgPool3DGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input } = inputs;
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        input
    ], "avgPool3DGrad");
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(input.shape, filterSize, strides, 1 /* dilations */ , pad, dimRoundingMode);
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const filterDepth = convInfo.filterDepth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = (0, _tfjsCore.buffer)(input.shape, "float32");
    const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
    const dyBuf = backend.bufferSync(dy);
    for(let batch = 0; batch < convInfo.batchSize; ++batch){
        for(let channel = 0; channel < convInfo.inChannels; ++channel)for(let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth){
            for(let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow)for(let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol){
                // Shader code begins.
                const dyDepthCorner = dxDepth - padFront;
                const dyRowCorner = dxRow - padTop;
                const dyColCorner = dxCol - padLeft;
                let dotProd = 0;
                for(let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth){
                    const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
                    if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) continue;
                    for(let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight){
                        const dyRow = (dyRowCorner + wRow) / strideHeight;
                        if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) continue;
                        for(let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth){
                            const dyCol = (dyColCorner + wCol) / strideWidth;
                            if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) continue;
                            const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
                            dotProd += pixel;
                        }
                    }
                }
                dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
const avgPool3DGradConfig = {
    kernelName: (0, _tfjsCore.AvgPool3DGrad),
    backendName: "cpu",
    kernelFunc: avgPool3DGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kCJ3l":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "avgPoolGrad", ()=>avgPoolGrad);
parcelHelpers.export(exports, "avgPoolGradConfig", ()=>avgPoolGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function avgPoolGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input } = inputs;
    const x = input;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        input
    ], "avgPoolGrad");
    const { filterSize, strides, pad } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */ , pad);
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = (0, _tfjsCore.buffer)(x.shape, "float32");
    const avgMultiplier = 1 / (filterHeight * filterWidth);
    const dyData = backend.data.get(dy.dataId).values;
    const dyBuf = (0, _tfjsCore.buffer)(dy.shape, "float32", dyData);
    for(let b = 0; b < convInfo.batchSize; ++b)for(let d = 0; d < convInfo.inChannels; ++d){
        for(let dxR = 0; dxR < convInfo.inHeight; ++dxR)for(let dxC = 0; dxC < convInfo.inWidth; ++dxC){
            // Shader code begins.
            const dyRCorner = dxR - padTop;
            const dyCCorner = dxC - padLeft;
            let dotProd = 0;
            for(let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight){
                const dyR = (dyRCorner + wR) / strideHeight;
                if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) continue;
                for(let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth){
                    const dyC = (dyCCorner + wC) / strideWidth;
                    if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) continue;
                    const pixel = dyBuf.get(b, dyR, dyC, d);
                    dotProd += pixel;
                }
            }
            dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
const avgPoolGradConfig = {
    kernelName: (0, _tfjsCore.AvgPoolGrad),
    backendName: "cpu",
    kernelFunc: avgPoolGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a3KQN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchNorm", ()=>batchNorm);
parcelHelpers.export(exports, "batchNormConfig", ()=>batchNormConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function batchNorm(args) {
    const { inputs, backend, attrs } = args;
    const { x, scale, offset, mean, variance } = inputs;
    (0, _tfjsCore.util).assert(mean.shape.length === variance.shape.length, ()=>"Batch normalization gradient requires mean and variance to have equal ranks.");
    (0, _tfjsCore.util).assert(offset == null || mean.shape.length === offset.shape.length, ()=>"Batch normalization gradient requires mean and offset to have equal ranks.");
    (0, _tfjsCore.util).assert(scale == null || mean.shape.length === scale.shape.length, ()=>"Batch normalization gradient requires mean and scale to have equal ranks.");
    (0, _cpuUtil.assertNotComplex)([
        x,
        mean,
        variance,
        scale,
        offset
    ], "batchNorm");
    let { varianceEpsilon } = attrs;
    if (varianceEpsilon == null) varianceEpsilon = 0.001;
    const xVals = backend.data.get(x.dataId).values;
    const mVals = backend.data.get(mean.dataId).values;
    const varVals = backend.data.get(variance.dataId).values;
    const sVals = scale ? backend.data.get(scale.dataId).values : new Float32Array([
        1
    ]);
    const offVals = offset ? backend.data.get(offset.dataId).values : new Float32Array([
        0
    ]);
    const outVals = new Float32Array(xVals.length);
    const offValsLength = offVals.length;
    const sValsLength = sVals.length;
    const varValsLength = varVals.length;
    const mValsLength = mVals.length;
    let offi = 0;
    let mi = 0;
    let si = 0;
    let vi = 0;
    for(let i = 0; i < xVals.length; ++i){
        outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
        if (offi >= offValsLength) offi = 0;
        if (mi >= mValsLength) mi = 0;
        if (si >= sValsLength) si = 0;
        if (vi >= varValsLength) vi = 0;
    }
    return backend.makeTensorInfo(x.shape, x.dtype, outVals);
}
const batchNormConfig = {
    kernelName: (0, _tfjsCore.FusedBatchNorm),
    backendName: "cpu",
    kernelFunc: batchNorm
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7Jug2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "batchToSpaceND", ()=>batchToSpaceND);
parcelHelpers.export(exports, "batchToSpaceNDConfig", ()=>batchToSpaceNDConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _reshape = require("./Reshape");
var _slice = require("./Slice");
var _transpose = require("./Transpose");
function batchToSpaceND(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { blockShape, crops } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x
    ], "batchToSpaceND");
    const prod = blockShape.reduce((a, b)=>a * b);
    const reshaped = (0, _tfjsCore.backend_util).getReshaped(x.shape, blockShape, prod);
    const permuted = (0, _tfjsCore.backend_util).getPermuted(reshaped.length, blockShape.length);
    const reshapedPermuted = (0, _tfjsCore.backend_util).getReshapedPermuted(x.shape, blockShape, prod);
    const sliceBeginCoords = (0, _tfjsCore.backend_util).getSliceBeginCoords(crops, blockShape.length);
    const sliceSize = (0, _tfjsCore.backend_util).getSliceSize(reshapedPermuted, crops, blockShape.length);
    const xReshaped = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: reshaped
        }
    });
    const xTransposed = (0, _transpose.transpose)({
        inputs: {
            x: xReshaped
        },
        backend,
        attrs: {
            perm: permuted
        }
    });
    const xTransposedReshaped = (0, _reshape.reshape)({
        inputs: {
            x: xTransposed
        },
        backend,
        attrs: {
            shape: reshapedPermuted
        }
    });
    const result = (0, _slice.slice)({
        inputs: {
            x: xTransposedReshaped
        },
        backend,
        attrs: {
            begin: sliceBeginCoords,
            size: sliceSize
        }
    });
    backend.disposeIntermediateTensorInfo(xReshaped);
    backend.disposeIntermediateTensorInfo(xTransposed);
    backend.disposeIntermediateTensorInfo(xTransposedReshaped);
    return result;
}
const batchToSpaceNDConfig = {
    kernelName: (0, _tfjsCore.BatchToSpaceND),
    backendName: "cpu",
    kernelFunc: batchToSpaceND
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Reshape":"9350i","./Slice":"9L9GN","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hE7iz":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "bincount", ()=>bincount);
parcelHelpers.export(exports, "bincountConfig", ()=>bincountConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _bincountImpl = require("./Bincount_impl");
function bincount(args) {
    const { inputs, backend, attrs } = args;
    const { x, weights } = inputs;
    const { size } = attrs;
    const xVals = backend.data.get(x.dataId).values;
    const weightsVals = backend.data.get(weights.dataId).values;
    const outVals = (0, _bincountImpl.bincountImpl)(xVals, weightsVals, weights.dtype, weights.shape, size);
    return backend.makeTensorInfo([
        size
    ], weights.dtype, outVals);
}
const bincountConfig = {
    kernelName: (0, _tfjsCore.Bincount),
    backendName: "cpu",
    kernelFunc: bincount
};

},{"@tensorflow/tfjs-core":"21Ftl","./Bincount_impl":"4EF2S","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4GYx6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "broadcastArgs", ()=>broadcastArgs);
parcelHelpers.export(exports, "broadcastArgsConfig", ()=>broadcastArgsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function broadcastArgs(args) {
    const { inputs, backend } = args;
    const { s0, s1 } = inputs;
    const s0Vals = backend.data.get(s0.dataId).values;
    const s1Vals = backend.data.get(s1.dataId).values;
    const broadcastShape = (0, _tfjsCore.backend_util).assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
    return backend.makeTensorInfo([
        broadcastShape.length
    ], "int32", Int32Array.from(broadcastShape));
}
const broadcastArgsConfig = {
    kernelName: (0, _tfjsCore.BroadcastArgs),
    backendName: "cpu",
    kernelFunc: broadcastArgs
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ck0lb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "clipByValue", ()=>clipByValue);
parcelHelpers.export(exports, "clipByValueConfig", ()=>clipByValueConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const clipByValue = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.ClipByValue), (xi, attrs)=>{
    const clipAttrs = attrs;
    if (xi > clipAttrs.clipValueMax) return clipAttrs.clipValueMax;
    return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;
});
const clipByValueConfig = {
    kernelName: (0, _tfjsCore.ClipByValue),
    backendName: "cpu",
    kernelFunc: clipByValue
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kThBM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "complexAbs", ()=>complexAbs);
parcelHelpers.export(exports, "complexAbsConfig", ()=>complexAbsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
const complexAbs = (args)=>{
    const { x } = args.inputs;
    const cpuBackend = args.backend;
    const resultValues = new Float32Array((0, _tfjsCore.util).sizeFromShape(x.shape));
    const complexVals = cpuBackend.data.get(x.dataId);
    const real = complexVals.complexTensorInfos.real;
    const imag = complexVals.complexTensorInfos.imag;
    const realVals = cpuBackend.data.get(real.dataId).values;
    const imagVals = cpuBackend.data.get(imag.dataId).values;
    for(let i = 0; i < realVals.length; i++){
        const real = realVals[i];
        const imag = imagVals[i];
        resultValues[i] = Math.hypot(real, imag);
    }
    return cpuBackend.makeOutput(resultValues, x.shape, "float32");
};
const complexAbsConfig = {
    kernelName: (0, _tfjsCore.ComplexAbs),
    backendName: "cpu",
    kernelFunc: complexAbs
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iYEAT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "concat", ()=>concat);
parcelHelpers.export(exports, "concatConfig", ()=>concatConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _complex = require("./Complex");
var _concatImpl = require("./Concat_impl");
var _identity = require("./Identity");
var _imag = require("./Imag");
var _real = require("./Real");
var _reshape = require("./Reshape");
function concat(args) {
    const { inputs, backend, attrs } = args;
    const { axis } = attrs;
    const $axis = (0, _tfjsCore.util).parseAxisParam(axis, inputs[0].shape)[0];
    const shapes = inputs.map((t)=>t.shape);
    (0, _tfjsCore.backend_util).assertParamsConsistent(shapes, $axis);
    let outShape = (0, _tfjsCore.backend_util).computeOutShape(inputs.map((t)=>t.shape), $axis);
    if ((0, _tfjsCore.util).sizeFromShape(outShape) === 0) return backend.makeTensorInfo(outShape, inputs[0].dtype, []);
    // Keep only non-empty tensors (ignore tensors with 0 in their shape).
    const $inputs = inputs.filter((t)=>(0, _tfjsCore.util).sizeFromShape(t.shape) > 0);
    if ($inputs.length === 1) return (0, _identity.identity)({
        inputs: {
            x: $inputs[0]
        },
        backend
    });
    if ($inputs[0].dtype === "complex64") {
        const reals = $inputs.map((t)=>(0, _real.real)({
                inputs: {
                    input: t
                },
                backend
            }));
        const imags = $inputs.map((t)=>(0, _imag.imag)({
                inputs: {
                    input: t
                },
                backend
            }));
        const realConcated = concat({
            inputs: reals,
            backend,
            attrs: {
                axis: $axis
            }
        });
        const imagConcated = concat({
            inputs: imags,
            backend,
            attrs: {
                axis: $axis
            }
        });
        const result = (0, _complex.complex)({
            inputs: {
                real: realConcated,
                imag: imagConcated
            },
            backend
        });
        reals.forEach((r)=>backend.disposeIntermediateTensorInfo(r));
        imags.forEach((i)=>backend.disposeIntermediateTensorInfo(i));
        backend.disposeIntermediateTensorInfo(realConcated);
        backend.disposeIntermediateTensorInfo(imagConcated);
        return result;
    }
    // Any concat of n-dimensional tensors across any axis can be reduced to
    // a concatenation of two-dimensional tensors across the axis 1 by first
    // partitioning the axes of the original tensors into those less than the
    // axis to be concatenated and the rest. Then reshape the tensors
    // into a two-dimensional tensor by collapsing these two sets of axes and
    // concatenate the resulting matrices across the axis 1, finally reshaping
    // the result to have the proper shape.
    const inputs2D = $inputs.map((t)=>{
        const innerSize = (0, _tfjsCore.util).sizeFromShape(t.shape.slice($axis));
        const shape = [
            -1,
            innerSize
        ];
        return (0, _reshape.reshape)({
            inputs: {
                x: t
            },
            backend,
            attrs: {
                shape
            }
        });
    });
    const inputsValShapes = inputs2D.map((t)=>{
        return {
            vals: backend.data.get(t.dataId).values,
            shape: t.shape
        };
    });
    // Concats 2d tensors along axis=1.
    outShape = (0, _tfjsCore.backend_util).computeOutShape(inputs2D.map((t)=>t.shape), 1 /* axis */ );
    const simplyConcat = inputs2D[0].shape[0] === 1;
    const outVals = (0, _concatImpl.concatImpl)(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
    const finalOutShape = (0, _tfjsCore.backend_util).computeOutShape($inputs.map((t)=>t.shape), $axis);
    const outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
    inputs2D.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return outInfo;
}
const concatConfig = {
    kernelName: (0, _tfjsCore.Concat),
    backendName: "cpu",
    kernelFunc: concat
};

},{"@tensorflow/tfjs-core":"21Ftl","./Complex":"3DPmh","./Concat_impl":"50NO7","./Identity":"btREJ","./Imag":"af3Tx","./Real":"eZ7aJ","./Reshape":"9350i","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"af3Tx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "imag", ()=>imag);
parcelHelpers.export(exports, "imagConfig", ()=>imagConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function imag(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    const imag = backend.data.get(input.dataId).complexTensorInfos.imag;
    const imagVal = backend.data.get(imag.dataId).values;
    // When complex tensor is disposed, its underlying parts will be disposed too.
    // Make new tensor out of the imag value of the complex. This makes sure the
    // value is still accessible even if complex tensor is disposed.
    return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal);
}
const imagConfig = {
    kernelName: (0, _tfjsCore.Imag),
    backendName: "cpu",
    kernelFunc: imag
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4bj3X":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2D", ()=>conv2D);
parcelHelpers.export(exports, "conv2DConfig", ()=>conv2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function conv2D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dataFormat, dilations, dimRoundingMode } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        filter
    ], "conv2d");
    const $dataFormat = (0, _tfjsCore.backend_util).convertConv2DDataFormat(dataFormat);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const padLeft = convInfo.padInfo.left;
    const padTop = convInfo.padInfo.top;
    const isChannelsLast = convInfo.dataFormat === "channelsLast";
    const y = new (0, _tfjsCore.TensorBuffer)(convInfo.outShape, x.dtype);
    const xStrides = (0, _tfjsCore.util).computeStrides(x.shape);
    const filterStrides = (0, _tfjsCore.util).computeStrides(filter.shape);
    const xBatchStride = xStrides[0];
    const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];
    const xColStride = isChannelsLast ? xStrides[2] : 1;
    const xChannelStride = isChannelsLast ? 1 : xStrides[1];
    const yBatchStride = y.strides[0];
    const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];
    const yColStride = isChannelsLast ? y.strides[2] : 1;
    const yChannelStride = isChannelsLast ? 1 : y.strides[1];
    const xVals = backend.data.get(x.dataId).values;
    const wVals = backend.data.get(filter.dataId).values;
    const yVals = y.values;
    for(let b = 0; b < convInfo.batchSize; ++b){
        const xOffset1 = b * xBatchStride;
        const yOffset1 = b * yBatchStride;
        for(let yR = 0; yR < convInfo.outHeight; ++yR){
            const yOffset2 = yOffset1 + yR * yRowStride;
            const xRCorner = yR * convInfo.strideHeight - padTop;
            for(let wR = 0; wR < filterHeight; ++wR){
                const xR = xRCorner + wR * dilationHeight;
                if (xR < 0 || xR >= convInfo.inHeight) continue;
                const wOffset1 = wR * filterStrides[0];
                const xOffset2 = xOffset1 + xR * xRowStride;
                for(let yC = 0; yC < convInfo.outWidth; ++yC){
                    const yOffset3 = yOffset2 + yC * yColStride;
                    const xCCorner = yC * convInfo.strideWidth - padLeft;
                    for(let wC = 0; wC < filterWidth; ++wC){
                        const xC = xCCorner + wC * dilationWidth;
                        if (xC < 0 || xC >= convInfo.inWidth) continue;
                        const wOffset2 = wOffset1 + wC * filterStrides[1];
                        const xOffset3 = xOffset2 + xC * xColStride;
                        let wOffset3 = wOffset2;
                        for(let d1 = 0; d1 < convInfo.inChannels; ++d1){
                            const xVal = xVals[xOffset3 + d1 * xChannelStride];
                            for(let d2 = 0; d2 < convInfo.outChannels; ++d2)yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];
                            wOffset3 += convInfo.outChannels;
                        }
                    }
                }
            }
        }
    }
    return backend.makeTensorInfo(y.shape, y.dtype, yVals);
}
const conv2DConfig = {
    kernelName: (0, _tfjsCore.Conv2D),
    backendName: "cpu",
    kernelFunc: conv2D
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jHKF1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2DBackpropFilter", ()=>conv2DBackpropFilter);
parcelHelpers.export(exports, "conv2DBackpropFilterConfig", ()=>conv2DBackpropFilterConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function conv2DBackpropFilter(args) {
    const { inputs, backend, attrs } = args;
    const { x, dy } = inputs;
    const { strides, pad, dataFormat, dimRoundingMode, filterShape } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        dy
    ], "conv2dBackpropFilter");
    const $dataFormat = (0, _tfjsCore.backend_util).convertConv2DDataFormat(dataFormat);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */ , pad, dimRoundingMode, false, $dataFormat);
    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
    const isChannelsLast = convInfo.dataFormat === "channelsLast";
    const dW = new (0, _tfjsCore.TensorBuffer)(convInfo.filterShape, "float32");
    const leftPad = convInfo.padInfo.left;
    const topPad = convInfo.padInfo.top;
    const xVals = backend.data.get(x.dataId).values;
    const dyVals = backend.data.get(dy.dataId).values;
    const xBuf = new (0, _tfjsCore.TensorBuffer)(x.shape, x.dtype, xVals);
    const dyBuf = new (0, _tfjsCore.TensorBuffer)(dy.shape, dy.dtype, dyVals);
    for(let wR = 0; wR < filterHeight; ++wR){
        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
        for(let wC = 0; wC < filterWidth; ++wC){
            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
            for(let d1 = 0; d1 < convInfo.inChannels; ++d1)for(let d2 = 0; d2 < convInfo.outChannels; ++d2){
                let dotProd = 0;
                for(let b = 0; b < convInfo.batchSize; ++b)for(let yR = yRMin; yR < yRMax; ++yR){
                    const xR = wR + yR * strideHeight - topPad;
                    for(let yC = yCMin; yC < yCMax; ++yC){
                        const xC = wC + yC * strideWidth - leftPad;
                        if (isChannelsLast) dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
                        else dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);
                    }
                }
                dW.set(dotProd, wR, wC, d1, d2);
            }
        }
    }
    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);
}
const conv2DBackpropFilterConfig = {
    kernelName: (0, _tfjsCore.Conv2DBackpropFilter),
    backendName: "cpu",
    kernelFunc: conv2DBackpropFilter
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fBpei":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv2DBackpropInput", ()=>conv2DBackpropInput);
parcelHelpers.export(exports, "conv2DBackpropInputConfig", ()=>conv2DBackpropInputConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function conv2DBackpropInput(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { inputShape, strides, pad, dataFormat, dimRoundingMode } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        filter
    ], "conv2dBackpropInput");
    const filterStrides = (0, _tfjsCore.util).computeStrides(filter.shape);
    const dyStrides = (0, _tfjsCore.util).computeStrides(dy.shape);
    let $dataFormat = (0, _tfjsCore.backend_util).convertConv2DDataFormat(dataFormat);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */ , pad, dimRoundingMode, false, $dataFormat);
    const dx = new (0, _tfjsCore.TensorBuffer)(convInfo.inShape, "float32");
    const dxValues = dx.values;
    const dyValues = backend.data.get(dy.dataId).values;
    const fltValues = backend.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2] = filterStrides;
    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
    $dataFormat = convInfo.dataFormat;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    const isChannelsLast = $dataFormat === "channelsLast";
    const xBatchStride = dx.strides[0];
    const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
    const xColStride = isChannelsLast ? dx.strides[2] : 1;
    const xChannelStride = isChannelsLast ? 1 : dx.strides[1];
    const yBatchStride = dyStrides[0];
    const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
    const yColStride = isChannelsLast ? dyStrides[2] : 1;
    const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
    for(let b = 0; b < batchSize; ++b){
        for(let d1 = 0; d1 < inChannels; ++d1)for(let xR = 0; xR < inHeight; ++xR){
            const xRCorner = xR - topPad;
            const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
            const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
            for(let xC = 0; xC < inWidth; ++xC){
                const xCCorner = xC - leftPad;
                const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
                const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
                let dotProd = 0;
                for(let yR = xRMin; yR < yRMax; ++yR){
                    const wR = yR * strideHeight - xRCorner;
                    for(let yC = xCMin; yC < yCMax; ++yC){
                        const wC = yC * strideWidth - xCCorner;
                        const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;
                        const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
                        for(let d2 = 0; d2 < outChannels; ++d2){
                            const pixel = dyValues[dyOffset + yChannelStride * d2];
                            const weight = fltValues[fltOffset + d2];
                            dotProd += pixel * weight;
                        }
                    }
                }
                const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;
                dxValues[dxOffset] = dotProd;
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
const conv2DBackpropInputConfig = {
    kernelName: (0, _tfjsCore.Conv2DBackpropInput),
    backendName: "cpu",
    kernelFunc: conv2DBackpropInput
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iJ4dh":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3D", ()=>conv3D);
parcelHelpers.export(exports, "conv3DConfig", ()=>conv3DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function conv3D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dilations } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        filter
    ], "conv3d");
    const convInfo = (0, _tfjsCore.backend_util).computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);
    const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;
    const padFront = padInfo.front;
    const padLeft = padInfo.left;
    const padTop = padInfo.top;
    const y = new (0, _tfjsCore.TensorBuffer)(convInfo.outShape, x.dtype);
    const xVals = backend.data.get(x.dataId).values;
    const wVals = backend.data.get(filter.dataId).values;
    const yVals = y.values;
    const xStrides = (0, _tfjsCore.util).computeStrides(x.shape);
    const filterStrides = (0, _tfjsCore.util).computeStrides(filter.shape);
    for(let b = 0; b < convInfo.batchSize; ++b){
        const xOffset1 = b * xStrides[0];
        const yOffset1 = b * y.strides[0];
        for(let yF = 0; yF < convInfo.outDepth; ++yF){
            const yOffset2 = yOffset1 + yF * y.strides[1];
            const xFCorner = yF * convInfo.strideDepth - padFront;
            for(let wF = 0; wF < filterDepth; ++wF){
                const xF = xFCorner + wF * dilationDepth;
                if (xF < 0 || xF >= convInfo.inDepth) continue;
                const wOffset1 = wF * filterStrides[0];
                const xOffset2 = xOffset1 + xF * xStrides[1];
                for(let yR = 0; yR < convInfo.outHeight; ++yR){
                    const yOffset3 = yOffset2 + yR * y.strides[2];
                    const xRCorner = yR * convInfo.strideHeight - padTop;
                    for(let wR = 0; wR < filterHeight; ++wR){
                        const xR = xRCorner + wR * dilationHeight;
                        if (xR < 0 || xR >= convInfo.inHeight) continue;
                        const wOffset2 = wOffset1 + wR * filterStrides[1];
                        const xOffset3 = xOffset2 + xR * xStrides[2];
                        for(let yC = 0; yC < convInfo.outWidth; ++yC){
                            const yOffset4 = yOffset3 + yC * convInfo.outChannels;
                            const xCCorner = yC * convInfo.strideWidth - padLeft;
                            for(let wC = 0; wC < filterWidth; ++wC){
                                const xC = xCCorner + wC * dilationWidth;
                                if (xC < 0 || xC >= convInfo.inWidth) continue;
                                const wOffset3 = wOffset2 + wC * filterStrides[2];
                                const xOffset4 = xOffset3 + xC * convInfo.inChannels;
                                let wOffset4 = wOffset3;
                                for(let d1 = 0; d1 < convInfo.inChannels; ++d1){
                                    const xVal = xVals[xOffset4 + d1];
                                    for(let d2 = 0; d2 < convInfo.outChannels; ++d2)yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
                                    wOffset4 += convInfo.outChannels;
                                }
                            }
                        }
                    }
                }
            }
        }
    }
    return backend.makeTensorInfo(y.shape, y.dtype, y.values);
}
const conv3DConfig = {
    kernelName: (0, _tfjsCore.Conv3D),
    backendName: "cpu",
    kernelFunc: conv3D
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g7cYG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3DBackpropFilterV2", ()=>conv3DBackpropFilterV2);
parcelHelpers.export(exports, "conv3DBackpropFilterV2Config", ()=>conv3DBackpropFilterV2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function conv3DBackpropFilterV2(args) {
    const { inputs, backend, attrs } = args;
    const { x, dy } = inputs;
    const { strides, pad, filterShape } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        dy
    ], "conv3dBackpropFilterV2");
    const xStrides = (0, _tfjsCore.util).computeStrides(x.shape);
    const dyStrides = (0, _tfjsCore.util).computeStrides(dy.shape);
    const convInfo = (0, _tfjsCore.backend_util).computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */ , pad);
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const filterDepth = convInfo.filterDepth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dw = new (0, _tfjsCore.TensorBuffer)(convInfo.filterShape, "float32");
    const dwValues = dw.values;
    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;
    const dyValues = backend.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
    const xValues = backend.data.get(x.dataId).values;
    const [xS0, xS1, xS2, xS3] = xStrides;
    const frontPad = convInfo.padInfo.front;
    const leftPad = convInfo.padInfo.left;
    const topPad = convInfo.padInfo.top;
    for(let wF = 0; wF < filterDepth; ++wF){
        const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
        const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
        const wOffset1 = wF * dwS0;
        for(let wR = 0; wR < filterHeight; ++wR){
            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
            const wOffset2 = wR * dwS1 + wOffset1;
            for(let wC = 0; wC < filterWidth; ++wC){
                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
                const wOffset3 = wC * dwS2 + wOffset2;
                for(let d1 = 0; d1 < convInfo.inChannels; ++d1){
                    const wOffset4 = d1 * dwS3 + wOffset3;
                    for(let d2 = 0; d2 < convInfo.outChannels; ++d2){
                        let dotProd = 0;
                        for(let b = 0; b < convInfo.batchSize; ++b){
                            const xOffset1 = b * xS0;
                            const yOffset1 = b * dyS0;
                            for(let yF = yFMin; yF < yFMax; ++yF){
                                const xF = wF + yF * strideDepth - frontPad;
                                const xOffset2 = xF * xS1 + xOffset1;
                                const yOffset2 = yF * dyS1 + yOffset1;
                                for(let yR = yRMin; yR < yRMax; ++yR){
                                    const xR = wR + yR * strideHeight - topPad;
                                    const xOffset3 = xR * xS2 + xOffset2;
                                    const yOffset3 = yR * dyS2 + yOffset2;
                                    for(let yC = yCMin; yC < yCMax; ++yC){
                                        const xC = wC + yC * strideWidth - leftPad;
                                        const xOffset4 = xC * xS3 + xOffset3;
                                        const yOffset4 = yC * dyS3 + yOffset3;
                                        dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
                                    }
                                }
                            }
                        }
                        dwValues[wOffset4 + d2] = dotProd;
                    }
                }
            }
        }
    }
    return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);
}
const conv3DBackpropFilterV2Config = {
    kernelName: (0, _tfjsCore.Conv3DBackpropFilterV2),
    backendName: "cpu",
    kernelFunc: conv3DBackpropFilterV2
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l6kPr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "conv3DBackpropInputV2", ()=>conv3DBackpropInputV2);
parcelHelpers.export(exports, "conv3DBackpropInputV2Config", ()=>conv3DBackpropInputV2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function conv3DBackpropInputV2(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { pad, strides, inputShape } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        dy
    ], "conv3dBackpropInputV2");
    const dyStrides = (0, _tfjsCore.util).computeStrides(dy.shape);
    const filterStrides = (0, _tfjsCore.util).computeStrides(filter.shape);
    const convInfo = (0, _tfjsCore.backend_util).computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */ , pad);
    const dx = new (0, _tfjsCore.TensorBuffer)(convInfo.inShape, "float32");
    const dxValues = dx.values;
    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;
    const dyValues = backend.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
    const fltValues = backend.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2, fltS3] = filterStrides;
    const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;
    const frontPad = filterDepth - 1 - convInfo.padInfo.front;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    for(let b = 0; b < batchSize; ++b){
        for(let d1 = 0; d1 < inChannels; ++d1)// Frames of depth
        for(let xF = 0; xF < inDepth; ++xF){
            const xFCorner = xF - frontPad;
            const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
            const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
            // Rows as per standard 2d matrix notation
            for(let xR = 0; xR < inHeight; ++xR){
                const xRCorner = xR - topPad;
                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
                // Columns as per standard 2d matrix notation
                for(let xC = 0; xC < inWidth; ++xC){
                    const xCCorner = xC - leftPad;
                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
                    let dotProd = 0;
                    for(let yF = xFMin; yF < yFMax; ++yF){
                        const wF = yF * strideDepth - xFCorner;
                        for(let yR = xRMin; yR < yRMax; ++yR){
                            const wR = yR * strideHeight - xRCorner;
                            for(let yC = xCMin; yC < yCMax; ++yC){
                                const wC = yC * strideWidth - xCCorner;
                                const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
                                const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
                                for(let d2 = 0; d2 < outChannels; ++d2){
                                    const pixel = dyValues[dyOffset + d2];
                                    const weight = fltValues[fltOffset + d2];
                                    dotProd += pixel * weight;
                                }
                            }
                        }
                    }
                    dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;
                }
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
const conv3DBackpropInputV2Config = {
    kernelName: (0, _tfjsCore.Conv3DBackpropInputV2),
    backendName: "cpu",
    kernelFunc: conv3DBackpropInputV2
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hkcOn":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cos", ()=>cos);
parcelHelpers.export(exports, "cosConfig", ()=>cosConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const cos = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Cos), (xi)=>Math.cos(xi));
const cosConfig = {
    kernelName: (0, _tfjsCore.Cos),
    backendName: "cpu",
    kernelFunc: cos
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eg9u0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cosh", ()=>cosh);
parcelHelpers.export(exports, "coshConfig", ()=>coshConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const cosh = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Cosh), (xi)=>Math.cosh(xi));
const coshConfig = {
    kernelName: (0, _tfjsCore.Cosh),
    backendName: "cpu",
    kernelFunc: cosh
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8K1SK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cropAndResize", ()=>cropAndResize);
parcelHelpers.export(exports, "cropAndResizeConfig", ()=>cropAndResizeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function cropAndResize(args) {
    const { inputs, backend, attrs } = args;
    const { image, boxes, boxInd } = inputs;
    const { cropSize, method, extrapolationValue } = attrs;
    const [batch, imageHeight, imageWidth, numChannels] = image.shape;
    const numBoxes = boxes.shape[0];
    const [cropHeight, cropWidth] = cropSize;
    const output = (0, _tfjsCore.buffer)([
        numBoxes,
        cropHeight,
        cropWidth,
        numChannels
    ], "float32");
    const boxVals = backend.data.get(boxes.dataId).values;
    const boxIndVals = backend.data.get(boxInd.dataId).values;
    const imageVals = backend.data.get(image.dataId).values;
    const inStride = (0, _tfjsCore.util).computeStrides(image.shape); // to calculate flat indexes into image
    const outStride = (0, _tfjsCore.util).computeStrides(output.shape); // to calculate flat indexes into output
    // Reference implementation
    // tslint:disable-next-line:max-line-length
    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc
    for(let b = 0; b < numBoxes; b++){
        const startInd = b * 4;
        const y1 = boxVals[startInd];
        const x1 = boxVals[startInd + 1];
        const y2 = boxVals[startInd + 2];
        const x2 = boxVals[startInd + 3];
        const bInd = boxIndVals[b];
        if (bInd >= batch) continue;
        const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
        const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
        for(let y = 0; y < cropHeight; y++){
            const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);
            if (yInd < 0 || yInd > imageHeight - 1) {
                for(let x = 0; x < cropWidth; x++)for(let c = 0; c < numChannels; c++){
                    const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
                    output.values[ind] = extrapolationValue;
                }
                continue;
            }
            if (method === "bilinear") {
                const topInd = Math.floor(yInd);
                const bottomInd = Math.ceil(yInd);
                const yLerp = yInd - topInd;
                for(let x = 0; x < cropWidth; x++){
                    const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
                    if (xInd < 0 || xInd > imageWidth - 1) {
                        for(let c = 0; c < numChannels; c++){
                            const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
                            output.values[ind] = extrapolationValue;
                        }
                        continue;
                    }
                    const leftInd = Math.floor(xInd);
                    const rightInd = Math.ceil(xInd);
                    const xLerp = xInd - leftInd;
                    for(let c = 0; c < numChannels; c++){
                        let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
                        const topLeft = imageVals[ind];
                        ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
                        const topRight = imageVals[ind];
                        ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
                        const bottomLeft = imageVals[ind];
                        ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
                        const bottomRight = imageVals[ind];
                        const top = topLeft + (topRight - topLeft) * xLerp;
                        const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
                        ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
                        output.values[ind] = top + (bottom - top) * yLerp;
                    }
                }
            } else for(let x = 0; x < cropWidth; ++x){
                const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
                if (xInd < 0 || xInd > imageWidth - 1) {
                    for(let c = 0; c < numChannels; c++){
                        const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
                        output.values[ind] = extrapolationValue;
                    }
                    continue;
                }
                const closestX = Math.round(xInd);
                const closestY = Math.round(yInd);
                for(let c = 0; c < numChannels; c++){
                    const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];
                    const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
                    output.values[outInd] = imageVals[inInd];
                }
            }
        }
    }
    return backend.makeTensorInfo(output.shape, output.dtype, output.values);
}
const cropAndResizeConfig = {
    kernelName: (0, _tfjsCore.CropAndResize),
    backendName: "cpu",
    kernelFunc: cropAndResize
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bN0pp":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cumprod", ()=>cumprod);
parcelHelpers.export(exports, "cumprodConfig", ()=>cumprodConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _transpose = require("./Transpose");
function cumprod(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, exclusive, reverse } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "cumprod");
    const permutation = (0, _tfjsCore.backend_util).getAxesPermutation([
        axis
    ], x.shape.length);
    let $x = x;
    if (permutation != null) $x = (0, _transpose.transpose)({
        inputs: {
            x
        },
        backend,
        attrs: {
            perm: permutation
        }
    });
    const permutedAxis = (0, _tfjsCore.backend_util).getInnerMostAxes(1, x.shape.length)[0];
    if (permutedAxis !== $x.shape.length - 1) throw new Error(`backend.cumprod in CPU expects an inner-most ` + `axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
    const resultDtype = (0, _tfjsCore.upcastType)($x.dtype, "int32");
    const vals = (0, _tfjsCore.util).makeOnesTypedArray((0, _tfjsCore.util).sizeFromShape($x.shape), resultDtype);
    const aVals = backend.data.get($x.dataId).values;
    const finalDim = $x.shape[$x.shape.length - 1];
    const indexAdjuster = reverse ? (i, j)=>i + finalDim - j - 1 : (i, j)=>i + j;
    for(let i = 0; i < aVals.length; i += finalDim)for(let j = 0; j < finalDim; j++){
        const idx = indexAdjuster(i, j);
        if (j === 0) vals[idx] = exclusive ? 1 : aVals[idx];
        else {
            const prevIdx = indexAdjuster(i, j - 1);
            vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];
        }
    }
    const result = backend.makeTensorInfo($x.shape, resultDtype, vals);
    if (permutation != null) {
        const reversePermutation = (0, _tfjsCore.backend_util).getUndoAxesPermutation(permutation);
        const reverseTransposedResult = (0, _transpose.transpose)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                perm: reversePermutation
            }
        });
        backend.disposeIntermediateTensorInfo(result);
        backend.disposeIntermediateTensorInfo($x);
        return reverseTransposedResult;
    }
    return result;
}
const cumprodConfig = {
    kernelName: (0, _tfjsCore.Cumprod),
    backendName: "cpu",
    kernelFunc: cumprod
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8YA18":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "cumsum", ()=>cumsum);
parcelHelpers.export(exports, "cumsumConfig", ()=>cumsumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _transpose = require("./Transpose");
function cumsum(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, exclusive, reverse } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "cumsum");
    const permutation = (0, _tfjsCore.backend_util).getAxesPermutation([
        axis
    ], x.shape.length);
    let $x = x;
    if (permutation != null) $x = (0, _transpose.transpose)({
        inputs: {
            x
        },
        backend,
        attrs: {
            perm: permutation
        }
    });
    const permutedAxis = (0, _tfjsCore.backend_util).getInnerMostAxes(1, x.shape.length)[0];
    if (permutedAxis !== $x.shape.length - 1) throw new Error(`backend.cumsum in CPU expects an inner-most ` + `axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
    const resultDtype = (0, _tfjsCore.upcastType)($x.dtype, "int32");
    const vals = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape($x.shape), resultDtype);
    const aVals = backend.data.get($x.dataId).values;
    const finalDim = $x.shape[$x.shape.length - 1];
    const indexAdjuster = reverse ? (i, j)=>i + finalDim - j - 1 : (i, j)=>i + j;
    for(let i = 0; i < aVals.length; i += finalDim)for(let j = 0; j < finalDim; j++){
        const idx = indexAdjuster(i, j);
        if (j === 0) vals[idx] = exclusive ? 0 : aVals[idx];
        else {
            const prevIdx = indexAdjuster(i, j - 1);
            vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];
        }
    }
    const result = backend.makeTensorInfo($x.shape, resultDtype, vals);
    if (permutation != null) {
        const reversePermutation = (0, _tfjsCore.backend_util).getUndoAxesPermutation(permutation);
        const reverseTransposedResult = (0, _transpose.transpose)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                perm: reversePermutation
            }
        });
        backend.disposeIntermediateTensorInfo(result);
        backend.disposeIntermediateTensorInfo($x);
        return reverseTransposedResult;
    }
    return result;
}
const cumsumConfig = {
    kernelName: (0, _tfjsCore.Cumsum),
    backendName: "cpu",
    kernelFunc: cumsum
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cpFc0":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "denseBincount", ()=>denseBincount);
parcelHelpers.export(exports, "denseBincountConfig", ()=>denseBincountConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _bincountImpl = require("./Bincount_impl");
function denseBincount(args) {
    const { inputs, backend, attrs } = args;
    const { x, weights } = inputs;
    const { size, binaryOutput } = attrs;
    if (x.shape.length === 1) {
        const xVals = backend.data.get(x.dataId).values;
        const weightsVals = backend.data.get(weights.dataId).values;
        const outVals = (0, _bincountImpl.bincountImpl)(xVals, weightsVals, weights.dtype, weights.shape, size);
        return backend.makeTensorInfo([
            size
        ], weights.dtype, outVals);
    } else if (x.shape.length === 2) {
        const xBuf = backend.bufferSync(x);
        const weightsBuf = backend.bufferSync(weights);
        const outBuf = (0, _bincountImpl.bincountReduceImpl)(xBuf, weightsBuf, size, binaryOutput);
        return backend.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
    }
    throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank` + `${x.shape.length}.`);
}
const denseBincountConfig = {
    kernelName: (0, _tfjsCore.DenseBincount),
    backendName: "cpu",
    kernelFunc: denseBincount
};

},{"@tensorflow/tfjs-core":"21Ftl","./Bincount_impl":"4EF2S","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l4MfW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthToSpace", ()=>depthToSpace);
parcelHelpers.export(exports, "depthToSpaceConfig", ()=>depthToSpaceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function depthToSpace(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { blockSize, dataFormat } = attrs;
    (0, _tfjsCore.util).assert(dataFormat === "NHWC", ()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);
    const batchSize = x.shape[0];
    const inputHeight = x.shape[1];
    const inputWidth = x.shape[2];
    const inputDepth = x.shape[3];
    const outputHeight = inputHeight * blockSize;
    const outputWidth = inputWidth * blockSize;
    const outputDepth = inputDepth / (blockSize * blockSize);
    const xValues = backend.data.get(x.dataId).values;
    const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
    let outputIdx = 0;
    for(let b = 0; b < batchSize; ++b)for(let h = 0; h < outputHeight; ++h){
        const inH = Math.floor(h / blockSize);
        const offsetH = h % blockSize;
        for(let w = 0; w < outputWidth; ++w){
            const inW = Math.floor(w / blockSize);
            const offsetW = w % blockSize;
            const offsetD = (offsetH * blockSize + offsetW) * outputDepth;
            for(let d = 0; d < outputDepth; ++d){
                const inD = d + offsetD;
                const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
                result[outputIdx++] = xValues[inputIdx];
            }
        }
    }
    return backend.makeTensorInfo([
        batchSize,
        outputHeight,
        outputWidth,
        outputDepth
    ], x.dtype, result);
}
const depthToSpaceConfig = {
    kernelName: (0, _tfjsCore.DepthToSpace),
    backendName: "cpu",
    kernelFunc: depthToSpace
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2iRpa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNative", ()=>depthwiseConv2dNative);
parcelHelpers.export(exports, "depthwiseConv2dNativeConfig", ()=>depthwiseConv2dNativeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function depthwiseConv2dNative(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dilations, dimRoundingMode } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        filter
    ], "depthwiseConv2DNative");
    const xStrides = (0, _tfjsCore.util).computeStrides(x.shape);
    const filterStrides = (0, _tfjsCore.util).computeStrides(filter.shape);
    let $dilations = dilations;
    if ($dilations == null) $dilations = [
        1,
        1
    ];
    (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, $dilations), ()=>"Error in depthwiseConv2d: Either strides or dilations must be " + `1. Got strides ${strides} and dilations '${$dilations}'`);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true);
    const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;
    const padLeft = padInfo.left;
    const padTop = padInfo.top;
    const chMul = convInfo.outChannels / convInfo.inChannels;
    const y = new (0, _tfjsCore.TensorBuffer)(convInfo.outShape, x.dtype);
    const xVals = backend.data.get(x.dataId).values;
    const wVals = backend.data.get(filter.dataId).values;
    const yVals = y.values;
    for(let b = 0; b < convInfo.batchSize; ++b){
        const xOffset1 = b * xStrides[0];
        const yOffset1 = b * y.strides[0];
        for(let yR = 0; yR < convInfo.outHeight; ++yR){
            const yOffset2 = yOffset1 + yR * y.strides[1];
            const xRCorner = yR * convInfo.strideHeight - padTop;
            for(let wR = 0; wR < filterHeight; ++wR){
                const xR = xRCorner + wR * dilationHeight;
                if (xR < 0 || xR >= convInfo.inHeight) continue;
                const wOffset1 = wR * filterStrides[0];
                const xOffset2 = xOffset1 + xR * xStrides[1];
                for(let yC = 0; yC < convInfo.outWidth; ++yC){
                    const yOffset3 = yOffset2 + yC * y.strides[2];
                    const xCCorner = yC * convInfo.strideWidth - padLeft;
                    for(let wC = 0; wC < filterWidth; ++wC){
                        const xC = xCCorner + wC * dilationWidth;
                        if (xC < 0 || xC >= convInfo.inWidth) continue;
                        const wOffset2 = wOffset1 + wC * filterStrides[1];
                        const xOffset3 = xOffset2 + xC * convInfo.inChannels;
                        let yOffset4 = yOffset3;
                        let wOffset3 = wOffset2;
                        for(let d1 = 0; d1 < convInfo.inChannels; ++d1){
                            const xVal = xVals[xOffset3 + d1];
                            for(let q = 0; q < chMul; ++q)yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
                            yOffset4 += chMul;
                            wOffset3 += chMul;
                        }
                    }
                }
            }
        }
    }
    return backend.makeTensorInfo(y.shape, y.dtype, y.values);
}
const depthwiseConv2dNativeConfig = {
    kernelName: (0, _tfjsCore.DepthwiseConv2dNative),
    backendName: "cpu",
    kernelFunc: depthwiseConv2dNative
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"j6FA2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropFilter", ()=>depthwiseConv2dNativeBackpropFilter);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropFilterConfig", ()=>depthwiseConv2dNativeBackpropFilterConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function depthwiseConv2dNativeBackpropFilter(args) {
    const { inputs, backend, attrs } = args;
    const { x, dy } = inputs;
    const { strides, dilations, pad, dimRoundingMode, filterShape } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        dy
    ], "depthwiseConv2dNativeBackpropFilter");
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true);
    const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
    const dW = new (0, _tfjsCore.TensorBuffer)(convInfo.filterShape, "float32");
    const leftPad = convInfo.padInfo.left;
    const topPad = convInfo.padInfo.top;
    const chMul = convInfo.outChannels / convInfo.inChannels;
    const xVals = backend.data.get(x.dataId).values;
    const xBuf = new (0, _tfjsCore.TensorBuffer)(x.shape, x.dtype, xVals);
    const dyVals = backend.data.get(dy.dataId).values;
    const dyBuf = new (0, _tfjsCore.TensorBuffer)(dy.shape, dy.dtype, dyVals);
    for(let wR = 0; wR < filterHeight; ++wR){
        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
        const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
        for(let wC = 0; wC < filterWidth; ++wC){
            const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
            const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
            for(let d2 = 0; d2 < convInfo.outChannels; ++d2){
                const d1 = Math.trunc(d2 / chMul);
                const dm = d2 % chMul;
                let dotProd = 0;
                for(let b = 0; b < convInfo.batchSize; ++b)for(let yR = yRMin; yR < yRMax; ++yR){
                    const xR = wR + yR * strideHeight - topPad;
                    for(let yC = yCMin; yC < yCMax; ++yC){
                        const xC = wC + yC * strideWidth - leftPad;
                        dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
                    }
                }
                dW.set(dotProd, wR, wC, d1, dm);
            }
        }
    }
    return backend.makeTensorInfo(dW.shape, dW.dtype, dW.values);
}
const depthwiseConv2dNativeBackpropFilterConfig = {
    kernelName: (0, _tfjsCore.DepthwiseConv2dNativeBackpropFilter),
    backendName: "cpu",
    kernelFunc: depthwiseConv2dNativeBackpropFilter
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4Kyp1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropInput", ()=>depthwiseConv2dNativeBackpropInput);
parcelHelpers.export(exports, "depthwiseConv2dNativeBackpropInputConfig", ()=>depthwiseConv2dNativeBackpropInputConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function depthwiseConv2dNativeBackpropInput(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { strides, dilations, pad, dimRoundingMode, inputShape } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        filter
    ], "depthwiseConv2DNativeBackpropInput");
    const dyStrides = (0, _tfjsCore.util).computeStrides(dy.shape);
    const filterStrides = (0, _tfjsCore.util).computeStrides(filter.shape);
    const convInfo = (0, _tfjsCore.backend_util).computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true);
    const dx = new (0, _tfjsCore.TensorBuffer)(convInfo.inShape, "float32");
    const dxValues = dx.values;
    const [dxS0, dxS1, dxS2] = dx.strides;
    const dyValues = backend.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2] = dyStrides;
    const fltValues = backend.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2] = filterStrides;
    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    const chMul = outChannels / inChannels;
    for(let b = 0; b < batchSize; ++b){
        for(let d1 = 0; d1 < inChannels; ++d1)for(let xR = 0; xR < inHeight; ++xR){
            const xRCorner = xR - topPad;
            const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
            const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
            for(let xC = 0; xC < inWidth; ++xC){
                const xCCorner = xC - leftPad;
                const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
                const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
                let dotProd = 0;
                for(let yR = xRMin; yR < yRMax; ++yR){
                    const wR = yR * strideHeight - xRCorner;
                    for(let yC = xCMin; yC < yCMax; ++yC){
                        const wC = yC * strideWidth - xCCorner;
                        const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
                        const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
                        for(let dm = 0; dm < chMul; ++dm){
                            const d2 = d1 * chMul + dm;
                            const pixel = dyValues[dyOffset + d2];
                            const weight = fltValues[fltOffset + dm];
                            dotProd += pixel * weight;
                        }
                    }
                }
                dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
const depthwiseConv2dNativeBackpropInputConfig = {
    kernelName: (0, _tfjsCore.DepthwiseConv2dNativeBackpropInput),
    backendName: "cpu",
    kernelFunc: depthwiseConv2dNativeBackpropInput
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c5zdj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "diag", ()=>diag);
parcelHelpers.export(exports, "diagConfig", ()=>diagConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function diag(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const xVals = backend.data.get(x.dataId).values;
    const outBuf = (0, _tfjsCore.buffer)([
        xSize,
        xSize
    ], x.dtype);
    const vals = outBuf.values;
    for(let i = 0; i < xVals.length; i++)vals[i * xSize + i] = xVals[i];
    const outShape = [
        ...x.shape,
        ...x.shape
    ];
    return backend.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
}
const diagConfig = {
    kernelName: (0, _tfjsCore.Diag),
    backendName: "cpu",
    kernelFunc: diag
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8CglA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "dilation2DConfig", ()=>dilation2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
const dilation2DConfig = {
    kernelName: (0, _tfjsCore.Dilation2D),
    backendName: "cpu",
    kernelFunc: ({ inputs, backend, attrs })=>{
        const { x, filter } = inputs;
        const { strides, pad, dilations } = attrs;
        const cpuBackend = backend;
        const xVals = cpuBackend.data.get(x.dataId).values;
        const xRank = x.shape.length;
        const filterVals = cpuBackend.data.get(filter.dataId).values;
        const filterRank = filter.shape.length;
        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = (0, _tfjsCore.backend_util).computeDilation2DInfo(x.shape, filter.shape, strides, pad, "NHWC" /* dataFormat */ , dilations);
        const outSize = (0, _tfjsCore.util).sizeFromShape(outShape);
        const outRank = outShape.length;
        const outputVals = (0, _tfjsCore.util).getArrayFromDType(x.dtype, outSize);
        // Upsampling the input by fill in `dilation size - 1` values between each
        // input value.
        // This implementation follows the TF c++ implementation:
        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
        for(let b = 0; b < batchSize; ++b)for(let hOut = 0; hOut < outHeight; ++hOut){
            const hBeg = hOut * strideHeight - padInfo.top;
            for(let wOut = 0; wOut < outWidth; ++wOut){
                const wBeg = wOut * strideWidth - padInfo.left;
                for(let d = 0; d < inChannels; ++d){
                    let curVal = Number.MIN_SAFE_INTEGER;
                    for(let h = 0; h < filterHeight; ++h){
                        const hIn = hBeg + h * dilationHeight;
                        if (hIn >= 0 && hIn < inHeight) for(let w = 0; w < filterWidth; ++w){
                            const wIn = wBeg + w * dilationWidth;
                            if (wIn >= 0 && wIn < inWidth) {
                                const xIndex = (0, _tfjsCore.util).locToIndex([
                                    b,
                                    hIn,
                                    wIn,
                                    d
                                ], xRank, (0, _tfjsCore.util).computeStrides(x.shape));
                                const filterIndex = (0, _tfjsCore.util).locToIndex([
                                    h,
                                    w,
                                    d
                                ], filterRank, (0, _tfjsCore.util).computeStrides(filter.shape));
                                const val = xVals[xIndex] + filterVals[filterIndex];
                                if (val > curVal) curVal = val;
                            }
                        }
                    }
                    const outputIndex = (0, _tfjsCore.util).locToIndex([
                        b,
                        hOut,
                        wOut,
                        d
                    ], outRank, (0, _tfjsCore.util).computeStrides(outShape));
                    outputVals[outputIndex] = curVal;
                }
            }
        }
        const dataId = cpuBackend.write((0, _tfjsCore.util).toTypedArray(outputVals, x.dtype), outShape, x.dtype);
        return {
            dataId,
            shape: outShape,
            dtype: x.dtype
        };
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5GOEE":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "dilation2DBackpropFilterConfig", ()=>dilation2DBackpropFilterConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
const dilation2DBackpropFilterConfig = {
    kernelName: (0, _tfjsCore.Dilation2DBackpropFilter),
    backendName: "cpu",
    kernelFunc: ({ inputs, backend, attrs })=>{
        const { x, filter, dy } = inputs;
        const { strides, pad, dilations } = attrs;
        const cpuBackend = backend;
        const $x = (0, _tfjsCore.util).toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
        const $filter = (0, _tfjsCore.util).toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = (0, _tfjsCore.backend_util).computeDilation2DInfo(x.shape, filter.shape, strides, pad, "NHWC" /* dataFormat */ , dilations);
        (0, _tfjsCore.util).assert(dy.rank === outShape.length, ()=>`Error in ${0, _tfjsCore.Dilation2DBackpropFilter}, dy ` + `must have the same rank as output ${outShape.length}, but got ` + `${dy.rank}`);
        const $dy = (0, _tfjsCore.util).toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
        // The computed filter gradients has the same dimensions as the filter:
        // [filterHeight, filterWidth, depth]
        const gradients = (0, _tfjsCore.util).makeZerosNestedTypedArray(filter.shape, filter.dtype);
        // In the case of multiple argmax branches, we only back-propagate along the
        // last branch, i.e., the one with largest value of `h * filter_cols + w`,
        // similarly to the max-pooling backward routines.
        // This implementation follows the TF c++ implementation:
        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
        for(let b = 0; b < batchSize; ++b)for(let hOut = 0; hOut < outHeight; ++hOut){
            const hBeg = hOut * strideHeight - padInfo.top;
            for(let wOut = 0; wOut < outWidth; ++wOut){
                const wBeg = wOut * strideWidth - padInfo.left;
                for(let d = 0; d < inChannels; ++d){
                    let curVal = Number.MIN_SAFE_INTEGER;
                    let hMax = 0;
                    let wMax = 0;
                    for(let h = 0; h < filterHeight; ++h){
                        const hIn = hBeg + h * dilationHeight;
                        if (hIn >= 0 && hIn < inHeight) for(let w = 0; w < filterWidth; ++w){
                            const wIn = wBeg + w * dilationWidth;
                            if (wIn >= 0 && wIn < inWidth) {
                                const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
                                if (val > curVal) {
                                    curVal = val;
                                    hMax = h;
                                    wMax = w;
                                }
                            }
                        }
                    }
                    gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];
                }
            }
        }
        const dataId = cpuBackend.write((0, _tfjsCore.util).toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);
        return {
            dataId,
            shape: filter.shape,
            dtype: filter.dtype
        };
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fnjGq":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "dilation2DBackpropInputConfig", ()=>dilation2DBackpropInputConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
const dilation2DBackpropInputConfig = {
    kernelName: (0, _tfjsCore.Dilation2DBackpropInput),
    backendName: "cpu",
    kernelFunc: ({ inputs, backend, attrs })=>{
        const { x, filter, dy } = inputs;
        const { strides, pad, dilations } = attrs;
        const cpuBackend = backend;
        const $x = (0, _tfjsCore.util).toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
        const $filter = (0, _tfjsCore.util).toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = (0, _tfjsCore.backend_util).computeDilation2DInfo(x.shape, filter.shape, strides, pad, "NHWC" /* dataFormat */ , dilations);
        (0, _tfjsCore.util).assert(dy.rank === outShape.length, ()=>`Error in ${0, _tfjsCore.Dilation2DBackpropInput}, dy ` + `must have the same rank as output ${outShape.length}, but got ` + `${dy.rank}`);
        const $dy = (0, _tfjsCore.util).toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
        // The computed gradients has the same dimensions as the input:
        // [batch, inputHeight, inputCols, inChannel]
        const gradients = (0, _tfjsCore.util).makeZerosNestedTypedArray(x.shape, x.dtype);
        // In the case of multiple argmax branches, we only back-propagate along the
        // last branch, i.e., the one with largest value of `h * filter_cols + w`,
        // similarly to the max-pooling backward routines.
        // This implementation follows the TF c++ implementation:
        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
        for(let b = 0; b < batchSize; ++b)for(let hOut = 0; hOut < outHeight; ++hOut){
            const hBeg = hOut * strideHeight - padInfo.top;
            for(let wOut = 0; wOut < outWidth; ++wOut){
                const wBeg = wOut * strideWidth - padInfo.left;
                for(let d = 0; d < inChannels; ++d){
                    let curVal = Number.MIN_SAFE_INTEGER;
                    let hInMax = hBeg < 0 ? 0 : hBeg;
                    let wInMax = wBeg < 0 ? 0 : wBeg;
                    for(let h = 0; h < filterHeight; ++h){
                        const hIn = hBeg + h * dilationHeight;
                        if (hIn >= 0 && hIn < inHeight) for(let w = 0; w < filterWidth; ++w){
                            const wIn = wBeg + w * dilationWidth;
                            if (wIn >= 0 && wIn < inWidth) {
                                const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
                                if (val > curVal) {
                                    curVal = val;
                                    hInMax = hIn;
                                    wInMax = wIn;
                                }
                            }
                        }
                    }
                    gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
                }
            }
        }
        const dataId = cpuBackend.write((0, _tfjsCore.util).toTypedArray(gradients, x.dtype), x.shape, x.dtype);
        return {
            dataId,
            shape: x.shape,
            dtype: x.dtype
        };
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9ZCFV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2023 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "draw", ()=>draw);
parcelHelpers.export(exports, "drawConfig", ()=>drawConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function draw(args) {
    const { inputs, backend, attrs } = args;
    const { image } = inputs;
    const { canvas, options } = attrs;
    const { contextOptions, imageOptions } = options || {};
    const alpha = (imageOptions === null || imageOptions === void 0 ? void 0 : imageOptions.alpha) || 1;
    const contextType = (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextType) || "2d";
    if (contextType !== "2d") throw new Error(`Context type ${contextOptions.contextType} is not supported by the CPU backend.`);
    const ctx = canvas.getContext(contextType, (contextOptions === null || contextOptions === void 0 ? void 0 : contextOptions.contextAttributes) || {});
    if (ctx == null) throw new Error(`Could not get the context with ${contextType} type.`);
    const [height, width] = image.shape.slice(0, 2);
    const depth = image.shape.length === 2 ? 1 : image.shape[2];
    const data = backend.data.get(image.dataId).values;
    const multiplier = image.dtype === "float32" ? 255 : 1;
    const bytes = new Uint8ClampedArray(width * height * 4);
    for(let i = 0; i < height * width; ++i){
        const rgba = [
            0,
            0,
            0,
            255 * alpha
        ];
        for(let d = 0; d < depth; d++){
            const value = data[i * depth + d];
            if (image.dtype === "float32") {
                if (value < 0 || value > 1) throw new Error(`Tensor values for a float32 Tensor must be in the ` + `range [0 - 1] but encountered ${value}.`);
            } else if (image.dtype === "int32") {
                if (value < 0 || value > 255) throw new Error(`Tensor values for a int32 Tensor must be in the ` + `range [0 - 255] but encountered ${value}.`);
            }
            if (depth === 1) {
                rgba[0] = value * multiplier;
                rgba[1] = value * multiplier;
                rgba[2] = value * multiplier;
            } else rgba[d] = value * multiplier;
        }
        const j = i * 4;
        bytes[j + 0] = Math.round(rgba[0]);
        bytes[j + 1] = Math.round(rgba[1]);
        bytes[j + 2] = Math.round(rgba[2]);
        bytes[j + 3] = Math.round(rgba[3]);
    }
    canvas.width = width;
    canvas.height = height;
    const imageData = new ImageData(bytes, width, height);
    ctx.putImageData(imageData, 0, 0);
    return image;
}
const drawConfig = {
    kernelName: (0, _tfjsCore.Draw),
    backendName: "cpu",
    kernelFunc: draw
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lXbVv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "einsum", ()=>einsum);
parcelHelpers.export(exports, "einsumConfig", ()=>einsumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _multiply = require("./Multiply");
var _reshape = require("./Reshape");
var _sum = require("./Sum");
var _transpose = require("./Transpose");
function einsum(args) {
    const { inputs, backend, attrs } = args;
    const { equation } = attrs;
    const tensors = inputs;
    const { allDims, summedDims, idDims } = (0, _tfjsCore.backend_util).decodeEinsumEquation(equation, tensors.length);
    (0, _tfjsCore.backend_util).checkEinsumDimSizes(allDims.length, idDims, tensors);
    const { path, steps } = (0, _tfjsCore.backend_util).getEinsumComputePath(summedDims, idDims);
    const nSteps = steps.length;
    let out = null;
    let numDimsRemaining = allDims.length;
    const tensorsToDispose = [];
    for(let i = 0; i < nSteps; ++i){
        for (const idTerm of steps[i]){
            const { permutationIndices: perm, expandDims: dimsToExpand } = (0, _tfjsCore.backend_util).getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
            let x;
            if ((0, _tfjsCore.backend_util).isIdentityPermutation(perm)) x = tensors[idTerm];
            else {
                x = (0, _transpose.transpose)({
                    inputs: {
                        x: tensors[idTerm]
                    },
                    backend,
                    attrs: {
                        perm
                    }
                });
                tensorsToDispose.push(x);
            }
            const targetShape = x.shape.slice();
            for(let k = 0; k < dimsToExpand.length; ++k)targetShape.splice(dimsToExpand[k], 0, 1);
            if (!(0, _tfjsCore.util).arraysEqual(x.shape, targetShape)) {
                x = (0, _reshape.reshape)({
                    inputs: {
                        x
                    },
                    backend,
                    attrs: {
                        shape: targetShape
                    }
                });
                tensorsToDispose.push(x);
            }
            if (out === null) out = x;
            else {
                // tslint:disable-next-line: no-unnecessary-type-assertion
                out = (0, _multiply.multiply)({
                    inputs: {
                        a: x,
                        b: out
                    },
                    backend
                });
                tensorsToDispose.push(out);
            }
        }
        if (i < nSteps - 1) {
            if (path[i] >= 0) {
                out = (0, _sum.sum)({
                    inputs: {
                        x: out
                    },
                    backend,
                    attrs: {
                        axis: path[i] - (allDims.length - numDimsRemaining),
                        keepDims: false
                    }
                });
                tensorsToDispose.push(out);
            }
            numDimsRemaining--;
        }
    }
    // Clean up intermediate tensors.
    for (const tensorInfo of tensorsToDispose){
        if (tensorInfo === out) continue;
        backend.disposeIntermediateTensorInfo(tensorInfo);
    }
    return out;
}
const einsumConfig = {
    kernelName: (0, _tfjsCore.Einsum),
    backendName: "cpu",
    kernelFunc: einsum
};

},{"@tensorflow/tfjs-core":"21Ftl","./Multiply":"FrRi2","./Reshape":"9350i","./Sum":"6gbbV","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6gbbV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sum", ()=>sum);
parcelHelpers.export(exports, "sumConfig", ()=>sumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _zerosImpl = require("../utils/zeros_impl");
var _cast = require("./Cast");
var _identity = require("./Identity");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function sum(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "sum");
    let $x;
    if (x.dtype === "bool") $x = (0, _cast.cast)({
        inputs: {
            x
        },
        backend,
        attrs: {
            dtype: "int32"
        }
    });
    else $x = (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    const xRank = $x.shape.length;
    const axes = (0, _tfjsCore.util).parseAxisParam(axis, $x.shape);
    const permutation = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    let reductionAxes = axes;
    let permutedX = $x;
    if (permutation != null) {
        permutedX = (0, _transpose.transpose)({
            inputs: {
                x: $x
            },
            backend,
            attrs: {
                perm: permutation
            }
        });
        reductionAxes = (0, _tfjsCore.backend_util).getInnerMostAxes(reductionAxes.length, xRank);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("sum", reductionAxes, permutedX.shape.length);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(permutedX.shape, reductionAxes);
    const resultDtype = (0, _tfjsCore.backend_util).upcastType(permutedX.dtype, "int32");
    let result = (0, _zerosImpl.zeros)(backend, outShape, resultDtype);
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const vals = backend.data.get(result.dataId).values;
    const aVals = backend.data.get(permutedX.dataId).values;
    for(let i = 0; i < vals.length; ++i){
        const offset = i * reduceSize;
        let sum = 0;
        for(let j = 0; j < reduceSize; ++j)sum += aVals[offset + j];
        vals[i] = sum;
    }
    if (keepDims) {
        const newShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(result.shape, axes);
        const oldResult = result;
        result = (0, _reshape.reshape)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                shape: newShape
            }
        });
        backend.disposeIntermediateTensorInfo(oldResult);
    }
    backend.disposeIntermediateTensorInfo($x);
    if (permutation != null) backend.disposeIntermediateTensorInfo(permutedX);
    return result;
}
const sumConfig = {
    kernelName: (0, _tfjsCore.Sum),
    backendName: "cpu",
    kernelFunc: sum
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/zeros_impl":"6us7T","./Cast":"fAtIw","./Identity":"btREJ","./Reshape":"9350i","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cZudI":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "eluGrad", ()=>eluGrad);
parcelHelpers.export(exports, "eluGradConfig", ()=>eluGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function eluGrad(args) {
    const { inputs, backend } = args;
    const { dy, y } = inputs;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        y
    ], "eluGrad");
    const resultValues = new Float32Array((0, _tfjsCore.util).sizeFromShape(y.shape));
    const values = backend.data.get(y.dataId).values;
    const dyValues = backend.data.get(dy.dataId).values;
    for(let i = 0; i < values.length; ++i){
        const v = values[i];
        if (v >= 0) resultValues[i] = dyValues[i];
        else resultValues[i] = dyValues[i] * (v + 1);
    }
    return backend.makeTensorInfo(y.shape, "float32", resultValues);
}
const eluGradConfig = {
    kernelName: (0, _tfjsCore.EluGrad),
    backendName: "cpu",
    kernelFunc: eluGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"g7iiK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "erf", ()=>erf);
parcelHelpers.export(exports, "erfConfig", ()=>erfConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const p = (0, _tfjsCore.backend_util).ERF_P;
const a1 = (0, _tfjsCore.backend_util).ERF_A1;
const a2 = (0, _tfjsCore.backend_util).ERF_A2;
const a3 = (0, _tfjsCore.backend_util).ERF_A3;
const a4 = (0, _tfjsCore.backend_util).ERF_A4;
const a5 = (0, _tfjsCore.backend_util).ERF_A5;
const erf = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Erf), (xi)=>{
    const sign = Math.sign(xi);
    const v = Math.abs(xi);
    const t = 1.0 / (1.0 + p * v);
    return sign * (1.0 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v));
});
const erfConfig = {
    kernelName: (0, _tfjsCore.Erf),
    backendName: "cpu",
    kernelFunc: erf
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4z77l":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "expandDims", ()=>expandDims);
parcelHelpers.export(exports, "expandDimsConfig", ()=>expandDimsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reshape = require("./Reshape");
function expandDims(args) {
    const { inputs, backend, attrs } = args;
    const { input } = inputs;
    const { dim } = attrs;
    const inputRank = input.shape.length;
    const newShape = input.shape.slice();
    let $dim = dim;
    if (dim < 0) {
        // Negative value is counted from the tail of rank.
        (0, _tfjsCore.util).assert(-(inputRank + 1) <= dim, ()=>`Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
        $dim = inputRank + dim + 1;
    }
    newShape.splice($dim, 0, 1);
    return (0, _reshape.reshape)({
        inputs: {
            x: input
        },
        backend,
        attrs: {
            shape: newShape
        }
    });
}
const expandDimsConfig = {
    kernelName: (0, _tfjsCore.ExpandDims),
    backendName: "cpu",
    kernelFunc: expandDims
};

},{"@tensorflow/tfjs-core":"21Ftl","./Reshape":"9350i","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7gqPz":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fft", ()=>fft);
parcelHelpers.export(exports, "fftConfig", ()=>fftConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fftUtils = require("../utils/fft_utils");
var _reshape = require("./Reshape");
function fft(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    const inputSize = (0, _tfjsCore.util).sizeFromShape(input.shape);
    // Collapse all outer dimensions to a single batch dimension.
    const innerDimensionSize = input.shape[input.shape.length - 1];
    const batch = inputSize / innerDimensionSize;
    const input2D = (0, _reshape.reshape)({
        inputs: {
            x: input
        },
        backend,
        attrs: {
            shape: [
                batch,
                innerDimensionSize
            ]
        }
    });
    const result = (0, _fftUtils.fftBatch)(input2D, false, backend);
    const resultReshaped = (0, _reshape.reshape)({
        inputs: {
            x: result
        },
        backend,
        attrs: {
            shape: input.shape
        }
    });
    backend.disposeIntermediateTensorInfo(input2D);
    backend.disposeIntermediateTensorInfo(result);
    return resultReshaped;
}
const fftConfig = {
    kernelName: (0, _tfjsCore.FFT),
    backendName: "cpu",
    kernelFunc: fft
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/fft_utils":"8ziy5","./Reshape":"9350i","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8ziy5":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
/**
 * Calculate FFT of inner most elements of batch tensor.
 */ parcelHelpers.export(exports, "fftBatch", ()=>fftBatch);
parcelHelpers.export(exports, "fftImpl", ()=>fftImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _add = require("../kernels/Add");
var _complex = require("../kernels/Complex");
var _concat = require("../kernels/Concat");
var _identity = require("../kernels/Identity");
var _imag = require("../kernels/Imag");
var _multiply = require("../kernels/Multiply");
var _real = require("../kernels/Real");
var _realDiv = require("../kernels/RealDiv");
var _slice = require("../kernels/Slice");
var _sub = require("../kernels/Sub");
function fftBatch(input, inverse, cpuBackend) {
    const inputShape = input.shape;
    const batch = inputShape[0];
    const innerDim = inputShape[1];
    const inputVals = cpuBackend.data.get(input.dataId);
    const real2D = inputVals.complexTensorInfos.real;
    const imag2D = inputVals.complexTensorInfos.imag;
    // Collects real and imaginary values separately.
    const resultShape = [
        batch,
        innerDim
    ];
    const resultSize = (0, _tfjsCore.util).sizeFromShape(resultShape);
    const resultReal = (0, _tfjsCore.util).getTypedArrayFromDType("float32", resultSize);
    const resultImag = (0, _tfjsCore.util).getTypedArrayFromDType("float32", resultSize);
    for(let b = 0; b < batch; b++){
        // TODO: Support slice ops for complex type.
        const r = (0, _slice.slice)({
            inputs: {
                x: real2D
            },
            backend: cpuBackend,
            attrs: {
                begin: [
                    b,
                    0
                ],
                size: [
                    1,
                    innerDim
                ]
            }
        });
        const i = (0, _slice.slice)({
            inputs: {
                x: imag2D
            },
            backend: cpuBackend,
            attrs: {
                begin: [
                    b,
                    0
                ],
                size: [
                    1,
                    innerDim
                ]
            }
        });
        const input = (0, _complex.complex)({
            inputs: {
                real: r,
                imag: i
            },
            backend: cpuBackend
        });
        // Run FFT by batch element.
        const { real, imag } = fftImpl(input, inverse, cpuBackend);
        const res = (0, _tfjsCore.backend_util).mergeRealAndImagArrays(real, imag);
        for(let d = 0; d < innerDim; d++){
            const c = (0, _tfjsCore.backend_util).getComplexWithIndex(res, d);
            resultReal[b * innerDim + d] = c.real;
            resultImag[b * innerDim + d] = c.imag;
        }
        cpuBackend.disposeIntermediateTensorInfo(r);
        cpuBackend.disposeIntermediateTensorInfo(i);
        cpuBackend.disposeIntermediateTensorInfo(input);
    }
    const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal);
    const $imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultImag);
    const result = (0, _complex.complex)({
        inputs: {
            real: $realInfo,
            imag: $imagInfo
        },
        backend: cpuBackend
    });
    cpuBackend.disposeIntermediateTensorInfo($realInfo);
    cpuBackend.disposeIntermediateTensorInfo($imagInfo);
    return result;
}
function fftImpl(input, inverse, cpuBackend) {
    const inputSize = (0, _tfjsCore.util).sizeFromShape(input.shape);
    const inputVals = cpuBackend.data.get(input.dataId);
    const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;
    const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;
    if (isExponentOf2(inputSize)) {
        const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);
        const resultShape = [
            input.shape[0],
            input.shape[1]
        ];
        if (inverse) {
            const realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.real);
            const imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.imag);
            const sizeInfo = cpuBackend.makeTensorInfo([], "float32", (0, _tfjsCore.util).createScalarValue(inputSize, "float32"));
            const sizeInfoCopy = (0, _identity.identity)({
                inputs: {
                    x: sizeInfo
                },
                backend: cpuBackend
            });
            const divRealInfo = (0, _realDiv.realDivConfig).kernelFunc({
                inputs: {
                    a: realInfo,
                    b: sizeInfo
                },
                backend: cpuBackend
            });
            const divImagInfo = (0, _realDiv.realDivConfig).kernelFunc({
                inputs: {
                    a: imagInfo,
                    b: sizeInfoCopy
                },
                backend: cpuBackend
            });
            const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;
            const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;
            cpuBackend.disposeIntermediateTensorInfo(realInfo);
            cpuBackend.disposeIntermediateTensorInfo(imagInfo);
            cpuBackend.disposeIntermediateTensorInfo(sizeInfo);
            cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);
            cpuBackend.disposeIntermediateTensorInfo(divRealInfo);
            cpuBackend.disposeIntermediateTensorInfo(divImagInfo);
            return {
                real: divRealVals,
                imag: divImagVals
            };
        }
        return result;
    } else {
        const data = (0, _tfjsCore.backend_util).mergeRealAndImagArrays(realVals, imagVals);
        const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);
        return (0, _tfjsCore.backend_util).splitRealAndImagArrays(rawOutput);
    }
}
function isExponentOf2(size) {
    return (size & size - 1) === 0;
}
// FFT using Cooley-Tukey algorithm on radix 2 dimensional input.
function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {
    if (size === 1) return {
        real: realVals,
        imag: imagVals
    };
    const data = (0, _tfjsCore.backend_util).mergeRealAndImagArrays(realVals, imagVals);
    const half = size / 2;
    const evenComplex = (0, _tfjsCore.backend_util).complexWithEvenIndex(data);
    const evenRealVals = evenComplex.real;
    const evenImagVals = evenComplex.imag;
    const evenShape = [
        evenRealVals.length
    ];
    const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenRealVals);
    const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenImagVals);
    const evenTensorInfo = (0, _complex.complex)({
        inputs: {
            real: evenRealInfo,
            imag: evenImagInfo
        },
        backend: cpuBackend
    });
    const oddComplex = (0, _tfjsCore.backend_util).complexWithOddIndex(data);
    const oddRealVals = oddComplex.real;
    const oddImagVals = oddComplex.imag;
    const oddShape = [
        oddRealVals.length
    ];
    const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddRealVals);
    const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddImagVals);
    const oddTensorInfo = (0, _complex.complex)({
        inputs: {
            real: oddRealInfo,
            imag: oddImagInfo
        },
        backend: cpuBackend
    });
    // Recursive call for half part of original input.
    const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);
    const $evenRealVals = $evenComplex.real;
    const $evenImagVals = $evenComplex.imag;
    const $evenShape = [
        $evenRealVals.length
    ];
    const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenRealVals);
    const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenImagVals);
    const $evenTensorInfo = (0, _complex.complex)({
        inputs: {
            real: $evenRealInfo,
            imag: $evenImagInfo
        },
        backend: cpuBackend
    });
    const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);
    const $oddRealVals = $oddComplex.real;
    const $oddImagVals = $oddComplex.imag;
    const $oddShape = [
        $oddRealVals.length
    ];
    const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals);
    const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals);
    const $oddTensorInfo = (0, _complex.complex)({
        inputs: {
            real: $oddRealInfo,
            imag: $oddImagInfo
        },
        backend: cpuBackend
    });
    const e = (0, _tfjsCore.backend_util).exponents(size, inverse);
    const eShape = [
        e.real.length
    ];
    const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.real);
    const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.imag);
    const complexInfo = (0, _complex.complex)({
        inputs: {
            real: eRealInfo,
            imag: eImagInfo
        },
        backend: cpuBackend
    });
    const exponentInfo = (0, _multiply.multiply)({
        inputs: {
            a: complexInfo,
            b: $oddTensorInfo
        },
        backend: cpuBackend
    });
    const addPart = (0, _add.add)({
        inputs: {
            a: $evenTensorInfo,
            b: exponentInfo
        },
        backend: cpuBackend
    });
    const subPart = (0, _sub.sub)({
        inputs: {
            a: $evenTensorInfo,
            b: exponentInfo
        },
        backend: cpuBackend
    });
    const addPartReal = (0, _real.real)({
        inputs: {
            input: addPart
        },
        backend: cpuBackend
    });
    const subPartReal = (0, _real.real)({
        inputs: {
            input: subPart
        },
        backend: cpuBackend
    });
    const addPartImag = (0, _imag.imag)({
        inputs: {
            input: addPart
        },
        backend: cpuBackend
    });
    const subPartImag = (0, _imag.imag)({
        inputs: {
            input: subPart
        },
        backend: cpuBackend
    });
    const $real = (0, _concat.concat)({
        inputs: [
            addPartReal,
            subPartReal
        ],
        backend: cpuBackend,
        attrs: {
            axis: 0
        }
    });
    const $imag = (0, _concat.concat)({
        inputs: [
            addPartImag,
            subPartImag
        ],
        backend: cpuBackend,
        attrs: {
            axis: 0
        }
    });
    const $realVals = cpuBackend.data.get($real.dataId).values;
    const $imagVals = cpuBackend.data.get($imag.dataId).values;
    cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);
    cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);
    cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);
    cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);
    cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);
    cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);
    cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);
    cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);
    cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);
    cpuBackend.disposeIntermediateTensorInfo(eRealInfo);
    cpuBackend.disposeIntermediateTensorInfo(eImagInfo);
    cpuBackend.disposeIntermediateTensorInfo(complexInfo);
    cpuBackend.disposeIntermediateTensorInfo(exponentInfo);
    cpuBackend.disposeIntermediateTensorInfo(addPart);
    cpuBackend.disposeIntermediateTensorInfo(subPart);
    cpuBackend.disposeIntermediateTensorInfo(addPartReal);
    cpuBackend.disposeIntermediateTensorInfo(addPartImag);
    cpuBackend.disposeIntermediateTensorInfo(subPartReal);
    cpuBackend.disposeIntermediateTensorInfo(subPartImag);
    cpuBackend.disposeIntermediateTensorInfo($real);
    cpuBackend.disposeIntermediateTensorInfo($imag);
    return {
        real: $realVals,
        imag: $imagVals
    };
}
// Calculate fourier transform by multplying sinusoid matrix.
function fourierTransformByMatmul(data, size, inverse) {
    const ret = new Float32Array(size * 2);
    // TODO: Use matmul instead once it supports complex64 type.
    for(let r = 0; r < size; r++){
        let real = 0.0;
        let imag = 0.0;
        for(let c = 0; c < size; c++){
            const e = (0, _tfjsCore.backend_util).exponent(r * c, size, inverse);
            const term = (0, _tfjsCore.backend_util).getComplexWithIndex(data, c);
            real += term.real * e.real - term.imag * e.imag;
            imag += term.real * e.imag + term.imag * e.real;
        }
        if (inverse) {
            real /= size;
            imag /= size;
        }
        (0, _tfjsCore.backend_util).assignToTypedArray(ret, real, imag, r);
    }
    return ret;
}

},{"@tensorflow/tfjs-core":"21Ftl","../kernels/Add":"dippY","../kernels/Complex":"3DPmh","../kernels/Concat":"iYEAT","../kernels/Identity":"btREJ","../kernels/Imag":"af3Tx","../kernels/Multiply":"FrRi2","../kernels/Real":"eZ7aJ","../kernels/RealDiv":"9Ly75","../kernels/Slice":"9L9GN","../kernels/Sub":"4DByI","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9Ly75":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "realDivImpl", ()=>realDivImpl);
parcelHelpers.export(exports, "div", ()=>div);
parcelHelpers.export(exports, "realDivConfig", ()=>realDivConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const realDivImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a / b);
const div = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.RealDiv), realDivImpl);
const realDivConfig = {
    kernelName: (0, _tfjsCore.RealDiv),
    backendName: "cpu",
    kernelFunc: div
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4BMgR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fill", ()=>fill);
parcelHelpers.export(exports, "fillConfig", ()=>fillConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function fill(args) {
    const { backend, attrs } = args;
    const { shape, value, dtype } = attrs;
    const $dtype = dtype || (0, _tfjsCore.util).inferDtype(value);
    const values = (0, _tfjsCore.util).getArrayFromDType($dtype, (0, _tfjsCore.util).sizeFromShape(shape));
    fillValues(values, value, $dtype);
    return backend.makeTensorInfo(shape, $dtype, values);
}
const fillConfig = {
    kernelName: (0, _tfjsCore.Fill),
    backendName: "cpu",
    kernelFunc: fill
};
function fillValues(values, value, dtype) {
    if (dtype === "string") values.fill(value);
    else values.fill(value);
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"8d8ZF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "flipLeftRightConfig", ()=>flipLeftRightConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
const flipLeftRightConfig = {
    kernelName: (0, _tfjsCore.FlipLeftRight),
    backendName: "cpu",
    kernelFunc: ({ inputs, attrs, backend })=>{
        const { image } = inputs;
        const cpuBackend = backend;
        const output = (0, _tfjsCore.util).getTypedArrayFromDType(image.dtype, (0, _tfjsCore.util).sizeFromShape(image.shape));
        const [batch, imageHeight, imageWidth, numChannels] = image.shape;
        const imageVals = cpuBackend.data.get(image.dataId).values;
        for(let batchIdx = 0; batchIdx < batch; batchIdx++){
            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
            for(let row = 0; row < imageHeight; row++){
                const rowOffset = row * (imageWidth * numChannels);
                for(let col = 0; col < imageWidth; col++){
                    const colOffset = col * numChannels;
                    for(let channel = 0; channel < numChannels; channel++){
                        const coordX = Math.round(imageWidth - col - 1);
                        const outIdx = batchOffset + rowOffset + colOffset + channel;
                        let outputValue = imageVals[outIdx];
                        // If the coordinate position falls within the image boundaries...
                        if (coordX >= 0 && coordX < imageWidth) {
                            // set the output to the image value at the coordinate position.
                            const rotatedColOffset = coordX * numChannels;
                            const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;
                            outputValue = imageVals[imageIdx];
                        }
                        output[outIdx] = outputValue;
                    }
                }
            }
        }
        const dataId = cpuBackend.write(output, image.shape, image.dtype);
        return {
            dataId,
            shape: image.shape,
            dtype: image.dtype
        };
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1OWT7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fusedConv2D", ()=>fusedConv2D);
parcelHelpers.export(exports, "fusedConv2DConfig", ()=>fusedConv2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fusedUtils = require("../utils/fused_utils");
var _add = require("./Add");
var _conv2D = require("./Conv2D");
var _reshape = require("./Reshape");
function fusedConv2D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter, bias, preluActivationWeights } = inputs;
    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
    let result = (0, _conv2D.conv2D)({
        inputs: {
            x,
            filter
        },
        backend,
        attrs: {
            strides,
            pad,
            dataFormat,
            dilations,
            dimRoundingMode
        }
    });
    if (bias) {
        const resultOld = result;
        // For NCHW format, if bias is a 1-D tensor, it is supposed to be aligned
        // to the channel of the conv2d's result; if the bias is a scalar, the
        // bias_add is computed as if the bias was broadcasted to the shape of the
        // conv2d's result.
        if (dataFormat === "NCHW" && bias.shape.length === 1 && bias.shape[0] !== 1) {
            const reshapedBias = (0, _reshape.reshape)({
                inputs: {
                    x: bias
                },
                backend,
                attrs: {
                    shape: [
                        bias.shape[0],
                        1,
                        1
                    ]
                }
            });
            result = (0, _add.add)({
                inputs: {
                    a: result,
                    b: reshapedBias
                },
                backend
            });
            backend.disposeIntermediateTensorInfo(reshapedBias);
        } else // This condition handles NHWC and NCHW (scalar case). The only other case
        // for NCHW (1D case) is handled above.
        result = (0, _add.add)({
            inputs: {
                a: result,
                b: bias
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(resultOld);
    }
    if (activation) {
        const resultOld = result;
        // For NCHW format, if PReLu activation weights is a 1-D tensor, it is
        // supposed to be aligned with the channel of the conv2d's result. For other
        // cases, whether NCHW or NHWC data format, the conv2d result is
        // already aligned with the activation weights.
        if (dataFormat === "NCHW" && activation === "prelu" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {
            const reshapedAlpha = (0, _reshape.reshape)({
                inputs: {
                    x: preluActivationWeights
                },
                backend,
                attrs: {
                    shape: [
                        preluActivationWeights.shape[0],
                        1,
                        1
                    ]
                }
            });
            result = (0, _fusedUtils.applyActivation)(backend, result, activation, reshapedAlpha, leakyreluAlpha);
            backend.disposeIntermediateTensorInfo(reshapedAlpha);
        } else result = (0, _fusedUtils.applyActivation)(backend, result, activation, preluActivationWeights, leakyreluAlpha);
        backend.disposeIntermediateTensorInfo(resultOld);
    }
    return result;
}
const fusedConv2DConfig = {
    kernelName: (0, _tfjsCore.FusedConv2D),
    backendName: "cpu",
    kernelFunc: fusedConv2D
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/fused_utils":"6DUEF","./Add":"dippY","./Conv2D":"4bj3X","./Reshape":"9350i","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5SkmJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "fusedDepthwiseConv2D", ()=>fusedDepthwiseConv2D);
parcelHelpers.export(exports, "fusedDepthwiseConv2DConfig", ()=>fusedDepthwiseConv2DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fusedUtils = require("../utils/fused_utils");
var _add = require("./Add");
var _depthwiseConv2DNative = require("./DepthwiseConv2dNative");
function fusedDepthwiseConv2D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter, bias, preluActivationWeights } = inputs;
    const { strides, pad, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
    let result = (0, _depthwiseConv2DNative.depthwiseConv2dNative)({
        inputs: {
            x,
            filter
        },
        backend,
        attrs: {
            strides,
            pad,
            dataFormat,
            dilations,
            dimRoundingMode
        }
    });
    if (bias) {
        const oldResult = result;
        result = (0, _add.add)({
            inputs: {
                a: result,
                b: bias
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(oldResult);
    }
    if (activation) {
        const oldResult = result;
        result = (0, _fusedUtils.applyActivation)(backend, result, activation, preluActivationWeights, leakyreluAlpha);
        backend.disposeIntermediateTensorInfo(oldResult);
    }
    return result;
}
const fusedDepthwiseConv2DConfig = {
    kernelName: (0, _tfjsCore.FusedDepthwiseConv2D),
    backendName: "cpu",
    kernelFunc: fusedDepthwiseConv2D
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/fused_utils":"6DUEF","./Add":"dippY","./DepthwiseConv2dNative":"2iRpa","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6Owm3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gatherNd", ()=>gatherNd);
parcelHelpers.export(exports, "gatherNdConfig", ()=>gatherNdConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _gatherNdImpl = require("./GatherNd_Impl");
function gatherNd(args) {
    const { inputs, backend } = args;
    const { params, indices } = inputs;
    const paramsSize = (0, _tfjsCore.util).sizeFromShape(params.shape);
    const indicesShape = indices.shape;
    const sliceRank = indicesShape[indicesShape.length - 1];
    const [resultShape, numSlices, sliceSize, strides] = (0, _tfjsCore.backend_util).prepareAndValidate(params, indices);
    if (numSlices === 0) return backend.makeTensorInfo(resultShape, params.dtype, []);
    const indicesData = backend.data.get(indices.dataId).values;
    const paramsBuf = backend.bufferSync(params);
    const outBuf = (0, _gatherNdImpl.gatherNdImpl)(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
    return backend.makeTensorInfo(resultShape, params.dtype, outBuf.values);
}
const gatherNdConfig = {
    kernelName: (0, _tfjsCore.GatherNd),
    backendName: "cpu",
    kernelFunc: gatherNd
};

},{"@tensorflow/tfjs-core":"21Ftl","./GatherNd_Impl":"klZow","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dUokB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "gatherV2", ()=>gatherV2);
parcelHelpers.export(exports, "gatherV2Config", ()=>gatherV2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _gatherV2Impl = require("./GatherV2_impl");
var _reshape = require("./Reshape");
function gatherV2(args) {
    const { inputs, backend, attrs } = args;
    const { x, indices } = inputs;
    const { axis, batchDims } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x,
        indices
    ], "gatherV2");
    // Throw error when any index is out of bound.
    const parsedAxis = (0, _tfjsCore.util).parseAxisParam(axis, x.shape)[0];
    const indicesVals = backend.data.get(indices.dataId).values;
    const axisDim = x.shape[parsedAxis];
    for(let i = 0; i < indicesVals.length; ++i){
        const index = indicesVals[i];
        (0, _tfjsCore.util).assert(index <= axisDim - 1 && index >= 0, ()=>`GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
    }
    let $batchDims = batchDims;
    if (batchDims == null) $batchDims = 0;
    const indicesSize = (0, _tfjsCore.util).sizeFromShape(indices.shape);
    const shapeInfo = (0, _tfjsCore.backend_util).segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);
    const flattenX = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: [
                shapeInfo.batchSize,
                shapeInfo.outerSize,
                shapeInfo.dimSize,
                shapeInfo.sliceSize
            ]
        }
    });
    const flattenIndex = (0, _reshape.reshape)({
        inputs: {
            x: indices
        },
        backend,
        attrs: {
            shape: [
                shapeInfo.batchSize,
                indicesSize / shapeInfo.batchSize
            ]
        }
    });
    const flattenOutputShape = [
        shapeInfo.batchSize,
        shapeInfo.outerSize,
        indicesSize / shapeInfo.batchSize,
        shapeInfo.sliceSize
    ];
    const indicesBuf = backend.bufferSync(flattenIndex);
    const xBuf = backend.bufferSync(flattenX);
    const outBuf = (0, _gatherV2Impl.gatherV2Impl)(xBuf, indicesBuf, flattenOutputShape);
    backend.disposeIntermediateTensorInfo(flattenX);
    backend.disposeIntermediateTensorInfo(flattenIndex);
    return backend.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
}
const gatherV2Config = {
    kernelName: (0, _tfjsCore.GatherV2),
    backendName: "cpu",
    kernelFunc: gatherV2
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./GatherV2_impl":"5y26k","./Reshape":"9350i","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hHvJu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "ifft", ()=>ifft);
parcelHelpers.export(exports, "ifftConfig", ()=>ifftConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _fftUtils = require("../utils/fft_utils");
var _reshape = require("./Reshape");
function ifft(args) {
    const { inputs, backend } = args;
    const { input } = inputs;
    const inputSize = (0, _tfjsCore.util).sizeFromShape(input.shape);
    // Collapse all outer dimensions to a single batch dimension.
    const innerDimensionSize = input.shape[input.shape.length - 1];
    const batch = inputSize / innerDimensionSize;
    const input2D = (0, _reshape.reshape)({
        inputs: {
            x: input
        },
        backend,
        attrs: {
            shape: [
                batch,
                innerDimensionSize
            ]
        }
    });
    const result = (0, _fftUtils.fftBatch)(input2D, true, backend);
    const resultReshaped = (0, _reshape.reshape)({
        inputs: {
            x: result
        },
        backend,
        attrs: {
            shape: input.shape
        }
    });
    backend.disposeIntermediateTensorInfo(input2D);
    backend.disposeIntermediateTensorInfo(result);
    return resultReshaped;
}
const ifftConfig = {
    kernelName: (0, _tfjsCore.IFFT),
    backendName: "cpu",
    kernelFunc: ifft
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/fft_utils":"8ziy5","./Reshape":"9350i","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"a8CWT":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isFinite", ()=>isFinite);
parcelHelpers.export(exports, "isFiniteConfig", ()=>isFiniteConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const isFinite = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.IsFinite), (xi)=>Number.isFinite(xi) ? 1 : 0, "bool");
const isFiniteConfig = {
    kernelName: (0, _tfjsCore.IsFinite),
    backendName: "cpu",
    kernelFunc: isFinite
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"342Gs":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isInf", ()=>isInf);
parcelHelpers.export(exports, "isInfConfig", ()=>isInfConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const isInf = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.IsInf), (xi)=>Math.abs(xi) === Infinity ? 1 : 0, "bool");
const isInfConfig = {
    kernelName: (0, _tfjsCore.IsInf),
    backendName: "cpu",
    kernelFunc: isInf
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gKXUy":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "isNaN", ()=>isNaN);
parcelHelpers.export(exports, "isNaNConfig", ()=>isNaNConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const isNaN = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.IsNan), (xi)=>Number.isNaN(xi) ? 1 : 0, "bool");
const isNaNConfig = {
    kernelName: (0, _tfjsCore.IsNan),
    backendName: "cpu",
    kernelFunc: isNaN
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3aMpD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "linSpace", ()=>linSpace);
parcelHelpers.export(exports, "linSpaceConfig", ()=>linSpaceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _linSpaceImpl = require("./LinSpace_impl");
function linSpace(args) {
    const { backend, attrs } = args;
    const { start, stop, num } = attrs;
    const outVals = (0, _linSpaceImpl.linSpaceImpl)(start, stop, num);
    return backend.makeTensorInfo([
        outVals.length
    ], "float32", outVals);
}
const linSpaceConfig = {
    kernelName: (0, _tfjsCore.LinSpace),
    backendName: "cpu",
    kernelFunc: linSpace
};

},{"@tensorflow/tfjs-core":"21Ftl","./LinSpace_impl":"4tRcP","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bNfse":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "log1p", ()=>log1p);
parcelHelpers.export(exports, "log1pConfig", ()=>log1pConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const log1p = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Log1p), (xi)=>Math.log1p(xi));
const log1pConfig = {
    kernelName: (0, _tfjsCore.Log1p),
    backendName: "cpu",
    kernelFunc: log1p
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9q52j":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalAndImpl", ()=>logicalAndImpl);
parcelHelpers.export(exports, "logicalAnd", ()=>logicalAnd);
parcelHelpers.export(exports, "logicalAndConfig", ()=>logicalAndConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const logicalAndImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a && b);
const logicalAnd = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.LogicalAnd), logicalAndImpl, null, "bool");
const logicalAndConfig = {
    kernelName: (0, _tfjsCore.LogicalAnd),
    backendName: "cpu",
    kernelFunc: logicalAnd
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"929wA":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalNot", ()=>logicalNot);
parcelHelpers.export(exports, "logicalNotConfig", ()=>logicalNotConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const logicalNot = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.LogicalNot), (xi)=>xi ? 0 : 1, "bool");
const logicalNotConfig = {
    kernelName: (0, _tfjsCore.LogicalNot),
    backendName: "cpu",
    kernelFunc: logicalNot
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4bpjj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "logicalOrImpl", ()=>logicalOrImpl);
parcelHelpers.export(exports, "logicalOr", ()=>logicalOr);
parcelHelpers.export(exports, "logicalOrConfig", ()=>logicalOrConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const logicalOrImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>a || b);
const logicalOr = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.LogicalOr), logicalOrImpl, null, "bool");
const logicalOrConfig = {
    kernelName: (0, _tfjsCore.LogicalOr),
    backendName: "cpu",
    kernelFunc: logicalOr
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1ZDWc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "lRN", ()=>lRN);
parcelHelpers.export(exports, "LRNConfig", ()=>LRNConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function lRN(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { depthRadius, bias, alpha, beta } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "LRN");
    const channels = x.shape[3];
    const maxD = channels - 1;
    const xValues = backend.data.get(x.dataId).values;
    const size = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const result = new Float32Array(size);
    function sumAcrossChannels(offset) {
        const currentChannel = offset % channels;
        let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
        const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);
        let sum = 0.0;
        for(; beginSumOffset <= endSumOffset; beginSumOffset++){
            const z = xValues[beginSumOffset];
            sum += z * z;
        }
        return sum;
    }
    for(let offset = 0; offset < size; offset++){
        const sum = sumAcrossChannels(offset);
        const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);
        result[offset] = val;
    }
    return backend.makeTensorInfo(x.shape, x.dtype, result);
}
const LRNConfig = {
    kernelName: (0, _tfjsCore.LRN),
    backendName: "cpu",
    kernelFunc: lRN
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"01nUa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "lRNGrad", ()=>lRNGrad);
parcelHelpers.export(exports, "LRNGradConfig", ()=>LRNGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function lRNGrad(args) {
    const { inputs, backend, attrs } = args;
    const { x, y, dy } = inputs;
    const { depthRadius, bias, alpha, beta } = attrs;
    (0, _cpuUtil.assertNotComplex)(dy, "LRNGrad");
    const dySize = (0, _tfjsCore.util).sizeFromShape(dy.shape);
    const channels = dy.shape[3];
    const dyValues = backend.data.get(dy.dataId).values;
    const xValues = backend.data.get(x.dataId).values;
    const yValues = backend.data.get(y.dataId).values;
    const result = new Float32Array(dySize);
    const size = dySize;
    for(let offset = 0; offset < size; offset++){
        const currentChannel = offset % channels;
        const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
        const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);
        let norm = 0;
        for(let k = depthBegin; k < depthEnd; k++)norm += Math.pow(xValues[k], 2);
        norm = alpha * norm + bias;
        for(let k = depthBegin; k < depthEnd; k++){
            let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm;
            if (offset === k) dyi += Math.pow(norm, -beta);
            dyi *= dyValues[offset];
            result[k] += dyi;
        }
    }
    return backend.makeTensorInfo(dy.shape, x.dtype, result);
}
const LRNGradConfig = {
    kernelName: (0, _tfjsCore.LRNGrad),
    backendName: "cpu",
    kernelFunc: lRNGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ceCEC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "max", ()=>max);
parcelHelpers.export(exports, "maxConfig", ()=>maxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _maxImpl = require("./Max_impl");
var _transposeImpl = require("./Transpose_impl");
function max(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { reductionIndices, keepDims } = attrs;
    const cpuBackend = backend;
    let xShape = x.shape;
    const xRank = xShape.length;
    const origAxes = (0, _tfjsCore.util).parseAxisParam(reductionIndices, xShape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, xRank);
    let xVals = cpuBackend.data.get(x.dataId).values;
    if (permutedAxes != null) {
        const newShape = new Array(xRank);
        for(let i = 0; i < newShape.length; i++)newShape[i] = xShape[permutedAxes[i]];
        xVals = (0, _transposeImpl.transposeImpl)(xVals, xShape, x.dtype, permutedAxes, newShape);
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, xRank);
        xShape = newShape;
    }
    (0, _cpuUtil.assertNotComplex)(x, "max");
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("max", axes, xRank);
    const [maxOutShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(xShape, axes);
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const result = (0, _maxImpl.maxImpl)(xVals, reduceSize, maxOutShape, x.dtype);
    const dataId = cpuBackend.write(result, maxOutShape, x.dtype);
    let outShape = maxOutShape;
    if (keepDims) {
        // reshape
        const newShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(maxOutShape, origAxes);
        outShape = newShape;
    }
    return {
        dataId,
        shape: outShape,
        dtype: x.dtype
    };
}
const maxConfig = {
    kernelName: (0, _tfjsCore.Max),
    backendName: "cpu",
    kernelFunc: max
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Max_impl":"6BNe1","./Transpose_impl":"2fFZe","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2HK6O":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool", ()=>maxPool);
parcelHelpers.export(exports, "maxPoolConfig", ()=>maxPoolConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _poolUtils = require("../utils/pool_utils");
var _identity = require("./Identity");
function maxPool(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    (0, _cpuUtil.assertNotComplex)(x, "maxPool");
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const dilations = 1;
    (0, _tfjsCore.util).assert((0, _tfjsCore.backend_util).eitherStridesOrDilationsAreOne(strides, dilations), ()=>"Error in maxPool: Either strides or dilations must be 1. " + `Got strides ${strides} and dilations '${dilations}'`);
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode);
    let res;
    if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && (0, _tfjsCore.util).arraysEqual(convInfo.inShape, convInfo.outShape)) res = (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    else {
        const xValues = backend.data.get(x.dataId).values;
        const strides = (0, _tfjsCore.util).computeStrides(x.shape);
        const buffer = (0, _poolUtils.pool)(xValues, x.shape, x.dtype, strides, convInfo, "max");
        res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values);
    }
    return res;
}
const maxPoolConfig = {
    kernelName: (0, _tfjsCore.MaxPool),
    backendName: "cpu",
    kernelFunc: maxPool
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/pool_utils":"fzyhw","./Identity":"btREJ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"guFsS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool3D", ()=>maxPool3D);
parcelHelpers.export(exports, "maxPool3DConfig", ()=>maxPool3DConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _poolUtils = require("../utils/pool_utils");
function maxPool3D(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { filterSize, strides, pad, dimRoundingMode, dataFormat } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "maxPool3d");
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(x.shape, filterSize, strides, 1 /* dilations */ , pad, dimRoundingMode, dataFormat);
    const xValues = backend.data.get(x.dataId).values;
    const outBuf = (0, _poolUtils.pool3d)(xValues, x.shape, x.dtype, (0, _tfjsCore.util).computeStrides(x.shape), convInfo, "max");
    return backend.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
}
const maxPool3DConfig = {
    kernelName: (0, _tfjsCore.MaxPool3D),
    backendName: "cpu",
    kernelFunc: maxPool3D
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/pool_utils":"fzyhw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bsOhi":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPool3DGrad", ()=>maxPool3DGrad);
parcelHelpers.export(exports, "maxPool3DGradConfig", ()=>maxPool3DGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _poolUtils = require("../utils/pool_utils");
function maxPool3DGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input } = inputs;
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        input
    ], "maxPool3DGrad");
    const convInfo = (0, _tfjsCore.backend_util).computePool3DInfo(input.shape, filterSize, strides, 1 /* dilations */ , pad, dimRoundingMode);
    const inputBuf = backend.bufferSync(input);
    const maxPosBuf = (0, _poolUtils.maxPool3dPositions)(inputBuf, convInfo);
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationDepth = convInfo.dilationDepth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterDepth = convInfo.effectiveFilterDepth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = (0, _tfjsCore.buffer)(input.shape, "float32");
    const dyBuf = backend.bufferSync(dy);
    for(let batch = 0; batch < convInfo.batchSize; ++batch){
        for(let channel = 0; channel < convInfo.inChannels; ++channel)for(let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth){
            for(let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow)for(let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol){
                // Shader code begins
                const dyDepthCorner = dxDepth - padFront;
                const dyRowCorner = dxRow - padTop;
                const dyColCorner = dxCol - padLeft;
                let dotProd = 0;
                for(let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth){
                    const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
                    if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) continue;
                    for(let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight){
                        const dyRow = (dyRowCorner + wRow) / strideHeight;
                        if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) continue;
                        for(let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth){
                            const dyCol = (dyColCorner + wCol) / strideWidth;
                            if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) continue;
                            const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);
                            const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;
                            const mask = maxPos === curPos ? 1 : 0;
                            if (mask === 0) continue;
                            const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
                            dotProd += pixel * mask;
                        }
                    }
                }
                dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
const maxPool3DGradConfig = {
    kernelName: (0, _tfjsCore.MaxPool3DGrad),
    backendName: "cpu",
    kernelFunc: maxPool3DGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/pool_utils":"fzyhw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"esvnZ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPoolGrad", ()=>maxPoolGrad);
parcelHelpers.export(exports, "maxPoolGradConfig", ()=>maxPoolGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _poolUtils = require("../utils/pool_utils");
function maxPoolGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input, output } = inputs;
    const x = input;
    (0, _cpuUtil.assertNotComplex)([
        input,
        output
    ], "maxPoolGrad");
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */ , pad, dimRoundingMode);
    const xValues = backend.data.get(x.dataId).values;
    const maxPosBuf = (0, _tfjsCore.buffer)(convInfo.outShape, x.dtype, (0, _poolUtils.maxPoolPositions)(xValues, x.shape, x.dtype, convInfo).values);
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = (0, _tfjsCore.buffer)(x.shape, "float32");
    const dyData = backend.data.get(dy.dataId).values;
    const dyBuf = (0, _tfjsCore.buffer)(dy.shape, "float32", dyData);
    for(let b = 0; b < convInfo.batchSize; ++b)for(let d = 0; d < convInfo.inChannels; ++d){
        for(let dxR = 0; dxR < convInfo.inHeight; ++dxR)for(let dxC = 0; dxC < convInfo.inWidth; ++dxC){
            // Shader code begins.
            const dyRCorner = dxR - padTop;
            const dyCCorner = dxC - padLeft;
            let dotProd = 0;
            for(let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight){
                const dyR = (dyRCorner + wR) / strideHeight;
                if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) continue;
                for(let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth){
                    const dyC = (dyCCorner + wC) / strideWidth;
                    if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) continue;
                    const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);
                    const curPos = wR * effectiveFilterWidth + wC;
                    const mask = maxPos === curPos ? 1 : 0;
                    if (mask === 0) continue;
                    const pixel = dyBuf.get(b, dyR, dyC, d);
                    dotProd += pixel * mask;
                }
            }
            dx.set(dotProd, b, dxR, dxC, d);
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
const maxPoolGradConfig = {
    kernelName: (0, _tfjsCore.MaxPoolGrad),
    backendName: "cpu",
    kernelFunc: maxPoolGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","../utils/pool_utils":"fzyhw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aBn34":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPoolWithArgmaxConfig", ()=>maxPoolWithArgmaxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _maxPoolWithArgmaxImpl = require("./MaxPoolWithArgmax_impl");
const maxPoolWithArgmaxConfig = {
    kernelName: (0, _tfjsCore.MaxPoolWithArgmax),
    backendName: "cpu",
    kernelFunc: ({ inputs, attrs, backend })=>{
        const { x } = inputs;
        const { filterSize, strides, pad, includeBatchInIndex } = attrs;
        const cpuBackend = backend;
        (0, _cpuUtil.assertNotComplex)(x, "MaxPoolWithArgmax");
        const values = cpuBackend.data.get(x.dataId).values;
        const convInfo = (0, _tfjsCore.backend_util).computePool2DInfo(x.shape, filterSize, strides, [
            1,
            1
        ], pad);
        const [pooled, indexes] = (0, _maxPoolWithArgmaxImpl.maxPoolWithArgmaxImpl)(values, x.shape, x.dtype, includeBatchInIndex, convInfo);
        const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);
        const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);
        return [
            {
                dataId: pooledDataId,
                shape: convInfo.outShape,
                dtype: x.dtype
            },
            {
                dataId: indexesDataId,
                shape: convInfo.outShape,
                dtype: "int32"
            }
        ];
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./MaxPoolWithArgmax_impl":"7OAHL","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7OAHL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "maxPoolWithArgmaxImpl", ()=>maxPoolWithArgmaxImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _poolUtils = require("../utils/pool_utils");
function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {
    const strides = (0, _tfjsCore.util).computeStrides(xShape);
    const maxPools = (0, _poolUtils.pool)(xValues, xShape, dtype, strides, convInfo, "max");
    const maxPositions = (0, _poolUtils.maxPoolPositions)(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);
    return [
        maxPools.values,
        maxPositions.values
    ];
}

},{"@tensorflow/tfjs-core":"21Ftl","../utils/pool_utils":"fzyhw","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hu1l4":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mean", ()=>mean);
parcelHelpers.export(exports, "meanConfig", ()=>meanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cast = require("./Cast");
var _realDiv = require("./RealDiv");
var _sum = require("./Sum");
function mean(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    const axes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    const shapes = (0, _tfjsCore.backend_util).computeOutAndReduceShapes(x.shape, axes);
    const reduceShape = shapes[1];
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const toDispose = [];
    const reduceSizeScalar = backend.makeTensorInfo([], "float32", new Float32Array([
        reduceSize
    ]));
    toDispose.push(reduceSizeScalar);
    const $x = (0, _cast.cast)({
        inputs: {
            x
        },
        backend,
        attrs: {
            dtype: "float32"
        }
    });
    toDispose.push($x);
    const res = (0, _realDiv.div)({
        inputs: {
            a: $x,
            b: reduceSizeScalar
        },
        backend
    });
    toDispose.push(res);
    const result = (0, _sum.sum)({
        inputs: {
            x: res
        },
        backend,
        attrs: {
            axis,
            keepDims
        }
    });
    toDispose.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return result;
}
const meanConfig = {
    kernelName: (0, _tfjsCore.Mean),
    backendName: "cpu",
    kernelFunc: mean
};

},{"@tensorflow/tfjs-core":"21Ftl","./Cast":"fAtIw","./RealDiv":"9Ly75","./Sum":"6gbbV","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"h3WES":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "min", ()=>min);
parcelHelpers.export(exports, "minConfig", ()=>minConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function min(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "min");
    const origAxes = (0, _tfjsCore.util).parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = (0, _tfjsCore.backend_util).getAxesPermutation(axes, x.shape.length);
    let $x = x;
    if (permutedAxes != null) {
        $x = (0, _transpose.transpose)({
            inputs: {
                x
            },
            backend,
            attrs: {
                perm: permutedAxes
            }
        });
        axes = (0, _tfjsCore.backend_util).getInnerMostAxes(axes.length, x.shape.length);
    }
    (0, _tfjsCore.backend_util).assertAxesAreInnerMostDims("min", axes, $x.shape.length);
    const [outShape, reduceShape] = (0, _tfjsCore.backend_util).computeOutAndReduceShapes($x.shape, axes);
    const reduceSize = (0, _tfjsCore.util).sizeFromShape(reduceShape);
    const vals = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape(outShape), $x.dtype);
    const aVals = backend.data.get($x.dataId).values;
    for(let i = 0; i < vals.length; ++i){
        const offset = i * reduceSize;
        let min = aVals[offset];
        for(let j = 0; j < reduceSize; ++j){
            const value = aVals[offset + j];
            if (Number.isNaN(value) || value < min) min = value;
        }
        vals[i] = min;
    }
    if (permutedAxes != null) backend.disposeIntermediateTensorInfo($x);
    const result = backend.makeTensorInfo(outShape, $x.dtype, vals);
    if (keepDims) {
        const expandedShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(outShape, origAxes);
        const reshapedResult = (0, _reshape.reshape)({
            inputs: {
                x: result
            },
            backend,
            attrs: {
                shape: expandedShape
            }
        });
        backend.disposeIntermediateTensorInfo(result);
        return reshapedResult;
    }
    return result;
}
const minConfig = {
    kernelName: (0, _tfjsCore.Min),
    backendName: "cpu",
    kernelFunc: min
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Reshape":"9350i","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1HbpJ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "mirrorPad", ()=>mirrorPad);
parcelHelpers.export(exports, "mirrorPadConfig", ()=>mirrorPadConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function mirrorPad(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { paddings, mode } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "mirrorPad");
    const outShape = paddings.map((p, i)=>p[0] + x.shape[i] + p[1]);
    const start = paddings.map((p)=>p[0]);
    const end = paddings.map((p, i)=>p[0] + x.shape[i]);
    const offset = mode === "reflect" ? 0 : 1;
    const xVals = backend.data.get(x.dataId).values;
    const xRank = x.shape.length;
    const xStrides = (0, _tfjsCore.util).computeStrides(x.shape);
    const resultSize = (0, _tfjsCore.util).sizeFromShape(outShape);
    const resultRank = outShape.length;
    const resultStrides = (0, _tfjsCore.util).computeStrides(outShape);
    const resVals = (0, _tfjsCore.util).getTypedArrayFromDType(x.dtype, resultSize);
    for(let i = 0; i < resultSize; i++){
        let coords = (0, _tfjsCore.util).indexToLoc(i, resultRank, resultStrides);
        for(let i = 0; i < resultRank; i++){
            if (coords[i] < start[i]) coords[i] = start[i] * 2 - coords[i] - offset;
            else if (coords[i] >= end[i]) coords[i] = (end[i] - 1) * 2 - coords[i] + offset;
        }
        coords = coords.map((c, i)=>c - start[i]);
        const inIndex = (0, _tfjsCore.util).locToIndex(coords, xRank, xStrides);
        resVals[i] = xVals[inIndex];
    }
    const outId = backend.write(resVals, outShape, x.dtype);
    return {
        dataId: outId,
        shape: outShape,
        dtype: x.dtype
    };
}
const mirrorPadConfig = {
    kernelName: (0, _tfjsCore.MirrorPad),
    backendName: "cpu",
    kernelFunc: mirrorPad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iD4b6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "modImpl", ()=>modImpl);
parcelHelpers.export(exports, "mod", ()=>mod);
parcelHelpers.export(exports, "modConfig", ()=>modConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const modImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((aValue, bValue)=>{
    const rem = aValue % bValue;
    if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) return rem;
    else return (rem + bValue) % bValue;
});
const mod = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Mod), modImpl);
const modConfig = {
    kernelName: (0, _tfjsCore.Mod),
    backendName: "cpu",
    kernelFunc: mod
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dy4Sw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "multinomial", ()=>multinomial);
parcelHelpers.export(exports, "multinomialConfig", ()=>multinomialConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _seedrandom = require("seedrandom");
var _cpuUtil = require("../cpu_util");
var _softmax = require("./Softmax");
function multinomial(args) {
    const { inputs, backend, attrs } = args;
    const { logits } = inputs;
    const { numSamples, seed, normalized } = attrs;
    (0, _cpuUtil.assertNotComplex)(logits, "multinomial");
    const probabilities = normalized ? logits : (0, _softmax.softmax)({
        inputs: {
            logits
        },
        backend,
        attrs: {
            dim: -1
        }
    });
    const batchSize = probabilities.shape[0];
    const numEvents = probabilities.shape[1];
    const probVals = backend.data.get(probabilities.dataId).values;
    const resShape = [
        batchSize,
        numSamples
    ];
    const resVals = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape(resShape), "int32");
    for(let b = 0; b < batchSize; ++b){
        const offset = b * numEvents;
        // The cdf won't include the last event. It will be implicit if no other
        // event happened.
        const cdf = new Float32Array(numEvents - 1);
        cdf[0] = probVals[offset];
        for(let event = 1; event < cdf.length; ++event)cdf[event] = cdf[event - 1] + probVals[offset + event];
        const random = _seedrandom.alea(seed.toString());
        const outOffset = b * numSamples;
        for(let sampleId = 0; sampleId < numSamples; ++sampleId){
            const r = random();
            // Assume last event happened by default.
            resVals[outOffset + sampleId] = cdf.length;
            for(let event = 0; event < cdf.length; event++)if (r < cdf[event]) {
                resVals[outOffset + sampleId] = event;
                break;
            }
        }
    }
    if (!normalized) backend.disposeIntermediateTensorInfo(probabilities);
    return backend.makeTensorInfo(resShape, "int32", resVals);
}
const multinomialConfig = {
    kernelName: (0, _tfjsCore.Multinomial),
    backendName: "cpu",
    kernelFunc: multinomial
};

},{"@tensorflow/tfjs-core":"21Ftl","seedrandom":"9SiBG","../cpu_util":"1l037","./Softmax":"1UnfO","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1UnfO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "softmax", ()=>softmax);
parcelHelpers.export(exports, "softmaxConfig", ()=>softmaxConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _exp = require("./Exp");
var _max = require("./Max");
var _realDiv = require("./RealDiv");
var _reshape = require("./Reshape");
var _sub = require("./Sub");
var _sum = require("./Sum");
function softmax(args) {
    const { inputs, backend, attrs } = args;
    const { logits } = inputs;
    const { dim } = attrs;
    const logitsRank = logits.shape.length;
    let $dim = dim;
    if ($dim === -1) $dim = logitsRank - 1;
    if ($dim !== logitsRank - 1) throw Error("Softmax along a non-last dimension is not yet supported. " + `Logits was rank ${logitsRank} and dim was ${$dim}`);
    const axes = (0, _tfjsCore.util).parseAxisParam([
        $dim
    ], logits.shape);
    const maxLogit = (0, _max.max)({
        inputs: {
            x: logits
        },
        backend,
        attrs: {
            reductionIndices: axes,
            keepDims: false
        }
    });
    const expandedShape = (0, _tfjsCore.backend_util).expandShapeToKeepDim(maxLogit.shape, axes);
    const maxLogitReshaped = (0, _reshape.reshape)({
        inputs: {
            x: maxLogit
        },
        backend,
        attrs: {
            shape: expandedShape
        }
    });
    const a = (0, _sub.sub)({
        inputs: {
            a: logits,
            b: maxLogitReshaped
        },
        backend
    });
    const b = (0, _exp.exp)({
        inputs: {
            x: a
        },
        backend
    });
    const sumExp = (0, _sum.sum)({
        inputs: {
            x: b
        },
        backend,
        attrs: {
            axis: axes,
            keepDims: false
        }
    });
    const sumReshaped = (0, _reshape.reshape)({
        inputs: {
            x: sumExp
        },
        backend,
        attrs: {
            shape: expandedShape
        }
    });
    const result = (0, _realDiv.div)({
        inputs: {
            a: b,
            b: sumReshaped
        },
        backend
    });
    backend.disposeIntermediateTensorInfo(maxLogit);
    backend.disposeIntermediateTensorInfo(maxLogitReshaped);
    backend.disposeIntermediateTensorInfo(a);
    backend.disposeIntermediateTensorInfo(b);
    backend.disposeIntermediateTensorInfo(sumExp);
    backend.disposeIntermediateTensorInfo(sumReshaped);
    return result;
}
const softmaxConfig = {
    kernelName: (0, _tfjsCore.Softmax),
    backendName: "cpu",
    kernelFunc: softmax
};

},{"@tensorflow/tfjs-core":"21Ftl","./Exp":"6eexr","./Max":"ceCEC","./RealDiv":"9Ly75","./Reshape":"9350i","./Sub":"4DByI","./Sum":"6gbbV","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cU4dv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV3", ()=>nonMaxSuppressionV3);
parcelHelpers.export(exports, "nonMaxSuppressionV3Config", ()=>nonMaxSuppressionV3Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
const nonMaxSuppressionV3Impl = (0, _tfjsCore.kernel_impls).nonMaxSuppressionV3Impl;
function nonMaxSuppressionV3(args) {
    const { inputs, backend, attrs } = args;
    const { boxes, scores } = inputs;
    const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
    (0, _cpuUtil.assertNotComplex)(boxes, "NonMaxSuppression");
    const boxesVals = backend.data.get(boxes.dataId).values;
    const scoresVals = backend.data.get(scores.dataId).values;
    const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
    return backend.makeTensorInfo([
        selectedIndices.length
    ], "int32", new Int32Array(selectedIndices));
}
const nonMaxSuppressionV3Config = {
    kernelName: (0, _tfjsCore.NonMaxSuppressionV3),
    backendName: "cpu",
    kernelFunc: nonMaxSuppressionV3
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"chYiL":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV4", ()=>nonMaxSuppressionV4);
parcelHelpers.export(exports, "nonMaxSuppressionV4Config", ()=>nonMaxSuppressionV4Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
const nonMaxSuppressionV4Impl = (0, _tfjsCore.kernel_impls).nonMaxSuppressionV4Impl;
function nonMaxSuppressionV4(args) {
    const { inputs, backend, attrs } = args;
    const { boxes, scores } = inputs;
    const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
    (0, _cpuUtil.assertNotComplex)(boxes, "NonMaxSuppressionPadded");
    const boxesVals = backend.data.get(boxes.dataId).values;
    const scoresVals = backend.data.get(scores.dataId).values;
    const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
    return [
        backend.makeTensorInfo([
            selectedIndices.length
        ], "int32", new Int32Array(selectedIndices)),
        backend.makeTensorInfo([], "int32", new Int32Array([
            validOutputs
        ]))
    ];
}
const nonMaxSuppressionV4Config = {
    kernelName: (0, _tfjsCore.NonMaxSuppressionV4),
    backendName: "cpu",
    kernelFunc: nonMaxSuppressionV4
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bVnt1":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "nonMaxSuppressionV5", ()=>nonMaxSuppressionV5);
parcelHelpers.export(exports, "nonMaxSuppressionV5Config", ()=>nonMaxSuppressionV5Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
const nonMaxSuppressionV5Impl = (0, _tfjsCore.kernel_impls).nonMaxSuppressionV5Impl;
function nonMaxSuppressionV5(args) {
    const { inputs, backend, attrs } = args;
    const { boxes, scores } = inputs;
    const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
    (0, _cpuUtil.assertNotComplex)(boxes, "NonMaxSuppressionWithScore");
    const boxesVals = backend.data.get(boxes.dataId).values;
    const scoresVals = backend.data.get(scores.dataId).values;
    const maxOutputSizeVal = maxOutputSize;
    const iouThresholdVal = iouThreshold;
    const scoreThresholdVal = scoreThreshold;
    const softNmsSigmaVal = softNmsSigma;
    const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
    return [
        backend.makeTensorInfo([
            selectedIndices.length
        ], "int32", new Int32Array(selectedIndices)),
        backend.makeTensorInfo([
            selectedScores.length
        ], "float32", new Float32Array(selectedScores))
    ];
}
const nonMaxSuppressionV5Config = {
    kernelName: (0, _tfjsCore.NonMaxSuppressionV5),
    backendName: "cpu",
    kernelFunc: nonMaxSuppressionV5
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9Evwj":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "oneHot", ()=>oneHot);
parcelHelpers.export(exports, "oneHotConfig", ()=>oneHotConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function oneHot(args) {
    const { inputs, backend, attrs } = args;
    const { indices } = inputs;
    const { dtype, depth, onValue, offValue } = attrs;
    (0, _cpuUtil.assertNotComplex)(indices, "oneHot");
    const indicesSize = (0, _tfjsCore.util).sizeFromShape(indices.shape);
    const res = new Float32Array(indicesSize * depth);
    res.fill(offValue);
    const indicesVal = backend.data.get(indices.dataId).values;
    for(let event = 0; event < indicesSize; ++event)if (indicesVal[event] >= 0 && indicesVal[event] < depth) res[event * depth + indicesVal[event]] = onValue;
    return backend.makeTensorInfo([
        ...indices.shape,
        depth
    ], dtype, res);
}
const oneHotConfig = {
    kernelName: (0, _tfjsCore.OneHot),
    backendName: "cpu",
    kernelFunc: oneHot
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kWIOw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "onesLike", ()=>onesLike);
parcelHelpers.export(exports, "onesLikeConfig", ()=>onesLikeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _complex = require("./Complex");
var _fill = require("./Fill");
var _imag = require("./Imag");
var _real = require("./Real");
var _zerosLike = require("./ZerosLike");
function onesLike(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    if (x.dtype === "string") throw new Error("onesLike is not supported for string tensors");
    else if (x.dtype === "complex64") {
        const realPart = (0, _real.real)({
            inputs: {
                input: x
            },
            backend
        });
        const r = onesLike({
            inputs: {
                x: realPart
            },
            backend
        });
        const imagPart = (0, _imag.imag)({
            inputs: {
                input: x
            },
            backend
        });
        const i = (0, _zerosLike.zerosLike)({
            inputs: {
                x: imagPart
            },
            backend
        });
        const result = (0, _complex.complex)({
            inputs: {
                real: r,
                imag: i
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(realPart);
        backend.disposeIntermediateTensorInfo(r);
        backend.disposeIntermediateTensorInfo(imagPart);
        backend.disposeIntermediateTensorInfo(i);
        return result;
    } else return (0, _fill.fill)({
        backend,
        attrs: {
            shape: x.shape,
            value: 1,
            dtype: x.dtype
        }
    });
}
const onesLikeConfig = {
    kernelName: (0, _tfjsCore.OnesLike),
    backendName: "cpu",
    kernelFunc: onesLike
};

},{"@tensorflow/tfjs-core":"21Ftl","./Complex":"3DPmh","./Fill":"4BMgR","./Imag":"af3Tx","./Real":"eZ7aJ","./ZerosLike":"hfVJr","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hfVJr":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "zerosLike", ()=>zerosLike);
parcelHelpers.export(exports, "zerosLikeConfig", ()=>zerosLikeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _complex = require("./Complex");
var _fill = require("./Fill");
var _imag = require("./Imag");
var _real = require("./Real");
function zerosLike(args) {
    const { inputs, backend } = args;
    const { x } = inputs;
    if (x.dtype === "string") throw new Error("zerosLike is not supported for string tensors");
    else if (x.dtype === "complex64") {
        const realPart = (0, _real.real)({
            inputs: {
                input: x
            },
            backend
        });
        const r = zerosLike({
            inputs: {
                x: realPart
            },
            backend
        });
        const imagPart = (0, _imag.imag)({
            inputs: {
                input: x
            },
            backend
        });
        const i = zerosLike({
            inputs: {
                x: imagPart
            },
            backend
        });
        const result = (0, _complex.complex)({
            inputs: {
                real: r,
                imag: i
            },
            backend
        });
        backend.disposeIntermediateTensorInfo(realPart);
        backend.disposeIntermediateTensorInfo(r);
        backend.disposeIntermediateTensorInfo(imagPart);
        backend.disposeIntermediateTensorInfo(i);
        return result;
    } else return (0, _fill.fill)({
        backend,
        attrs: {
            shape: x.shape,
            value: 0,
            dtype: x.dtype
        }
    });
}
const zerosLikeConfig = {
    kernelName: (0, _tfjsCore.ZerosLike),
    backendName: "cpu",
    kernelFunc: zerosLike
};

},{"@tensorflow/tfjs-core":"21Ftl","./Complex":"3DPmh","./Fill":"4BMgR","./Imag":"af3Tx","./Real":"eZ7aJ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3NDYm":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "pack", ()=>pack);
parcelHelpers.export(exports, "packConfig", ()=>packConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _concat = require("./Concat");
var _expandDims = require("./ExpandDims");
function pack(args) {
    const { inputs, backend, attrs } = args;
    const { axis } = attrs;
    if (inputs.length === 1) return (0, _expandDims.expandDims)({
        inputs: {
            input: inputs[0]
        },
        backend,
        attrs: {
            dim: axis
        }
    });
    const shape = inputs[0].shape;
    const dtype = inputs[0].dtype;
    inputs.forEach((t)=>{
        (0, _tfjsCore.util).assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
        (0, _tfjsCore.util).assert(dtype === t.dtype, ()=>"All tensors passed to stack must have matching dtypes");
    });
    const intermediateTensorInfos = [];
    const expandedTensors = inputs.map((t)=>{
        const expandedT = (0, _expandDims.expandDims)({
            inputs: {
                input: t
            },
            backend,
            attrs: {
                dim: axis
            }
        });
        intermediateTensorInfos.push(expandedT);
        return expandedT;
    });
    const result = (0, _concat.concat)({
        inputs: expandedTensors,
        backend,
        attrs: {
            axis
        }
    });
    intermediateTensorInfos.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return result;
}
const packConfig = {
    kernelName: (0, _tfjsCore.Pack),
    backendName: "cpu",
    kernelFunc: pack
};

},{"@tensorflow/tfjs-core":"21Ftl","./Concat":"iYEAT","./ExpandDims":"4z77l","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dssNb":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "padV2", ()=>padV2);
parcelHelpers.export(exports, "padV2Config", ()=>padV2Config);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function padV2(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { paddings, constantValue } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "pad");
    const outShape = paddings.map((p, i)=>p[0] + x.shape[i] + p[1]);
    const start = paddings.map((p)=>p[0]);
    const xVals = backend.data.get(x.dataId).values;
    const xSize = (0, _tfjsCore.util).sizeFromShape(x.shape);
    const xRank = x.shape.length;
    const xStrides = (0, _tfjsCore.util).computeStrides(x.shape);
    const resultSize = (0, _tfjsCore.util).sizeFromShape(outShape);
    const resultRank = outShape.length;
    const resultStrides = (0, _tfjsCore.util).computeStrides(outShape);
    const resVals = (0, _tfjsCore.util).getTypedArrayFromDType(x.dtype, resultSize);
    if (constantValue !== 0) resVals.fill(constantValue);
    for(let i = 0; i < xSize; i++){
        const coords = (0, _tfjsCore.util).indexToLoc(i, xRank, xStrides);
        const outCoords = coords.map((c, i)=>c + start[i]);
        const outIndex = (0, _tfjsCore.util).locToIndex(outCoords, resultRank, resultStrides);
        resVals[outIndex] = xVals[i];
    }
    const outId = backend.write(resVals, outShape, x.dtype);
    return {
        dataId: outId,
        shape: outShape,
        dtype: x.dtype
    };
}
const padV2Config = {
    kernelName: (0, _tfjsCore.PadV2),
    backendName: "cpu",
    kernelFunc: padV2
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jx4g8":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "powImpl", ()=>powImpl);
parcelHelpers.export(exports, "pow", ()=>pow);
parcelHelpers.export(exports, "powConfig", ()=>powConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _binaryImpl = require("../utils/binary_impl");
var _binaryUtils = require("../utils/binary_utils");
const powImpl = (0, _binaryImpl.createSimpleBinaryKernelImpl)((a, b)=>Math.pow(a, b));
const pow = (0, _binaryUtils.binaryKernelFunc)((0, _tfjsCore.Pow), powImpl);
const powConfig = {
    kernelName: (0, _tfjsCore.Pow),
    backendName: "cpu",
    kernelFunc: pow
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/binary_impl":"7WPlP","../utils/binary_utils":"dUQGx","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4jEHO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedGather", ()=>raggedGather);
parcelHelpers.export(exports, "raggedGatherConfig", ()=>raggedGatherConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _raggedGatherImpl = require("./RaggedGather_impl");
function raggedGather(args) {
    const { inputs, backend, attrs } = args;
    const { paramsNestedSplits, paramsDenseValues, indices } = inputs;
    const { outputRaggedRank } = attrs;
    const $paramsNestedSplits = paramsNestedSplits.map((t)=>backend.data.get(t.dataId).values);
    const $paramsNestedSplitsShapes = paramsNestedSplits.map((t)=>t.shape);
    const $paramsDenseValues = backend.data.get(paramsDenseValues.dataId).values;
    const $indices = backend.data.get(indices.dataId).values;
    const [outputNestedSplits, outputDenseValues, outputDenseValuesShape] = (0, _raggedGatherImpl.raggedGatherImpl)($paramsNestedSplits, $paramsNestedSplitsShapes, $paramsDenseValues, paramsDenseValues.shape, paramsDenseValues.dtype, $indices, indices.shape, outputRaggedRank);
    const outputNestedSplitsTensors = outputNestedSplits.map((splits)=>backend.makeTensorInfo([
            splits.length
        ], "int32", splits));
    const outputDenseValuesTensor = backend.makeTensorInfo(outputDenseValuesShape, paramsDenseValues.dtype, outputDenseValues);
    return outputNestedSplitsTensors.concat([
        outputDenseValuesTensor
    ]);
}
const raggedGatherConfig = {
    kernelName: (0, _tfjsCore.RaggedGather),
    backendName: "cpu",
    kernelFunc: raggedGather
};

},{"@tensorflow/tfjs-core":"21Ftl","./RaggedGather_impl":"59HGn","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kFVpX":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedRange", ()=>raggedRange);
parcelHelpers.export(exports, "raggedRangeConfig", ()=>raggedRangeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _raggedRangeImpl = require("./RaggedRange_impl");
function raggedRange(args) {
    const { inputs, backend } = args;
    const { starts, limits, deltas } = inputs;
    const $starts = backend.data.get(starts.dataId).values;
    const $limits = backend.data.get(limits.dataId).values;
    const $deltas = backend.data.get(deltas.dataId).values;
    const [rtNestedSplitsData, rtDenseValuesData] = (0, _raggedRangeImpl.raggedRangeImpl)($starts, starts.shape, starts.dtype, $limits, limits.shape, $deltas, deltas.shape);
    const rtNestedSplits = backend.makeTensorInfo([
        rtNestedSplitsData.length
    ], "int32", rtNestedSplitsData);
    const rtDenseValues = backend.makeTensorInfo([
        rtDenseValuesData.length
    ], starts.dtype, rtDenseValuesData);
    return [
        rtNestedSplits,
        rtDenseValues
    ];
}
const raggedRangeConfig = {
    kernelName: (0, _tfjsCore.RaggedRange),
    backendName: "cpu",
    kernelFunc: raggedRange
};

},{"@tensorflow/tfjs-core":"21Ftl","./RaggedRange_impl":"l2R3s","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bJlTR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "raggedTensorToTensor", ()=>raggedTensorToTensor);
parcelHelpers.export(exports, "raggedTensorToTensorConfig", ()=>raggedTensorToTensorConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _raggedTensorToTensorImpl = require("./RaggedTensorToTensor_impl");
function raggedTensorToTensor(args) {
    const { inputs, backend, attrs } = args;
    const { shape, values, defaultValue, rowPartitionTensors } = inputs;
    const { rowPartitionTypes } = attrs;
    const $shape = backend.data.get(shape.dataId).values;
    const $values = backend.data.get(values.dataId).values;
    const $defaultValue = backend.data.get(defaultValue.dataId).values;
    const $rowPartitionValues = rowPartitionTensors.map((t)=>backend.data.get(t.dataId).values);
    const rowPartitionValuesShapes = rowPartitionTensors.map((t)=>t.shape);
    const [outputShape, output] = (0, _raggedTensorToTensorImpl.raggedTensorToTensorImpl)($shape, shape.shape, $values, values.shape, values.dtype, $defaultValue, defaultValue.shape, $rowPartitionValues, rowPartitionValuesShapes, rowPartitionTypes);
    return backend.makeTensorInfo(outputShape, values.dtype, output);
}
const raggedTensorToTensorConfig = {
    kernelName: (0, _tfjsCore.RaggedTensorToTensor),
    backendName: "cpu",
    kernelFunc: raggedTensorToTensor
};

},{"@tensorflow/tfjs-core":"21Ftl","./RaggedTensorToTensor_impl":"jDwSG","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"1Dov9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "range", ()=>range);
parcelHelpers.export(exports, "rangeConfig", ()=>rangeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _rangeImpl = require("./Range_impl");
function range(args) {
    const { backend, attrs } = args;
    const { start, stop, dtype, step } = attrs;
    const values = (0, _rangeImpl.rangeImpl)(start, stop, step, dtype);
    return backend.makeTensorInfo([
        values.length
    ], dtype, values);
}
const rangeConfig = {
    kernelName: (0, _tfjsCore.Range),
    backendName: "cpu",
    kernelFunc: range
};

},{"@tensorflow/tfjs-core":"21Ftl","./Range_impl":"gJNJp","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"3BhEV":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reciprocal", ()=>reciprocal);
parcelHelpers.export(exports, "reciprocalConfig", ()=>reciprocalConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const reciprocal = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Reciprocal), (xi)=>1 / xi);
const reciprocalConfig = {
    kernelName: (0, _tfjsCore.Reciprocal),
    backendName: "cpu",
    kernelFunc: reciprocal
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kU0RB":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeBilinear", ()=>resizeBilinear);
parcelHelpers.export(exports, "resizeBilinearConfig", ()=>resizeBilinearConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function resizeBilinear(args) {
    const { inputs, backend, attrs } = args;
    const { images } = inputs;
    const { alignCorners, halfPixelCenters, size } = attrs;
    (0, _cpuUtil.assertNotComplex)(images, "resizeBilinear");
    const imagesStrides = (0, _tfjsCore.util).computeStrides(images.shape);
    const [newHeight, newWidth] = size;
    const [batch, oldHeight, oldWidth, numChannels] = images.shape;
    const xValues = backend.data.get(images.dataId).values;
    const result = new Float32Array((0, _tfjsCore.util).sizeFromShape([
        batch,
        newHeight,
        newWidth,
        numChannels
    ]));
    const effectiveInputSize = [
        alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
        alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
    ];
    const effectiveOutputSize = [
        alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
        alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
    ];
    let outputIdx = 0;
    const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
    const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
    for(let b = 0; b < batch; b++)for(let r = 0; r < newHeight; r++){
        let sourceFracRow;
        if (halfPixelCenters) sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;
        else sourceFracRow = effectiveRowSizeRatio * r;
        const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
        const rowFrac = sourceFracRow - sourceRowFloor;
        const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
        const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];
        const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];
        for(let c = 0; c < newWidth; c++){
            let sourceFracCol;
            if (halfPixelCenters) sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;
            else sourceFracCol = effectiveColSizeRatio * c;
            const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));
            const colFrac = sourceFracCol - sourceColFloor;
            const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
            const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];
            const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];
            const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];
            const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];
            for(let d = 0; d < numChannels; d++){
                // Begin shader.
                // Compute the fractional index of the source.
                const topLeft = xValues[topLeftOffest + d];
                const bottomLeft = xValues[botLeftOffset + d];
                const topRight = xValues[topRightOffset + d];
                const bottomRight = xValues[botRightOffest + d];
                const top = topLeft + (topRight - topLeft) * colFrac;
                const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
                const newValue = top + (bottom - top) * rowFrac;
                result[outputIdx++] = newValue;
            }
        }
    }
    return backend.makeTensorInfo([
        batch,
        newHeight,
        newWidth,
        numChannels
    ], "float32", result);
}
const resizeBilinearConfig = {
    kernelName: (0, _tfjsCore.ResizeBilinear),
    backendName: "cpu",
    kernelFunc: resizeBilinear
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"76Mq3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeBilinearGrad", ()=>resizeBilinearGrad);
parcelHelpers.export(exports, "resizeBilinearGradConfig", ()=>resizeBilinearGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function resizeBilinearGrad(args) {
    const { inputs, backend, attrs } = args;
    const { images, dy } = inputs;
    const { alignCorners } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        images
    ], "resizeBilinearGrad");
    const imagesStrides = (0, _tfjsCore.util).computeStrides(images.shape);
    const [batch, xHeight, xWidth, depth] = images.shape;
    const [, yHeight, yWidth] = dy.shape;
    const output = new Float32Array(batch * xHeight * xWidth * depth);
    // In the backwards pass, we want to find the pixels that were generated
    // for each pixel in the input image the forward pass and add the
    // corresponding coefficient from dy to the gradient (with some
    // interpolation).
    const effectiveXSize = [
        alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
        alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
    ];
    const effectiveYSize = [
        alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
        alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
    ];
    const heightScale = effectiveXSize[0] / effectiveYSize[0];
    const widthScale = effectiveXSize[1] / effectiveYSize[1];
    // Reference implementation
    // tslint:disable-next-line:max-line-length
    // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275
    const dyValues = backend.data.get(dy.dataId).values;
    let offset = 0;
    for(let b = 0; b < batch; b++){
        const bOffset = b * imagesStrides[0];
        for(let r = 0; r < yHeight; r++){
            const dxR = r * heightScale;
            const topDxRIndex = Math.floor(dxR);
            const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
            const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
            const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];
            const dxRLerp = dxR - topDxRIndex;
            const inverseDxRLerp = 1.0 - dxRLerp;
            for(let c = 0; c < yWidth; c++){
                const dxC = c * widthScale;
                const leftDxCIndex = Math.floor(dxC);
                const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
                const dxCLerp = dxC - leftDxCIndex;
                const inverseDxCLerp = 1.0 - dxCLerp;
                const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];
                const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];
                const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];
                const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];
                const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
                const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
                const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
                const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
                for(let d = 0; d < depth; d++){
                    const dyVal = dyValues[offset++];
                    output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;
                    output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
                    output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;
                    output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
                }
            }
        }
    }
    return backend.makeTensorInfo([
        batch,
        xWidth,
        xHeight,
        depth
    ], "float32", output);
}
const resizeBilinearGradConfig = {
    kernelName: (0, _tfjsCore.ResizeBilinearGrad),
    backendName: "cpu",
    kernelFunc: resizeBilinearGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"5c1lG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeNearestNeighbor", ()=>resizeNearestNeighbor);
parcelHelpers.export(exports, "resizeNearestNeighborConfig", ()=>resizeNearestNeighborConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function resizeNearestNeighbor(args) {
    const { inputs, backend, attrs } = args;
    const { images } = inputs;
    const { alignCorners, halfPixelCenters, size } = attrs;
    (0, _cpuUtil.assertNotComplex)(images, "resizeNearestNeighbor");
    const imagesStrides = (0, _tfjsCore.util).computeStrides(images.shape);
    const [newHeight, newWidth] = size;
    const [batch, oldHeight, oldWidth, numChannels] = images.shape;
    const xValues = backend.data.get(images.dataId).values;
    const output = new Float32Array(batch * newHeight * newWidth * numChannels);
    const effectiveInputSize = [
        alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
        alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
    ];
    const effectiveOutputSize = [
        alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
        alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
    ];
    const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
    const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
    let outputOffset = 0;
    for(let b = 0; b < batch; b++){
        const batchOffset = b * imagesStrides[0];
        for(let r = 0; r < newHeight; r++){
            const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r;
            let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
            if (halfPixelCenters) sourceNearestRow = Math.max(0, sourceNearestRow);
            const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
            for(let c = 0; c < newWidth; c++){
                const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;
                let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
                if (halfPixelCenters) sourceNearestCol = Math.max(0, sourceNearestCol);
                const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
                for(let d = 0; d < numChannels; d++){
                    // Begin shader.
                    // Compute the fractional index of the source.
                    const newVal = xValues[colOffset + d];
                    output[outputOffset++] = newVal;
                }
            }
        }
    }
    return backend.makeTensorInfo([
        batch,
        newHeight,
        newWidth,
        numChannels
    ], images.dtype, output);
}
const resizeNearestNeighborConfig = {
    kernelName: (0, _tfjsCore.ResizeNearestNeighbor),
    backendName: "cpu",
    kernelFunc: resizeNearestNeighbor
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"4hhu6":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "resizeNearestNeighborGrad", ()=>resizeNearestNeighborGrad);
parcelHelpers.export(exports, "resizeNearestNeighborGradConfig", ()=>resizeNearestNeighborGradConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function resizeNearestNeighborGrad(args) {
    const { inputs, backend, attrs } = args;
    const { images, dy } = inputs;
    const { alignCorners } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        dy,
        images
    ], "resizeNearestNeighborGrad");
    const imagesStrides = (0, _tfjsCore.util).computeStrides(images.shape);
    const dyStrides = (0, _tfjsCore.util).computeStrides(dy.shape);
    const [batch, xHeight, xWidth, depth] = images.shape;
    const [, yHeight, yWidth] = dy.shape;
    const output = new Float32Array(batch * xHeight * xWidth * depth);
    const dyValues = backend.data.get(dy.dataId).values;
    // In the backwards pass, we want to find the pixels that were generated
    // for each pixel in the input image the forward pass
    const effectiveXSize = [
        alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
        alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
    ];
    const effectiveYSize = [
        alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
        alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
    ];
    const heightScale = effectiveXSize[0] / effectiveYSize[0];
    const widthScale = effectiveXSize[1] / effectiveYSize[1];
    const invHeightScale = 1 / heightScale;
    const invWidthScale = 1 / widthScale;
    // This defines the size of the window of values around a particular
    // index in dy that we want to search for contributions to dx.
    const winHeight = Math.ceil(invHeightScale) * 2 + 2;
    const winWidth = Math.ceil(invWidthScale) * 2 + 2;
    // Loop over the output space.
    for(let b = 0; b < batch; b++){
        const batchOffset = b * imagesStrides[0];
        for(let r = 0; r < xHeight; r++){
            const rowOffset = batchOffset + r * imagesStrides[1];
            // Compute bounds for where in dy we will look
            const startRLerp = Math.floor(r * invHeightScale);
            const startDyR = Math.floor(startRLerp - winHeight / 2);
            for(let c = 0; c < xWidth; c++){
                const colOffset = rowOffset + c * imagesStrides[2];
                // Compute bounds for where in dy we will look
                const startCLerp = Math.floor(c * invWidthScale);
                const startDyC = Math.floor(startCLerp - winWidth / 2);
                for(let d = 0; d < depth; d++){
                    let accum = 0;
                    // loop over dy
                    for(let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++){
                        const dyR = dyRIndex + startDyR;
                        // Guard against the window exceeding the bounds of dy
                        if (dyR < 0 || dyR >= yHeight) continue;
                        const dyROffset = batchOffset + dyR * dyStrides[1];
                        const sourceFracRow = dyR * heightScale;
                        const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
                        if (r !== sourceNearestRow) continue;
                        for(let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++){
                            const dyC = dyCIndex + startDyC;
                            // Guard against the window exceeding the bounds of dy
                            if (dyC < 0 || dyC >= yWidth) continue;
                            const dyCOffset = dyROffset + dyC * dyStrides[2];
                            const sourceFracCol = dyC * widthScale;
                            const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
                            if (c === sourceNearestCol) accum += dyValues[dyCOffset + d];
                        }
                    }
                    output[colOffset + d] = accum;
                }
            }
        }
    }
    return backend.makeTensorInfo(images.shape, images.dtype, output);
}
const resizeNearestNeighborGradConfig = {
    kernelName: (0, _tfjsCore.ResizeNearestNeighborGrad),
    backendName: "cpu",
    kernelFunc: resizeNearestNeighborGrad
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"ff3xk":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "reverse", ()=>reverse);
parcelHelpers.export(exports, "reverseConfig", ()=>reverseConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _identity = require("./Identity");
function reverse(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { dims } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "reverse");
    const xRank = x.shape.length;
    const $dims = (0, _tfjsCore.util).parseAxisParam(dims, x.shape);
    if (xRank === 0) return (0, _identity.identity)({
        inputs: {
            x
        },
        backend
    });
    const outBuf = new (0, _tfjsCore.TensorBuffer)(x.shape, x.dtype);
    const xBuf = backend.bufferSync(x);
    for(let i = 0; i < outBuf.size; i++){
        const outLoc = outBuf.indexToLoc(i);
        const inLoc = outLoc.slice();
        $dims.forEach((d)=>inLoc[d] = x.shape[d] - 1 - inLoc[d]);
        outBuf.set(xBuf.get(...inLoc), ...outLoc);
    }
    return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
}
const reverseConfig = {
    kernelName: (0, _tfjsCore.Reverse),
    backendName: "cpu",
    kernelFunc: reverse
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Identity":"btREJ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"d9nWG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "rotateWithOffsetConfig", ()=>rotateWithOffsetConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
const rotateWithOffsetConfig = {
    kernelName: (0, _tfjsCore.RotateWithOffset),
    backendName: "cpu",
    kernelFunc: ({ inputs, attrs, backend })=>{
        const { image } = inputs;
        const { radians, fillValue, center } = attrs;
        const cpuBackend = backend;
        const output = (0, _tfjsCore.util).getTypedArrayFromDType(image.dtype, (0, _tfjsCore.util).sizeFromShape(image.shape));
        const [batch, imageHeight, imageWidth, numChannels] = image.shape;
        const [centerX, centerY] = (0, _tfjsCore.backend_util).getImageCenter(center, imageHeight, imageWidth);
        const fullOpacityValue = 255;
        const sinFactor = Math.sin(radians);
        const cosFactor = Math.cos(radians);
        const imageVals = cpuBackend.data.get(image.dataId).values;
        for(let batchIdx = 0; batchIdx < batch; batchIdx++){
            const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
            for(let row = 0; row < imageHeight; row++){
                const rowOffset = row * (imageWidth * numChannels);
                for(let col = 0; col < imageWidth; col++){
                    const colOffset = col * numChannels;
                    for(let channel = 0; channel < numChannels; channel++){
                        const coords = [
                            batch,
                            row,
                            col,
                            channel
                        ];
                        const x = coords[2];
                        const y = coords[1];
                        // coordX/coordY are the result of rotating and translating x/y.
                        let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
                        let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
                        coordX = Math.round(coordX + centerX);
                        coordY = Math.round(coordY + centerY);
                        let outputValue = fillValue;
                        if (typeof fillValue !== "number") {
                            if (channel === 3) outputValue = fullOpacityValue;
                            else outputValue = fillValue[channel];
                        }
                        // If the coordinate position falls within the image boundaries...
                        if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {
                            // set the output to the image value at the coordinate position.
                            const rotatedRowOffset = coordY * (imageWidth * numChannels);
                            const rotatedColOffset = coordX * numChannels;
                            const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;
                            outputValue = imageVals[imageIdx];
                        }
                        const outIdx = batchOffset + rowOffset + colOffset + channel;
                        output[outIdx] = outputValue;
                    }
                }
            }
        }
        const dataId = cpuBackend.write(output, image.shape, image.dtype);
        return {
            dataId,
            shape: image.shape,
            dtype: image.dtype
        };
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"gnp2v":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "round", ()=>round);
parcelHelpers.export(exports, "roundConfig", ()=>roundConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const round = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Round), (xi)=>{
    // The algorithm is based on banker's rounding.
    const base = Math.floor(xi);
    if (xi - base < 0.5) return Math.floor(xi);
    else if (xi - base > 0.5) return Math.ceil(xi);
    else {
        if (base % 2.0 === 0.0) return base;
        else return base + 1.0;
    }
});
const roundConfig = {
    kernelName: (0, _tfjsCore.Round),
    backendName: "cpu",
    kernelFunc: round
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bb9Cv":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "scatterNd", ()=>scatterNd);
parcelHelpers.export(exports, "scatterNdConfig", ()=>scatterNdConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _scatterImpl = require("./Scatter_impl");
function scatterNd(args) {
    const { inputs, backend, attrs } = args;
    const { indices, updates } = inputs;
    const { shape } = attrs;
    const { sliceRank, numUpdates, sliceSize, strides, outputSize } = (0, _tfjsCore.backend_util).calculateShapes(updates, indices, shape);
    const sumDupeIndices = true;
    const indicesBuf = backend.bufferSync(indices);
    const updatesBuf = backend.bufferSync(updates);
    const outBuf = (0, _scatterImpl.scatterImpl)(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0 /* defaultValue */ , sumDupeIndices);
    return backend.makeTensorInfo(shape, outBuf.dtype, outBuf.values);
}
const scatterNdConfig = {
    kernelName: (0, _tfjsCore.ScatterNd),
    backendName: "cpu",
    kernelFunc: scatterNd
};

},{"@tensorflow/tfjs-core":"21Ftl","./Scatter_impl":"kMt6f","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"f7WfM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "searchSorted", ()=>searchSorted);
parcelHelpers.export(exports, "searchSortedConfig", ()=>searchSortedConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _searchSortedImpl = require("./SearchSorted_impl");
function searchSorted(args) {
    const { inputs, backend, attrs } = args;
    const { sortedSequence, values } = inputs;
    const { side } = attrs;
    const $sortedSequence = backend.data.get(sortedSequence.dataId).values;
    const $values = backend.data.get(values.dataId).values;
    const output = (0, _searchSortedImpl.searchSortedImpl)($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
    return backend.makeTensorInfo(values.shape, "int32", output);
}
const searchSortedConfig = {
    kernelName: (0, _tfjsCore.SearchSorted),
    backendName: "cpu",
    kernelFunc: searchSorted
};

},{"@tensorflow/tfjs-core":"21Ftl","./SearchSorted_impl":"2K7QF","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2K7QF":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "searchSortedImpl", ()=>searchSortedImpl);
var _tfjsCore = require("@tensorflow/tfjs-core");
function lowerBound(array, value) {
    let left = 0;
    let right = array.length;
    let mid = 0;
    while(left < right){
        mid = Math.floor((left + right) / 2);
        if (array[mid] < value) left = mid + 1;
        else right = mid;
    }
    return right;
}
function upperBound(array, value) {
    let left = 0;
    let right = array.length;
    let mid = 0;
    while(left < right){
        mid = Math.floor((left + right) / 2);
        if (array[mid] <= value) left = mid + 1;
        else right = mid;
    }
    return right;
}
function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {
    const output = (0, _tfjsCore.util).getArrayFromDType("int32", batchSize * numValues);
    for(let b = 0; b < batchSize; ++b){
        const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);
        const outputOffset = b * numValues;
        for(let i = 0; i < numValues; ++i)output[outputOffset + i] = side === "left" ? lowerBound(sortedInputsSlice, values[i + outputOffset]) : upperBound(sortedInputsSlice, values[i + outputOffset]);
    }
    return output;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"lDXuK":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "select", ()=>select);
parcelHelpers.export(exports, "selectConfig", ()=>selectConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
function select(args) {
    const { inputs, backend } = args;
    const { condition, t, e } = inputs;
    (0, _cpuUtil.assertNotComplex)([
        condition,
        t,
        e
    ], "select");
    const conditionRank = condition.shape.length;
    const values = backend.data.get(condition.dataId).values;
    const tValues = backend.data.get(t.dataId).values;
    const eValues = backend.data.get(e.dataId).values;
    const resultDtype = (0, _tfjsCore.upcastType)(t.dtype, e.dtype);
    const newValues = (0, _tfjsCore.util).makeZerosTypedArray((0, _tfjsCore.util).sizeFromShape(t.shape), resultDtype);
    let index = 0;
    const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : (0, _tfjsCore.util).sizeFromShape(t.shape.slice(1));
    for(let i = 0; i < values.length; i++){
        for(let j = 0; j < offset; j++)if (values[i] === 1) newValues[index++] = tValues[i];
        else newValues[index++] = eValues[i];
    }
    return backend.makeTensorInfo(t.shape, resultDtype, newValues);
}
const selectConfig = {
    kernelName: (0, _tfjsCore.Select),
    backendName: "cpu",
    kernelFunc: select
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cJWbo":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "selu", ()=>selu);
parcelHelpers.export(exports, "seluConfig", ()=>seluConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const scaleAlpha = (0, _tfjsCore.backend_util).SELU_SCALEALPHA;
const scale = (0, _tfjsCore.backend_util).SELU_SCALE;
const selu = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Selu), (xi)=>{
    if (xi >= 0) return scale * xi;
    else return scaleAlpha * (Math.exp(xi) - 1);
});
const seluConfig = {
    kernelName: (0, _tfjsCore.Selu),
    backendName: "cpu",
    kernelFunc: selu
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"erXNP":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sign", ()=>sign);
parcelHelpers.export(exports, "signConfig", ()=>signConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const sign = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Sign), (xi)=>{
    if (xi < 0) return -1;
    else if (xi > 0) return 1;
    else return 0;
});
const signConfig = {
    kernelName: (0, _tfjsCore.Sign),
    backendName: "cpu",
    kernelFunc: sign
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"c9it7":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sin", ()=>sin);
parcelHelpers.export(exports, "sinConfig", ()=>sinConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const sin = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Sin), (xi)=>Math.sin(xi));
const sinConfig = {
    kernelName: (0, _tfjsCore.Sin),
    backendName: "cpu",
    kernelFunc: sin
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9tr3d":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sinh", ()=>sinh);
parcelHelpers.export(exports, "sinhConfig", ()=>sinhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const sinh = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Sinh), (xi)=>Math.sinh(xi));
const sinhConfig = {
    kernelName: (0, _tfjsCore.Sinh),
    backendName: "cpu",
    kernelFunc: sinh
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bojBS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "softplus", ()=>softplus);
parcelHelpers.export(exports, "softplusConfig", ()=>softplusConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
// mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX
// epsilon is the difference between 1.0 and the next representable float.
// For a single precision 32 bit float this should be 2^-23, see:
// https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm
const epsilon = 1.1920928955078125e-7;
const threshold = Math.log(epsilon) + 2.0;
const softplus = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Softplus), (xi)=>{
    // Value above which exp(x) may overflow, but softplus(x) == x
    // is within machine epsilon.
    const tooLarge = xi > -threshold;
    // Value below which exp(x) may underflow, but softplus(x) == exp(x)
    // is within machine epsilon.
    const tooSmall = xi < threshold;
    const expX = Math.exp(xi);
    let result;
    if (tooSmall) result = expX;
    else if (tooLarge) result = xi;
    else result = Math.log(1.0 + expX);
    return result;
});
const softplusConfig = {
    kernelName: (0, _tfjsCore.Softplus),
    backendName: "cpu",
    kernelFunc: softplus
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cO9CD":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "spaceToBatchND", ()=>spaceToBatchND);
parcelHelpers.export(exports, "spaceToBatchNDConfig", ()=>spaceToBatchNDConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _padV2 = require("./PadV2");
var _reshape = require("./Reshape");
var _transpose = require("./Transpose");
function spaceToBatchND(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { blockShape, paddings } = attrs;
    (0, _cpuUtil.assertNotComplex)([
        x
    ], "spaceToBatchND");
    const prod = (0, _tfjsCore.util).sizeFromShape(blockShape);
    const completePaddings = [
        [
            0,
            0
        ]
    ];
    completePaddings.push(...paddings);
    for(let i = 1 + blockShape.length; i < x.shape.length; ++i)completePaddings.push([
        0,
        0
    ]);
    const paddedX = (0, _padV2.padV2Config).kernelFunc({
        inputs: {
            x
        },
        backend,
        attrs: {
            paddings: completePaddings,
            constantValue: 0
        }
    });
    const reshapedPaddedShape = (0, _tfjsCore.backend_util).getReshaped(paddedX.shape, blockShape, prod, false);
    const permutedReshapedPaddedPermutation = (0, _tfjsCore.backend_util).getPermuted(reshapedPaddedShape.length, blockShape.length, false);
    const flattenShape = (0, _tfjsCore.backend_util).getReshapedPermuted(paddedX.shape, blockShape, prod, false);
    const reshapeInputs = {
        x: paddedX
    };
    const reshapeAttrs = {
        shape: reshapedPaddedShape
    };
    const paddedXReshaped = (0, _reshape.reshape)({
        inputs: reshapeInputs,
        backend,
        attrs: reshapeAttrs
    });
    const transposeInputs = {
        x: paddedXReshaped
    };
    const transposeAttrs = {
        perm: permutedReshapedPaddedPermutation
    };
    const paddedXT = (0, _transpose.transpose)({
        inputs: transposeInputs,
        backend,
        attrs: transposeAttrs
    });
    const resultReshapeInputs = {
        x: paddedXT
    };
    const resultReshapeAttrs = {
        shape: flattenShape
    };
    const result = (0, _reshape.reshape)({
        inputs: resultReshapeInputs,
        backend,
        attrs: resultReshapeAttrs
    });
    backend.disposeIntermediateTensorInfo(paddedX);
    backend.disposeIntermediateTensorInfo(paddedXReshaped);
    backend.disposeIntermediateTensorInfo(paddedXT);
    return result;
}
const spaceToBatchNDConfig = {
    kernelName: (0, _tfjsCore.SpaceToBatchND),
    backendName: "cpu",
    kernelFunc: spaceToBatchND
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./PadV2":"dssNb","./Reshape":"9350i","./Transpose":"61ywv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cLZdR":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseFillEmptyRows", ()=>sparseFillEmptyRows);
parcelHelpers.export(exports, "sparseFillEmptyRowsConfig", ()=>sparseFillEmptyRowsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _sparseFillEmptyRowsImpl = require("./SparseFillEmptyRows_impl");
function sparseFillEmptyRows(args) {
    const { inputs, backend } = args;
    const { indices, values, denseShape, defaultValue } = inputs;
    if (denseShape.shape.length !== 1) throw new Error(`Dense shape must be a vector, saw:
        ${denseShape.shape}`);
    if (indices.shape.length !== 2) throw new Error(`Indices must be a matrix, saw:
        ${indices.shape}`);
    if (values.shape.length !== 1) throw new Error(`Values must be a vector, saw:
        ${values.shape}`);
    if (defaultValue.shape.length !== 0) throw new Error(`Default value must be a scalar, saw:
        ${defaultValue.shape}`);
    const $indices = backend.data.get(indices.dataId).values;
    const $values = backend.data.get(values.dataId).values;
    const $denseShape = backend.data.get(denseShape.dataId).values;
    const $defaultValue = backend.data.get(defaultValue.dataId).values[0];
    const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = (0, _sparseFillEmptyRowsImpl.sparseFillEmptyRowsImpl)($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
    return [
        backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
        backend.makeTensorInfo([
            outputIndicesShape[0]
        ], values.dtype, outputValues),
        backend.makeTensorInfo([
            emptyRowIndicator.length
        ], "bool", new Uint8Array(emptyRowIndicator.map((value)=>Number(value)))),
        backend.makeTensorInfo([
            reverseIndexMap.length
        ], indices.dtype, new Int32Array(reverseIndexMap))
    ];
}
const sparseFillEmptyRowsConfig = {
    kernelName: (0, _tfjsCore.SparseFillEmptyRows),
    backendName: "cpu",
    kernelFunc: sparseFillEmptyRows
};

},{"@tensorflow/tfjs-core":"21Ftl","./SparseFillEmptyRows_impl":"72f9e","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"bVjgS":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseReshape", ()=>sparseReshape);
parcelHelpers.export(exports, "sparseReshapeConfig", ()=>sparseReshapeConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _sparseReshapeImpl = require("./SparseReshape_impl");
function sparseReshape(args) {
    const { inputs, backend } = args;
    const { inputIndices, inputShape, newShape } = inputs;
    if (inputIndices.shape.length !== 2) throw new Error(`Input indices should be a matrix but received shape
        ${inputIndices.shape}`);
    if (inputShape.shape.length !== 1) throw new Error(`Input shape should be a vector but received shape
        ${inputShape.shape}`);
    if (newShape.shape.length !== 1) throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
    const $inputShape = Array.from(backend.data.get(inputShape.dataId).values);
    const $inputIndices = backend.data.get(inputIndices.dataId).values;
    const targetShape = Array.from(backend.data.get(newShape.dataId).values);
    const [newIndices, indicesShape, outputShape] = (0, _sparseReshapeImpl.sparseReshapeImpl)($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
    return [
        backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
        backend.makeTensorInfo([
            outputShape.length
        ], newShape.dtype, new Int32Array(outputShape))
    ];
}
const sparseReshapeConfig = {
    kernelName: (0, _tfjsCore.SparseReshape),
    backendName: "cpu",
    kernelFunc: sparseReshape
};

},{"@tensorflow/tfjs-core":"21Ftl","./SparseReshape_impl":"ecdf6","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7zRqG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseSegmentMean", ()=>sparseSegmentMean);
parcelHelpers.export(exports, "sparseSegmentMeanConfig", ()=>sparseSegmentMeanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _sparseSegmentReductionImpl = require("./SparseSegmentReduction_impl");
function sparseSegmentMean(args) {
    const { inputs, backend } = args;
    const { data, indices, segmentIds } = inputs;
    if (data.shape.length < 1) throw new Error(`Data should be at least 1 dimensional but received scalar`);
    if (indices.shape.length !== 1) throw new Error(`Indices should be a vector but received shape
          ${indices.shape}`);
    if (segmentIds.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape
          ${segmentIds.shape}`);
    if (indices.shape[0] !== segmentIds.shape[0]) throw new Error(`segmentIds and indices should have same size.`);
    const $data = backend.data.get(data.dataId).values;
    const $indices = backend.data.get(indices.dataId).values;
    const $segmentIds = backend.data.get(segmentIds.dataId).values;
    const [outputData, outputDataShape] = (0, _sparseSegmentReductionImpl.sparseSegmentReductionImpl)($data, data.shape, data.dtype, $indices, $segmentIds, true);
    return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
const sparseSegmentMeanConfig = {
    kernelName: (0, _tfjsCore.SparseSegmentMean),
    backendName: "cpu",
    kernelFunc: sparseSegmentMean
};

},{"@tensorflow/tfjs-core":"21Ftl","./SparseSegmentReduction_impl":"ie2pQ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"dMeLQ":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseSegmentSum", ()=>sparseSegmentSum);
parcelHelpers.export(exports, "sparseSegmentSumConfig", ()=>sparseSegmentSumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _sparseSegmentReductionImpl = require("./SparseSegmentReduction_impl");
function sparseSegmentSum(args) {
    const { inputs, backend } = args;
    const { data, indices, segmentIds } = inputs;
    if (data.shape.length < 1) throw new Error(`Data should be at least 1 dimensional but received scalar`);
    if (indices.shape.length !== 1) throw new Error(`Indices should be a vector but received shape
         ${indices.shape}`);
    if (segmentIds.shape.length !== 1) throw new Error(`Segment ids should be a vector but received shape
         ${segmentIds.shape}`);
    if (indices.shape[0] !== segmentIds.shape[0]) throw new Error(`segmentIds and indices should have same size.`);
    const $data = backend.data.get(data.dataId).values;
    const $indices = backend.data.get(indices.dataId).values;
    const $segmentIds = backend.data.get(segmentIds.dataId).values;
    const [outputData, outputDataShape] = (0, _sparseSegmentReductionImpl.sparseSegmentReductionImpl)($data, data.shape, data.dtype, $indices, $segmentIds);
    return backend.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
const sparseSegmentSumConfig = {
    kernelName: (0, _tfjsCore.SparseSegmentSum),
    backendName: "cpu",
    kernelFunc: sparseSegmentSum
};

},{"@tensorflow/tfjs-core":"21Ftl","./SparseSegmentReduction_impl":"ie2pQ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"kfHsN":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "sparseToDense", ()=>sparseToDense);
parcelHelpers.export(exports, "sparseToDenseConfig", ()=>sparseToDenseConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _scatterImpl = require("./Scatter_impl");
function sparseToDense(args) {
    const { inputs, backend, attrs } = args;
    const { sparseIndices, sparseValues, defaultValue } = inputs;
    const { outputShape } = attrs;
    const { sliceRank, numUpdates, sliceSize, strides, outputSize } = (0, _tfjsCore.backend_util).calculateShapes(sparseValues, sparseIndices, outputShape);
    const sumDupeIndices = false;
    const indicesBuf = backend.bufferSync(sparseIndices);
    let outBuf;
    switch(sparseValues.dtype){
        case "bool":
            {
                const updatesBuf = backend.bufferSync(sparseValues);
                const $defaultValue = Boolean(backend.data.get(defaultValue.dataId).values[0]);
                outBuf = (0, _scatterImpl.scatterImpl)(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
                break;
            }
        case "float32":
            {
                const updatesBuf = backend.bufferSync(sparseValues);
                const $defaultValue = backend.data.get(defaultValue.dataId).values[0];
                outBuf = (0, _scatterImpl.scatterImpl)(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
                break;
            }
        case "int32":
            {
                const updatesBuf = backend.bufferSync(sparseValues);
                const $defaultValue = backend.data.get(defaultValue.dataId).values[0];
                outBuf = (0, _scatterImpl.scatterImpl)(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
                break;
            }
        case "string":
            {
                const updatesBuf = backend.bufferSync(sparseValues);
                const $defaultValue = (0, _tfjsCore.util).decodeString(backend.data.get(defaultValue.dataId).values[0]);
                outBuf = (0, _scatterImpl.scatterImpl)(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
                break;
            }
        default:
            throw new Error(`Unsupported type ${sparseValues.dtype}`);
    }
    return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
}
const sparseToDenseConfig = {
    kernelName: (0, _tfjsCore.SparseToDense),
    backendName: "cpu",
    kernelFunc: sparseToDense
};

},{"@tensorflow/tfjs-core":"21Ftl","./Scatter_impl":"kMt6f","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"j2Sbu":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "splitV", ()=>splitV);
parcelHelpers.export(exports, "splitVConfig", ()=>splitVConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _slice = require("./Slice");
function splitV(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { numOrSizeSplits, axis } = attrs;
    const $axis = (0, _tfjsCore.util).parseAxisParam(axis, x.shape)[0];
    const splitSizes = (0, _tfjsCore.backend_util).prepareSplitSize(x, numOrSizeSplits, $axis);
    const begin = new Array(x.shape.length).fill(0);
    const size = x.shape.slice();
    return splitSizes.map((s)=>{
        const sliceSize = [
            ...size
        ];
        sliceSize[$axis] = s;
        const sliceT = (0, _slice.slice)({
            inputs: {
                x
            },
            backend,
            attrs: {
                begin,
                size: sliceSize
            }
        });
        begin[$axis] += s;
        return sliceT;
    });
}
const splitVConfig = {
    kernelName: (0, _tfjsCore.SplitV),
    backendName: "cpu",
    kernelFunc: splitV
};

},{"@tensorflow/tfjs-core":"21Ftl","./Slice":"9L9GN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jjLIw":[function(require,module,exports) {
/**
 * @license
 * Copyright 2019 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "squareConfig", ()=>squareConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
const squareConfig = {
    kernelName: (0, _tfjsCore.Square),
    backendName: "cpu",
    kernelFunc: ({ inputs, backend })=>{
        const { x } = inputs;
        const cpuBackend = backend;
        (0, _cpuUtil.assertNotComplex)(x, "square");
        const values = cpuBackend.data.get(x.dataId).values;
        const newValues = new Float32Array(values.length);
        for(let i = 0; i < values.length; ++i){
            const value = values[i];
            newValues[i] = value * value;
        }
        const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
        return {
            dataId,
            shape: x.shape,
            dtype: x.dtype
        };
    }
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"2cqHx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "step", ()=>step);
parcelHelpers.export(exports, "stepConfig", ()=>stepConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const step = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Step), (xi, attrs)=>{
    const stepAttrs = attrs;
    if (isNaN(xi)) return NaN;
    else return xi > 0 ? 1 : stepAttrs.alpha;
});
const stepConfig = {
    kernelName: (0, _tfjsCore.Step),
    backendName: "cpu",
    kernelFunc: step
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"l76iO":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stridedSlice", ()=>stridedSlice);
parcelHelpers.export(exports, "stridedSliceConfig", ()=>stridedSliceConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _reshape = require("./Reshape");
var _slice = require("./Slice");
var _stridedSliceImpl = require("./StridedSlice_impl");
function stridedSlice(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "stridedSlice");
    const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = (0, _tfjsCore.slice_util).sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
    let result;
    // ref:
    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/strided_slice_op.cc
    if (isIdentity) // Optimization #1, slice is a no-op plus reshape
    result = (0, _reshape.reshape)({
        inputs: {
            x
        },
        backend,
        attrs: {
            shape: finalShape
        }
    });
    else if (sliceDim0 || isSimpleSlice) {
        // Optimization #2, slice is memory contiguous (only occurs in dim 0)
        (0, _tfjsCore.util).assert(x.shape.length >= 1, ()=>`Input must have rank at least 1, got: ${x.shape.length}`);
        const size = (0, _tfjsCore.slice_util).computeOutShape($begin, $end, $strides);
        // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end).
        const sliced = (0, _slice.slice)({
            inputs: {
                x
            },
            backend,
            attrs: {
                begin: $begin,
                size
            }
        });
        result = (0, _reshape.reshape)({
            inputs: {
                x: sliced
            },
            backend,
            attrs: {
                shape: finalShape
            }
        });
        backend.disposeIntermediateTensorInfo(sliced);
    } else {
        const xBuf = backend.bufferSync(x);
        const outBuf = (0, _stridedSliceImpl.stridedSliceImpl)(finalShapeSparse, xBuf, $strides, $begin);
        result = backend.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);
    }
    return result;
}
const stridedSliceConfig = {
    kernelName: (0, _tfjsCore.StridedSlice),
    backendName: "cpu",
    kernelFunc: stridedSlice
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Reshape":"9350i","./Slice":"9L9GN","./StridedSlice_impl":"kCJo9","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"7Beq9":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringNGrams", ()=>stringNGrams);
parcelHelpers.export(exports, "stringNGramsConfig", ()=>stringNGramsConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _stringNGramsImpl = require("./StringNGrams_impl");
function stringNGrams(args) {
    const { inputs, backend, attrs } = args;
    const { separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences } = attrs;
    const { data, dataSplits } = inputs;
    const $data = backend.data.get(data.dataId).values;
    const $dataSplits = backend.data.get(dataSplits.dataId).values;
    const [nGrams, nGramsSplits] = (0, _stringNGramsImpl.stringNGramsImpl)($data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences);
    return [
        backend.makeTensorInfo([
            nGrams.length
        ], "string", nGrams),
        backend.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
    ];
}
const stringNGramsConfig = {
    kernelName: (0, _tfjsCore.StringNGrams),
    backendName: "cpu",
    kernelFunc: stringNGrams
};

},{"@tensorflow/tfjs-core":"21Ftl","./StringNGrams_impl":"3rAXp","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"6TsmC":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringSplit", ()=>stringSplit);
parcelHelpers.export(exports, "stringSplitConfig", ()=>stringSplitConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _stringSplitImpl = require("./StringSplit_impl");
function stringSplit(args) {
    const { inputs, backend, attrs } = args;
    const { skipEmpty } = attrs;
    const { input, delimiter } = inputs;
    if (input.dtype !== "string") throw new Error("Input must be of datatype string");
    if (input.shape.length !== 1) throw new Error(`Input must be a vector, got shape: ${input.shape}`);
    if (delimiter.shape.length !== 0) throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
    const $input = backend.data.get(input.dataId).values;
    const $delimiter = backend.data.get(delimiter.dataId).values[0];
    const [indices, values, shape] = (0, _stringSplitImpl.stringSplitImpl)($input, $delimiter, skipEmpty);
    const outputSize = values.length;
    return [
        backend.makeTensorInfo([
            outputSize,
            2
        ], "int32", indices),
        backend.makeTensorInfo([
            outputSize
        ], "string", values),
        backend.makeTensorInfo([
            2
        ], "int32", new Int32Array(shape))
    ];
}
const stringSplitConfig = {
    kernelName: (0, _tfjsCore.StringSplit),
    backendName: "cpu",
    kernelFunc: stringSplit
};

},{"@tensorflow/tfjs-core":"21Ftl","./StringSplit_impl":"8xmpZ","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"irRRx":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "stringToHashBucketFast", ()=>stringToHashBucketFast);
parcelHelpers.export(exports, "stringToHashBucketFastConfig", ()=>stringToHashBucketFastConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _stringToHashBucketFastImpl = require("./StringToHashBucketFast_impl");
function stringToHashBucketFast(args) {
    const { inputs, backend, attrs } = args;
    const { numBuckets } = attrs;
    const { input } = inputs;
    if (input.dtype !== "string") throw new Error("Input must be of datatype string");
    if (numBuckets <= 0) throw new Error(`Number of buckets must be at least 1`);
    const $input = backend.data.get(input.dataId).values;
    const output = (0, _stringToHashBucketFastImpl.stringToHashBucketFastImpl)($input, numBuckets);
    return backend.makeTensorInfo(input.shape, "int32", output);
}
const stringToHashBucketFastConfig = {
    kernelName: (0, _tfjsCore.StringToHashBucketFast),
    backendName: "cpu",
    kernelFunc: stringToHashBucketFast
};

},{"@tensorflow/tfjs-core":"21Ftl","./StringToHashBucketFast_impl":"6jwLv","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"aMIl2":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tan", ()=>tan);
parcelHelpers.export(exports, "tanConfig", ()=>tanConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const tan = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Tan), (xi)=>Math.tan(xi));
const tanConfig = {
    kernelName: (0, _tfjsCore.Tan),
    backendName: "cpu",
    kernelFunc: tan
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"fdqiG":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tanh", ()=>tanh);
parcelHelpers.export(exports, "tanhConfig", ()=>tanhConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _unaryUtils = require("../utils/unary_utils");
const tanh = (0, _unaryUtils.unaryKernelFunc)((0, _tfjsCore.Tanh), (xi)=>Math.tanh(xi));
const tanhConfig = {
    kernelName: (0, _tfjsCore.Tanh),
    backendName: "cpu",
    kernelFunc: tanh
};

},{"@tensorflow/tfjs-core":"21Ftl","../utils/unary_utils":"5johK","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"jFIwa":[function(require,module,exports) {
/**
 * @license
 * Copyright 2022 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tensorScatterUpdate", ()=>tensorScatterUpdate);
parcelHelpers.export(exports, "tensorScatterUpdateConfig", ()=>tensorScatterUpdateConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _scatterImpl = require("./Scatter_impl");
function tensorScatterUpdate(args) {
    const { inputs, backend } = args;
    const { tensor, indices, updates } = inputs;
    const { sliceRank, numUpdates, sliceSize, strides, outputSize } = (0, _tfjsCore.backend_util).calculateShapes(updates, indices, tensor.shape);
    const sumDupeIndices = false;
    const indicesBuf = backend.bufferSync(indices);
    const updatesBuf = backend.bufferSync(updates);
    const tensorBuf = backend.bufferSync(tensor);
    const outBuf = (0, _scatterImpl.scatterImpl)(indicesBuf, updatesBuf, tensor.shape, outputSize, sliceSize, numUpdates, sliceRank, strides, tensorBuf, sumDupeIndices);
    return backend.makeTensorInfo(tensor.shape, outBuf.dtype, outBuf.values);
}
const tensorScatterUpdateConfig = {
    kernelName: (0, _tfjsCore.TensorScatterUpdate),
    backendName: "cpu",
    kernelFunc: tensorScatterUpdate
};

},{"@tensorflow/tfjs-core":"21Ftl","./Scatter_impl":"kMt6f","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"cYsKW":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "tile", ()=>tile);
parcelHelpers.export(exports, "tileConfig", ()=>tileConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _tileImpl = require("./Tile_impl");
function tile(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { reps } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "tile");
    const outBuf = (0, _tileImpl.tileImpl)(backend.bufferSync(x), reps);
    return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
}
const tileConfig = {
    kernelName: (0, _tfjsCore.Tile),
    backendName: "cpu",
    kernelFunc: tile
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Tile_impl":"7g2re","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9RDRk":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "topK", ()=>topK);
parcelHelpers.export(exports, "topKConfig", ()=>topKConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _topKImpl = require("./TopK_impl");
function topK(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { k, sorted } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "topk");
    const xVals = backend.data.get(x.dataId).values;
    const [allTopKVals, allTopKIndices] = (0, _topKImpl.topKImpl)(xVals, x.shape, x.dtype, k, sorted);
    return [
        backend.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
        backend.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
    ];
}
const topKConfig = {
    kernelName: (0, _tfjsCore.TopK),
    backendName: "cpu",
    kernelFunc: topK
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./TopK_impl":"dl1sf","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"hKZ0R":[function(require,module,exports) {
/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "transform", ()=>transform);
parcelHelpers.export(exports, "transformConfig", ()=>transformConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
function transform(args) {
    const { inputs, attrs, backend } = args;
    const { image, transforms } = inputs;
    const { interpolation, fillMode, fillValue, outputShape } = attrs;
    const [batch, imageHeight, imageWidth, numChannels] = image.shape;
    const [outHeight, outWidth] = outputShape != null ? outputShape : [
        imageHeight,
        imageWidth
    ];
    const outShape = [
        batch,
        outHeight,
        outWidth,
        numChannels
    ];
    const inStrides = (0, _tfjsCore.util).computeStrides(image.shape);
    const batchInStride = inStrides[0];
    const rowInStride = inStrides[1];
    const colInStride = inStrides[2];
    const outStrides = (0, _tfjsCore.util).computeStrides(outShape);
    const batchOutStride = outStrides[0];
    const rowOutStride = outStrides[1];
    const colOutStride = outStrides[2];
    const outVals = (0, _tfjsCore.util).getTypedArrayFromDType(image.dtype, (0, _tfjsCore.util).sizeFromShape(outShape));
    outVals.fill(fillValue);
    const imageVals = backend.data.get(image.dataId).values;
    const transformVals = backend.data.get(transforms.dataId).values;
    // Ref TF implementation:
    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.h
    for(let b = 0; b < batch; ++b){
        const transform = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);
        for(let outY = 0; outY < outHeight; ++outY){
            for(let outX = 0; outX < outWidth; ++outX)for(let channel = 0; channel < numChannels; ++channel){
                let val;
                const projection = transform[6] * outX + transform[7] * outY + 1;
                if (projection === 0) continue;
                const inX = (transform[0] * outX + transform[1] * outY + transform[2]) / projection;
                const inY = (transform[3] * outX + transform[4] * outY + transform[5]) / projection;
                const x = mapCoord(inX, imageWidth, fillMode);
                const y = mapCoord(inY, imageHeight, fillMode);
                switch(interpolation){
                    case "nearest":
                        val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);
                        break;
                    case "bilinear":
                        val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchInStride, rowInStride, colInStride, b, y, x, channel, fillValue);
                        break;
                    default:
                        throw new Error(`Error in Transform: Expect 'nearest' or ` + `'bilinear', but got ${interpolation}`);
                }
                const ind = b * batchOutStride + outY * rowOutStride + outX * colOutStride + channel;
                outVals[ind] = val;
            }
        }
        return backend.makeTensorInfo(outShape, image.dtype, outVals);
    }
    const dataId = backend.write(outVals, outShape, image.dtype);
    return {
        dataId,
        shape: image.shape,
        dtype: image.dtype
    };
}
const transformConfig = {
    kernelName: (0, _tfjsCore.Transform),
    backendName: "cpu",
    kernelFunc: transform
};
function mapCoord(outCoord, len, mode) {
    switch(mode){
        case "reflect":
            return mapCoordReflect(outCoord, len);
        case "wrap":
            return mapCoordWrap(outCoord, len);
        case "nearest":
            return mapCoordNearest(outCoord, len);
        case "constant":
        default:
            return mapCoordConstant(outCoord, len);
    }
}
function mapCoordReflect(outCoord, len) {
    // Reflect [abcd] to [dcba|abcd|dcba].
    let inCoord = outCoord;
    if (inCoord < 0) {
        if (len <= 1) inCoord = 0;
        else {
            const sz2 = 2 * len;
            if (inCoord < sz2) inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
            inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
        }
    } else if (inCoord > len - 1) {
        if (len <= 1) inCoord = 0;
        else {
            const sz2 = 2 * len;
            inCoord -= sz2 * Math.trunc(inCoord / sz2);
            if (inCoord >= len) inCoord = sz2 - inCoord - 1;
        }
    }
    // clamp is necessary because when outCoord = 3.5 and len = 4,
    // inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
    return (0, _tfjsCore.util).clamp(0, inCoord, len - 1);
}
function mapCoordWrap(outCoord, len) {
    // Wrap [abcd] to [abcd|abcd|abcd].
    let inCoord = outCoord;
    if (inCoord < 0) {
        if (len <= 1) inCoord = 0;
        else {
            const sz = len - 1;
            inCoord += len * (Math.trunc(-inCoord / sz) + 1);
        }
    } else if (inCoord > len - 1) {
        if (len <= 1) inCoord = 0;
        else {
            const sz = len - 1;
            inCoord -= len * Math.trunc(inCoord / sz);
        }
    }
    // clamp is necessary because when outCoord = -0.5 and len = 4,
    // inCoord = 3.5 and will be rounded to 4 in nearest interpolation.
    return (0, _tfjsCore.util).clamp(0, inCoord, len - 1);
}
function mapCoordConstant(outCoord, len) {
    return outCoord;
}
function mapCoordNearest(outCoord, len) {
    return (0, _tfjsCore.util).clamp(0, outCoord, len - 1);
}
function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
    const ind = batch * batchStride + y * rowStride + x * colStride + channel;
    if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) return imageVals[ind];
    else return fillValue;
}
function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
    const $y = Math.round(y);
    const $x = Math.round(x);
    return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);
}
function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
    const yFloor = Math.floor(y);
    const xFloor = Math.floor(x);
    const yCeil = yFloor + 1;
    const xCeil = xFloor + 1;
    // f(x, yFloor) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yFloor)
    //               + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yFloor)
    const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);
    // f(x, yCeil) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yCeil)
    //             + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yCeil)
    const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);
    // f(x, y) = (yCeil - y) / (yCeil - yFloor) * f(x, yFloor)
    //         + (y - yFloor) / (yCeil - yFloor) * f(x, yCeil)
    return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
}

},{"@tensorflow/tfjs-core":"21Ftl","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"9nca3":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the License);
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an AS IS BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unique", ()=>unique);
parcelHelpers.export(exports, "uniqueConfig", ()=>uniqueConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _uniqueImpl = require("./Unique_impl");
function unique(args) {
    const { inputs, attrs, backend } = args;
    const { axis } = attrs;
    const { x } = inputs;
    (0, _cpuUtil.assertNotComplex)(x, "unique");
    const values = backend.data.get(x.dataId).values;
    const { outputValues, outputShape, indices } = (0, _uniqueImpl.uniqueImpl)(values, axis, x.shape, x.dtype);
    return [
        backend.makeTensorInfo(outputShape, x.dtype, outputValues),
        backend.makeTensorInfo([
            indices.length
        ], "int32", indices)
    ];
}
const uniqueConfig = {
    kernelName: (0, _tfjsCore.Unique),
    backendName: "cpu",
    kernelFunc: unique
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Unique_impl":"gLexL","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"iIwqM":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unpack", ()=>unpack);
parcelHelpers.export(exports, "unpackConfig", ()=>unpackConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _reshape = require("./Reshape");
var _slice = require("./Slice");
function unpack(args) {
    const { inputs, backend, attrs } = args;
    const { value } = inputs;
    let { axis } = attrs;
    if (axis < 0) axis += value.shape.length;
    const valueRank = value.shape.length;
    const num = value.shape[axis];
    const outShape = new Array(valueRank - 1);
    let outIndex = 0;
    for(let i = 0; i < valueRank; i++)if (i !== axis) outShape[outIndex++] = value.shape[i];
    const begin = new Array(valueRank).fill(0);
    const size = value.shape.slice();
    size[axis] = 1;
    const res = new Array(num);
    for(let i = 0; i < res.length; i++){
        begin[axis] = i;
        const tempRes = (0, _slice.slice)({
            inputs: {
                x: value
            },
            backend,
            attrs: {
                begin,
                size
            }
        });
        res[i] = (0, _reshape.reshape)({
            inputs: {
                x: tempRes
            },
            backend,
            attrs: {
                shape: outShape
            }
        });
        backend.disposeIntermediateTensorInfo(tempRes);
    }
    return res;
}
const unpackConfig = {
    kernelName: (0, _tfjsCore.Unpack),
    backendName: "cpu",
    kernelFunc: unpack
};

},{"@tensorflow/tfjs-core":"21Ftl","./Reshape":"9350i","./Slice":"9L9GN","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}],"eWcTc":[function(require,module,exports) {
/**
 * @license
 * Copyright 2020 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */ var parcelHelpers = require("@parcel/transformer-js/src/esmodule-helpers.js");
parcelHelpers.defineInteropFlag(exports);
parcelHelpers.export(exports, "unsortedSegmentSum", ()=>unsortedSegmentSum);
parcelHelpers.export(exports, "unsortedSegmentSumConfig", ()=>unsortedSegmentSumConfig);
var _tfjsCore = require("@tensorflow/tfjs-core");
var _cpuUtil = require("../cpu_util");
var _cast = require("./Cast");
var _equal = require("./Equal");
var _expandDims = require("./ExpandDims");
var _multiply = require("./Multiply");
var _pack = require("./Pack");
var _sum = require("./Sum");
function unsortedSegmentSum(args) {
    const { inputs, backend, attrs } = args;
    const { x, segmentIds } = inputs;
    const { numSegments } = attrs;
    (0, _cpuUtil.assertNotComplex)(x, "unsortedSegmentSum");
    const xRank = x.shape.length;
    const segmentIdsRank = segmentIds.shape.length;
    const res = [];
    const intermediates = [];
    // Reshape the segment id's so that they can be broadcast with
    // x. The new shape should be [segmentIds.shape, 1, ..., 1]
    const numIters = xRank - segmentIdsRank;
    let $segmentIds = segmentIds;
    for(let i = 0; i < numIters; ++i){
        const expanded = (0, _expandDims.expandDims)({
            inputs: {
                input: $segmentIds
            },
            backend,
            attrs: {
                dim: i + 1
            }
        });
        $segmentIds = expanded;
        intermediates.push(expanded);
    }
    for(let i = 0; i < numSegments; ++i){
        const scalarValue = (0, _tfjsCore.util).createScalarValue(i, "int32");
        const segmentId = backend.makeTensorInfo([], "int32", scalarValue);
        const mask = (0, _equal.equal)({
            inputs: {
                a: segmentId,
                b: $segmentIds
            },
            backend
        });
        const maskCasted = (0, _cast.cast)({
            inputs: {
                x: mask
            },
            backend,
            attrs: {
                dtype: "float32"
            }
        });
        const mul = (0, _multiply.multiply)({
            inputs: {
                a: maskCasted,
                b: x
            },
            backend
        });
        const sumTensorInfo = (0, _sum.sum)({
            inputs: {
                x: mul
            },
            backend,
            attrs: {
                axis: 0,
                keepDims: false
            }
        });
        res.push(sumTensorInfo);
        intermediates.push(segmentId);
        intermediates.push(mask);
        intermediates.push(maskCasted);
        intermediates.push(mul);
        intermediates.push(sumTensorInfo);
    }
    const result = (0, _pack.pack)({
        inputs: res,
        backend,
        attrs: {
            axis: 0
        }
    });
    intermediates.forEach((t)=>backend.disposeIntermediateTensorInfo(t));
    return result;
}
const unsortedSegmentSumConfig = {
    kernelName: (0, _tfjsCore.UnsortedSegmentSum),
    backendName: "cpu",
    kernelFunc: unsortedSegmentSum
};

},{"@tensorflow/tfjs-core":"21Ftl","../cpu_util":"1l037","./Cast":"fAtIw","./Equal":"jK2O7","./ExpandDims":"4z77l","./Multiply":"FrRi2","./Pack":"3NDYm","./Sum":"6gbbV","@parcel/transformer-js/src/esmodule-helpers.js":"5dUr6"}]},["8uLJa"], "8uLJa", "parcelRequire4dc0")

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