ArtPlayer/docs/compiled/artplayer-plugin-danmuku-mask.legacy.js

/*!
 * artplayer-plugin-danmuku-mask.js v1.0.1
 * Github: https://github.com/zhw2590582/ArtPlayer
 * (c) 2017-2026 Harvey Zhao
 * Released under the MIT License.
 */
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this.div(t.neg()).neg();s=m}for(a=this;a.gte(t);){r=Math.max(1,Math.floor(a.toNumber()/t.toNumber()));for(var l=Math.ceil(Math.log(r)/Math.LN2),d=l<=48?1:u(2,l-48),p=o(r),h=p.mul(t);h.isNegative()||h.gt(a);)h=(p=o(r-=d,this.unsigned)).mul(t);p.isZero()&&(p=y),s=s.add(p),a=a.sub(h)}return s},I.div=I.divide,I.modulo=function(t){return n(t)||(t=c(t)),e?i((this.unsigned?e.rem_u:e.rem_s)(this.low,this.high,t.low,t.high),e.get_high(),this.unsigned):this.sub(this.div(t).mul(t))},I.mod=I.modulo,I.rem=I.modulo,I.not=function(){return i(~this.low,~this.high,this.unsigned)},I.and=function(e){return n(e)||(e=c(e)),i(this.low&e.low,this.high&e.high,this.unsigned)},I.or=function(e){return n(e)||(e=c(e)),i(this.low|e.low,this.high|e.high,this.unsigned)},I.xor=function(e){return n(e)||(e=c(e)),i(this.low^e.low,this.high^e.high,this.unsigned)},I.shiftLeft=function(e){return 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e=this.high,t=this.low;return[e>>>24,e>>>16&255,e>>>8&255,255&e,t>>>24,t>>>16&255,t>>>8&255,255&t]},t.fromBytes=function(e,n,r){return r?t.fromBytesLE(e,n):t.fromBytesBE(e,n)},t.fromBytesLE=function(e,n){return new t(e[0]|e[1]<<8|e[2]<<16|e[3]<<24,e[4]|e[5]<<8|e[6]<<16|e[7]<<24,n)},t.fromBytesBE=function(e,n){return new t(e[4]<<24|e[5]<<16|e[6]<<8|e[7],e[0]<<24|e[1]<<16|e[2]<<8|e[3],n)},hr}();const xr=gr(br),vr=xr||t({__proto__:null,default:xr},[br]);function wr(e){return vr.fromString(e,!0,16)}const kr=wr("c3a5c85c97cb3127"),Ir=wr("b492b66fbe98f273"),Nr=wr("9ae16a3b2f90404f");function Sr(e){return e.xor(e.shru(47))}function Tr(e,t,n){const r=e.slice(t,t+n);return vr.fromBytes(Array.from(r),!0,!0)}function Cr(e,t){return Tr(e,t,8)}function $r(e,t){return Tr(e,t,4)}function Er(e,t){return 0===t?e:e.shru(t).or(e.shl(64-t))}function Rr(e,t,n=wr("9ddfea08eb382d69")){let r=e.xor(t).mul(n);r=r.xor(r.shru(47));let a=t.xor(r).mul(n);return a=a.xor(a.shru(47)),a=a.mul(n),a}function 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To get the original bytes, call tensor.bytes().")}return e}bytes(){return e(this,null,function*(){this.throwIfDisposed();const e=yield Xr().read(this.dataId);return"string"===this.dtype?e:new Uint8Array(e.buffer)})}dispose(){this.isDisposed||(this.kerasMask&&this.kerasMask.dispose(),Xr().disposeTensor(this),this.isDisposedInternal=!0)}get isDisposed(){return this.isDisposedInternal}throwIfDisposed(){if(this.isDisposed)throw new Error("Tensor is disposed.")}print(e=!1){return Yr.print(this,e)}clone(){return this.throwIfDisposed(),Yr.clone(this)}toString(e=!1){return Ur(this.dataSync(),this.shape,this.dtype,e)}cast(e){return this.throwIfDisposed(),Yr.cast(this,e)}variable(e=!0,t,n){return this.throwIfDisposed(),Xr().makeVariable(this,e,t,n)}}function Zr(){return H("Tensor",()=>Qr)}Object.defineProperty(Qr,Symbol.hasInstance,{value:e=>!!e&&null!=e.data&&null!=e.dataSync&&null!=e.throwIfDisposed}),Zr();class Jr extends Qr{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!p(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Xr().disposeTensor(this),this.dataId=e.dataId,Xr().incRef(this,null)}dispose(){Xr().disposeVariable(this),this.isDisposedInternal=!0}}var ea,ta,na,ra,aa,sa,oa,ia,ua,la;Object.defineProperty(Jr,Symbol.hasInstance,{value:e=>e instanceof Qr&&null!=e.assign&&e.assign instanceof Function}),(ta=ea||(ea={})).R0="R0",ta.R1="R1",ta.R2="R2",ta.R3="R3",ta.R4="R4",ta.R5="R5",ta.R6="R6",(ra=na||(na={})).float32="float32",ra.int32="int32",ra.bool="int32",ra.complex64="complex64",(sa=aa||(aa={})).float32="float32",sa.int32="int32",sa.bool="bool",sa.complex64="complex64",(ia=oa||(oa={})).float32="float32",ia.int32="float32",ia.bool="float32",ia.complex64="complex64",(la=ua||(ua={})).float32="complex64",la.int32="complex64",la.bool="complex64",la.complex64="complex64";const ca={float32:oa,int32:na,bool:aa,complex64:ua};function da(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return ca[e][t]}function pa(e){return da(e,"int32")}function ha(e){return null!=e&&"object"===typeof e&&"texture"in e&&e.texture instanceof WebGLTexture}function fa(e){return"undefined"!==typeof GPUBuffer&&null!=e&&"object"===typeof e&&"buffer"in e&&e.buffer instanceof GPUBuffer}function ma(e,t){if(e.dtype===t.dtype)return[e,t];const n=da(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function ga(e){const t=[];return ya(e,t,new Set),t}function ya(e,t,n){if(null==e)return;if(e instanceof Qr)return void t.push(e);if(r=e,!Array.isArray(r)&&"object"!==typeof r)return;var r;const a=e;for(const s in a){const e=a[s];n.has(e)||(n.add(e),ya(e,t,n))}}function ba(e){return null!=e.kernelName}class xa{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(const e in this.registeredVariables)this.registeredVariables[e].dispose()}}class va{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new xa}ready(){return e(this,null,function*(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then(()=>{});if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t];if(yield this.initializeBackend(n).success)return void(yield this.setBackend(n))}throw new Error("Could not initialize any backends, all backend initializations failed.")})}get backend(){if(null!=this.pendingBackendInit)throw new Error(`Backend '${this.backendName}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);if(null==this.backendInstance){const{name:e,asyncInit:t}=this.initializeBackendsAndReturnBest();if(t)throw new Error(`The highest priority backend '${e}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);this.setBackend(e)}return this.backendInstance}backendNames(){return Object.keys(this.registryFactory)}findBackend(e){if(!(e in this.registry)){if(!(e in this.registryFactory))return null;{const{asyncInit:t}=this.initializeBackend(e);if(t)return null}}return this.registry[e]}findBackendFactory(e){return e in this.registryFactory?this.registryFactory[e].factory:null}registerBackend(e,t,n=1){return e in this.registryFactory?(ar(`${e} backend was already registered. Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}setBackend(t){return e(this,null,function*(){if(null==this.registryFactory[t])throw new Error(`Backend name '${t}' not found in registry`);if(this.backendName=t,null==this.registry[t]){this.backendInstance=null;const{success:e,asyncInit:n}=this.initializeBackend(t);if(!(n?yield e:e))return!1}return this.backendInstance=this.registry[t],this.setupRegisteredKernels(),this.profiler=new Vr(this.backendInstance),!0})}setupRegisteredKernels(){lr(this.backendName).forEach(e=>{null!=e.setupFunc&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){lr(e).forEach(t=>{null!=t.disposeFunc&&t.disposeFunc(this.registry[e])})}initializeBackend(e){const t=this.registryFactory[e];if(null==t)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{const n=t.factory();if(!n||n instanceof r||"function"!==typeof n.then)return this.registry[e]=n,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,r=n.then(n=>!(t<this.pendingBackendInitId)&&(this.registry[e]=n,this.pendingBackendInit=null,!0)).catch(n=>(t<this.pendingBackendInitId||(this.pendingBackendInit=null,ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}}catch(n){return ar(`Initialization of backend ${e} failed`),ar(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&null!=this.pendingBackendInit&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(0===Object.keys(this.registryFactory).length)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;t<e.length;t++){const n=e[t],{success:r,asyncInit:a}=this.initializeBackend(n);if(a||r)return{name:n,asyncInit:a}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){const n=this.state.tensorInfo.get(t),r=n.backend,a=this.readSync(t),s=r.refCount(t);r.disposeData(t,!0),n.backend=e,e.move(t,a,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n,r=null;if(null==t){if("function"!==typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!==typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!==typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");r=e}return this.scopedRun(()=>this.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,n){e();try{const e=n();return t(),e}catch(r){throw t(),r}}nextTensorId(){return va.nextTensorId++}nextVariableId(){return va.nextVariableId++}clone(e){const t=ka.runKernel(it,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],e=>({x:()=>{const t={x:e},n={dtype:"float32"};return ka.runKernel(me,t,n)}}),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=ir(e,this.backendName)))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){const r=this.backend.numDataIds();let a=0;n.forEach(e=>{a+="complex64"===e.dtype?3:1});const s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],o=r-t-a-s;if(o>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${o} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[];const r=this.isTapeOn(),a=this.state.numBytes,s=this.state.numTensors;let o,i;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=ba(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(ba(e)){const{kernelName:t,inputs:a,attrs:s}=e;null==this.backendName&&this.backend;const l=ir(t,this.backendName);u(null!=l,()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`),o=()=>{const e=this.backend.numDataIds();i=l.kernelFunc({inputs:a,attrs:s,backend:this.backend});const o=Array.isArray(i)?i:[i];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,o);const u=o.map(e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e));if(r){const e=this.getTensorsForGradient(t,a,u);n=this.saveTensorsForBackwardMode(e)}return u}}else{const{forwardFunc:t}=e,a=e=>{r&&(n=e.map(e=>this.keep(this.clone(e))))};o=()=>{const e=this.backend.numDataIds();i=this.tidy(()=>t(this.backend,a));const n=Array.isArray(i)?i:[i];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:c,attrs:d}=e,p=ba(e)?null:e.backwardsFunc;let h;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{this.ENV.getBool("DEBUG")||this.state.profiling?(h=this.profiler.profileKernel(l,c,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(h),t=h.outputs):t=o()}),r&&this.addTapeNode(l,c,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-a,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(e=>null!=c[e]?c[e].shape:null),outputShapes:t.map(e=>e.shape),kernelTimeMs:h.timeMs,extraInfo:h.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(e=>this.keep(this.clone(e)))}getTensorsForGradient(e,t,n){const r=ur(e);if(null!=r){const e=r.inputsToSave||[],a=r.outputsToSave||[];let s;r.saveAllInputs?(u(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),s=Object.keys(t).map(e=>t[e])):s=e.map(e=>t[e]);const o=n.filter((e,t)=>a[t]);return s.concat(o)}return[]}makeTensor(e,t,n,r){if(null==e)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",r=r||this.backend;let a=e;"string"===n&&N(e[0])&&(a=e.map(e=>Mr(e)));const s=r.write(a,t,n),o=new Qr(t,n,s,this.nextTensorId());if(this.trackTensor(o,r),"string"===n){const e=this.state.tensorInfo.get(s),t=function(e){if(null==e)return 0;let t=0;return e.forEach(e=>t+=e.length),t}(a);this.state.numBytes+=t-e.bytes,e.bytes=t}return o}makeTensorFromDataId(e,t,n,r){const a={dataId:e,shape:t,dtype:n=n||"float32"};return this.makeTensorFromTensorInfo(a,r)}makeTensorFromTensorInfo(e,t){const{dataId:n,shape:r,dtype:a}=e,s=new Qr(r,a,n,this.nextTensorId());return this.trackTensor(s,t),s}makeVariable(e,t=!0,n,r){n=n||this.nextVariableId().toString(),null!=r&&r!==e.dtype&&(e=e.cast(r));const a=new Jr(e,t,n,this.nextTensorId());if(null!=this.state.registeredVariables[a.name])throw new Error(`Variable with name ${a.name} was already registered`);return this.state.registeredVariables[a.name]=a,this.incRef(a,this.backend),a}trackTensor(e,t){this.state.numTensors++,"string"===e.dtype&&this.state.numStringTensors++;let n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=e.size*I(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof Jr||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;const t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,"string"===e.dtype&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),"complex64"!==e.dtype&&"string"!==e.dtype){const t=e.size*I(e.dtype);this.state.numBytes-=t}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(const e in this.state.registeredVariables){const t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),null!=this.state.registeredVariables[e.name]&&delete this.state.registeredVariables[e.name]}memory(){const e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}profile(t){return e(this,null,function*(){this.state.profiling=!0;const e=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=yield t(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(e=>e.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-e,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const t of this.state.activeProfile.kernels)t.kernelTimeMs=yield t.kernelTimeMs,t.extraInfo=yield t.extraInfo;return this.state.activeProfile})}isTapeOn(){return this.state.gradientDepth>0&&0===this.state.kernelDepth}addTapeNode(e,t,n,r,a,s){const o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:a},i=ur(e);null!=i&&(r=i.gradFunc),null!=r&&(o.gradient=e=>(e=e.map((e,t)=>{if(null==e){const e=n[t],r=A(e.size,e.dtype);return this.makeTensor(r,e.shape,e.dtype)}return e}),r(e.length>1?e:e[0],a,s))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){0===this.state.gradientDepth&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){const t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){const t=ga(e),n=new Set(t.map(e=>e.id));for(let a=0;a<this.state.activeScope.track.length;a++){const e=this.state.activeScope.track[a];e.kept||n.has(e.id)||e.dispose()}const r=this.state.scopeStack.pop();this.state.activeScope=0===this.state.scopeStack.length?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(e=>{e.kept||e.scopeId!==r.id||this.track(e)})}gradients(e,t,n,r=!1){if(u(t.length>0,()=>"gradients() received an empty list of xs."),null!=n&&"float32"!==n.dtype)throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);const a=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));u(a instanceof Qr,()=>"The result y returned by f() must be a tensor.");const s=function(e,t,n){const r={},a={};for(let u=0;u<t.length;u++)r[t[u].id]=!0;for(let u=0;u<e.length;u++){const n=e[u],s=n.inputs;for(const e in s){const o=s[e];let i=!1;for(let e=0;e<t.length;e++)if(r[o.id]){n.outputs.forEach(e=>r[e.id]=!0),i=!0,a[n.id]=!0;break}if(i)break}}const s={};s[n.id]=!0;const o={};for(let u=e.length-1;u>=0;u--){const t=e[u],n=t.inputs;for(let e=0;e<t.outputs.length;e++)if(s[t.outputs[e].id]){for(const e in n)s[n[e].id]=!0,o[t.id]=!0;break}}const i=[];for(let u=0;u<e.length;u++){const t=e[u];if(a[t.id]&&o[t.id]){const e={};for(const a in t.inputs){const n=t.inputs[a];r[n.id]&&(e[a]=n)}const n=Object.assign({},t);n.inputs=e,n.outputs=t.outputs,i.push(n)}}return i}(this.state.activeTape,t,a);if(!r&&0===s.length&&t.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 e={};e[a.id]=null==n?function(e){const t=_(d(e),"float32");return ka.makeTensor(t,e,"float32")}(a.shape):n,function(e,t,n,r){for(let a=t.length-1;a>=0;a--){const s=t[a],o=[];if(s.outputs.forEach(t=>{const n=e[t.id];null!=n?o.push(n):o.push(null)}),null==s.gradient)throw new Error(`Cannot compute gradient: gradient function not found for ${s.kernelName}.`);const i=s.gradient(o);for(const t in s.inputs){if(!(t in i))throw new Error(`Cannot backprop through input ${t}. Available gradients found: ${Object.keys(i)}.`);const a=n(()=>i[t]());if("float32"!==a.dtype)throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input ${t} must have 'float32' dtype, but has '${a.dtype}'`);const o=s.inputs[t];if(!p(a.shape,o.shape))throw new Error(`Error in gradient for op ${s.kernelName}. The gradient of input '${t}' has shape '${a.shape}', which does not match the shape of the input '${o.shape}'`);if(null==e[o.id])e[o.id]=a;else{const t=e[o.id];e[o.id]=r(t,a),t.dispose()}}}}(e,s,e=>this.tidy(e),Ia);const r=t.map(t=>e[t.id]);return 0===this.state.gradientDepth&&(this.state.activeTape.forEach(e=>{for(const t of e.saved)t.dispose()}),this.state.activeTape=null),{value:a,grads:r}})}customGrad(e){return u(T(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{let n;u(t.every(e=>e instanceof Qr),()=>"The args passed in customGrad(f)(x1, x2,...) must all be tensors");const r={};t.forEach((e,t)=>{r[t]=e});return this.runKernelFunc({forwardFunc:(r,a)=>(n=e(...t,a),u(n.value instanceof Qr,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),u(T(n.gradFunc),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n.value),backwardsFunc:(e,r)=>{const 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e=(p-1)*a+i-t,c=(h-1)*s+u-n,m=(f-1)*o+l-r,g=Math.floor(e/2),y=e-g,b=Math.floor(c/2),x=c-b,v=Math.floor(m/2);d={top:b,bottom:x,left:v,right:m-v,front:g,back:y,type:"SAME"}}}return{padInfo:d,outDepth:p,outHeight:h,outWidth:f}}(a,l,c,d,y,b,x,I,N,S,i),R=s?g*p:g;let _;return"channelsFirst"===o?_=[u,R,C,$,E]:"channelsLast"===o&&(_=[u,C,$,E,R]),{batchSize:u,dataFormat:o,inDepth:l,inHeight:c,inWidth:d,inChannels:p,outDepth:C,outHeight:$,outWidth:E,outChannels:R,padInfo:T,strideDepth:y,strideHeight:b,strideWidth:x,filterDepth:h,filterHeight:f,filterWidth:m,effectiveFilterDepth:I,effectiveFilterHeight:N,effectiveFilterWidth:S,dilationDepth:v,dilationHeight:w,dilationWidth:k,inShape:e,outShape:_,filterShape:t}}function co(e,t,n,r=1){const a=fo(t,r);return Math.floor((e[0]*(n-1)-n+a)/2)}function po(e){return"number"===typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function ho(e){return"number"===typeof e?[e,e,e]:e}function fo(e,t){return t<=1?e:e+(e-1)*(t-1)}function mo(e,t){if(!t)return 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n={x:Ra(e,"x","reshape","string_or_numeric")},r={shape:t};return ka.runKernel(rn,n,r)}});const ko=Oa({avgPool_:function(e,t,n,r,a){const s=Ra(e,"x","avgPool","float32");u(yo(n,1),()=>`Error in avgPool: Either strides or dilations must be 1. 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p=ka.runKernel(ue,c,d);return p=Bs(p,i.dtype),l?wo(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}});const No=Oa({concat_:function(e,t=0){u(e.length>=1,()=>"Pass at least one tensor to concat");const n=_a(e,"tensors","concat","string_or_numeric");if("complex64"===n[0].dtype&&n.forEach(e=>{if("complex64"!==e.dtype)throw new Error(`Cannot concatenate complex64 tensors with a tensor\n          with dtype ${e.dtype}. `)}),1===n.length)return Vs(n[0]);const r=n,a={axis:t};return ka.runKernel(ve,r,a)}});const So=Oa({matMul_:function(e,t,n=!1,r=!1){let a=Ra(e,"a","matMul"),s=Ra(t,"b","matMul");[a,s]=ma(a,s);const o={a:a,b:s},i={transposeA:n,transposeB:r};return ka.runKernel(ce,o,i)}});const To=Oa({sigmoid_:function(e){const t={x:Ra(e,"x","sigmoid","float32")};return ka.runKernel(kn,t)}});const Co=Oa({slice_:function(e,t,n){const r=Ra(e,"x","slice","string_or_numeric");if(0===r.rank)throw new Error("Slicing scalar is not possible");const a={x:r},s={begin:t,size:n};return ka.runKernel(bn,a,s)}});const $o=Oa({tanh_:function(e){const t={x:Ra(e,"x","tanh","float32")};return ka.runKernel(Un,t)}});const Eo=Oa({basicLSTMCell_:function(e,t,n,r,a,s){const o=Ra(e,"forgetBias","basicLSTMCell"),i=Ra(t,"lstmKernel","basicLSTMCell"),u=Ra(n,"lstmBias","basicLSTMCell"),l=Ra(r,"data","basicLSTMCell"),c=Ra(a,"c","basicLSTMCell"),d=Ra(s,"h","basicLSTMCell"),p=No([l,d],1),h=So(p,i),f=zs(h,u),m=f.shape[0],g=f.shape[1]/4,y=[m,g],b=Co(f,[0,0],y),x=Co(f,[0,g],y),v=Co(f,[0,2*g],y),w=Co(f,[0,3*g],y),k=zs(Hs(To(b),$o(x)),Hs(c,To(zs(o,v))));return[k,Hs($o(k),To(w))]}});const Ro=Oa({batchToSpaceND_:function(e,t,n){const r=Ra(e,"x","batchToSpaceND"),a=t.reduce((e,t)=>e*t);u(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),u(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`),u(r.shape[0]%a===0,()=>`input tensor batch is ${r.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${a}`);const s={x:r},o={blockShape:t,crops:n};return ka.runKernel(de,s,o)}});const _o=Oa({batchNorm_:function(e,t,n,r,a,s){null==s&&(s=.001);const o=Ra(e,"x","batchNorm"),i=Ra(t,"mean","batchNorm"),l=Ra(n,"variance","batchNorm");let c,d;null!=a&&(c=Ra(a,"scale","batchNorm")),null!=r&&(d=Ra(r,"offset","batchNorm")),u(i.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),u(null==d||i.rank===d.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),u(null==c||i.rank===c.rank,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");const p={x:function(e){let t;return t=0===e.rank||1===e.rank?wo(e,[1,1,1,e.size]):2===e.rank?wo(e,[1,1,e.shape[0],e.shape[1]]):3===e.rank?wo(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e,t}(o),scale:c,offset:d,mean:i,variance:l},h={varianceEpsilon:s},f=ka.runKernel(nt,p,h);return wo(f,o.shape)}});const Ao=Oa({batchNorm2d_:function(e,t,n,r,a,s){const o=Ra(e,"x","batchNorm"),i=Ra(t,"mean","batchNorm"),l=Ra(n,"variance","batchNorm");let c,d;return null!=a&&(c=Ra(a,"scale","batchNorm")),null!=r&&(d=Ra(r,"offset","batchNorm")),u(2===o.rank,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),u(2===i.rank||1===i.rank,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${i.rank}.`),u(2===l.rank||1===l.rank,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),null!=c&&u(2===c.rank||1===c.rank,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),null!=d&&u(2===d.rank||1===d.rank,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),_o(o,i,l,d,c,s)}});const Oo=Oa({batchNorm3d_:function(e,t,n,r,a,s){const o=Ra(e,"x","batchNorm"),i=Ra(t,"mean","batchNorm"),l=Ra(n,"variance","batchNorm");let c,d;return null!=a&&(c=Ra(a,"scale","batchNorm")),null!=r&&(d=Ra(r,"offset","batchNorm")),u(3===o.rank,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),u(3===i.rank||1===i.rank,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),u(3===l.rank||1===l.rank,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),null!=c&&u(3===c.rank||1===c.rank,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),null!=d&&u(3===d.rank||1===d.rank,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),_o(o,i,l,d,c,s)}});const Fo=Oa({batchNorm4d_:function(e,t,n,r,a,s){const o=Ra(e,"x","batchNorm"),i=Ra(t,"mean","batchNorm"),l=Ra(n,"variance","batchNorm");let c,d;return null!=a&&(c=Ra(a,"scale","batchNorm")),null!=r&&(d=Ra(r,"offset","batchNorm")),u(4===o.rank,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),u(4===i.rank||1===i.rank,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),u(4===l.rank||1===l.rank,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),null!=c&&u(4===c.rank||1===c.rank,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),null!=d&&u(4===d.rank||1===d.rank,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),_o(o,i,l,d,c,s)}});const Do=Oa({bincount_:function(e,t,n){const r=Ra(e,"x","bincount"),a=Ra(t,"weights","bincount");u("int32"===r.dtype,()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),u(n>=0,()=>`size must be non-negative, but got ${n}.`),u(a.size===r.size||0===a.size,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${r.shape}, weights shape: ${a.shape}.`);const s={x:r,weights:a},o={size:n};return ka.runKernel(pe,s,o)}});const Mo=Oa({bitwiseAnd_:function(e,t){const n=Ra(e,"x","bitwiseAnd"),r=Ra(t,"y","bitwiseAnd");if(!p(n.shape,r.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${n.shape}, y: ${r.shape}`);if("int32"!==n.dtype||"int32"!==r.dtype)throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${n.dtype} and type of y: ${r.dtype}`);const a={a:n,b:r};return ka.runKernel(he,a)}});const Po=Oa({broadcastArgs_:function(e,t){const n=Ra(e,"s0","broadcastArgs","int32"),r=Ra(t,"s1","broadcastArgs","int32");if(1!==n.rank)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(1!==r.rank)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${r.rank}`);const a={s0:n,s1:r};return ka.runKernel(fe,a)}});const Lo=Oa({broadcastTo_:function(e,t){let n=Ra(e,"broadcastTo","x");const r=n.shape;if(F(t),t.length<n.rank)throw new Error(`broadcastTo(): shape.length=${t.length} < input.rank=${n.rank}.`);if(t.length>n.rank){const e=n.shape.slice();for(;e.length<t.length;)e.unshift(1);n=wo(n,e)}const a=n.shape,s=Array.from(t);for(let u=t.length-1;u>=0;u--)if(a[u]===t[u])s[u]=1;else if(1!==n.shape[u])throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(0===s.map((e,t)=>e>1?t:-1).filter(e=>e>=0).length)return Vs(n);const o={x:n},i={reps:s};return ka.runKernel(Gn,o,i)}});const Bo=Oa({ceil_:function(e){const t={x:Ra(e,"x","ceil","float32")};return ka.runKernel(ge,t)}});function Vo(e,t,n){F(e);const r={shape:e,value:t,dtype:n=n||S(t)};return ka.runKernel(Ze,{},r)}const Wo=Oa({clipByValue_:function(e,t,n){const r=Ra(e,"x","clipByValue");if(u(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`),t===n)return Vo(r.shape,t,r.dtype);const a={x:r},s={clipValueMin:t,clipValueMax:n};return ka.runKernel(ye,a,s)}});const zo=Oa({concat1d_:function(e){return No(e,0)}});const Uo=Oa({concat2d_:function(e,t){return No(e,t)}});const Go=Oa({concat3d_:function(e,t){return No(e,t)}});const Ho=Oa({concat4d_:function(e,t){return No(e,t)}});const jo=Oa({conv2d_:function(e,t,n,r,a="NHWC",s=[1,1],o){const i=Ra(e,"x","conv2d","float32"),l=Ra(t,"filter","conv2d","float32");let c=i,d=!1;3===i.rank&&(d=!0,c=wo(i,[1,i.shape[0],i.shape[1],i.shape[2]])),u(4===c.rank,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),u(4===l.rank,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),vo("conv2d",r,o);const p="NHWC"===a?c.shape[3]:c.shape[1];u(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),u(yo(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),u(bo(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),u(bo(n),()=>"Error in conv2D: Strides should be larger than 0.");const h={x:c,filter:l},f={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:o},m=ka.runKernel(we,h,f);return d?wo(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const qo=Oa({conv1d_:function(e,t,n,r,a="NWC",s=1,o){const i=Ra(e,"x","conv1d"),l=Ra(t,"filter","conv1d");let c=i,d=!1;2===i.rank&&(d=!0,c=wo(i,[1,i.shape[0],i.shape[1]])),u(3===c.rank,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),u(3===l.rank,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),vo("conv1d",r,o),u(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),u(yo(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),u(bo(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),u(bo(n),()=>"Error in conv1D: Stride should be larger than 0."),u("NWC"===a,()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);const p=wo(l,[1,l.shape[0],l.shape[1],l.shape[2]]),h=wo(c,[c.shape[0],1,c.shape[1],c.shape[2]]),f=jo(h,p,[1,n],r,"NHWC",[1,s],o);return wo(f,d?[f.shape[2],f.shape[3]]:[f.shape[0],f.shape[2],f.shape[3]])}});const Ko=Oa({conv2DBackpropInput_:function(e,t,n,r,a,s="NHWC",o){u(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,c=!1;3===t.rank&&(c=!0,l=wo(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),u(4===i.length,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),u(4===l.rank,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),u(4===n.rank,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);const d="NHWC"===s?i[3]:i[1],p="NHWC"===s?l.shape[3]:l.shape[1];u(d===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${n.shape[2]}.`),u(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),vo("conv2dDerInput",a,o);const h={dy:l,filter:n},f={strides:r,pad:a,dataFormat:s,dimRoundingMode:o,inputShape:i},m=ka.runKernel(Ie,h,f);return c?wo(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const Xo=Oa({conv2dTranspose_:function(e,t,n,r,a,s){const o=Ra(e,"x","conv2dTranspose"),i=Ra(t,"filter","conv2dTranspose");return Ko(n,o,i,r,a,"NHWC",s)}});const Yo=Oa({conv3d_:function(e,t,n,r,a="NDHWC",s=[1,1,1]){const o=Ra(e,"x","conv3d"),i=Ra(t,"filter","conv3d");let l=o,c=!1;4===o.rank&&(c=!0,l=wo(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),u(5===l.rank,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),u(5===i.rank,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),u(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),u(yo(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),u("NDHWC"===a,()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`),u(bo(s),()=>"Error in conv3D: Dilated rates should be larger than 0."),u(bo(n),()=>"Error in conv3D: Strides should be larger than 0.");const d={x:l,filter:i},p={strides:n,pad:r,dataFormat:a,dilations:s},h=ka.runKernel(Ne,d,p);return c?wo(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}});const Qo=Oa({conv3DBackpropInput_:function(e,t,n,r,a){u(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,o=t,i=!1;4===t.rank&&(i=!0,o=wo(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),s=[1,e[0],e[1],e[2],e[3]]);const l=s[4],c=o.shape[4];u(5===s.length,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),u(5===o.rank,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),u(5===n.rank,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank 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a={x:Ra(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return ka.runKernel(Re,a,s)}});const ri=Oa({denseBincount_:function(e,t,n,r=!1){const a=Ra(e,"x","denseBincount"),s=Ra(t,"weights","denseBincount");u("int32"===a.dtype,()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),u(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),u(n>=0,()=>`size must be non-negative, but got ${n}.`),u(s.size===a.size||0===s.size,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${a.shape}, weights shape: ${s.shape}.`);const o={x:a,weights:s},i={size:n,binaryOutput:r};return ka.runKernel(Ae,o,i)}});const ai=Oa({depthToSpace_:function(e,t,n="NHWC"){const r=Ra(e,"x","depthToSpace","float32"),a="NHWC"===n?r.shape[1]:r.shape[2],s="NHWC"===n?r.shape[2]:r.shape[3],o="NHWC"===n?r.shape[3]:r.shape[1];u(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),u(a*t>=0,()=>`Negative 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channels (${p}) must match the inChannels dimension in filter ${l.shape[2]}.`),vo("depthwiseConv2d",r,o);const h={x:c,filter:l},f={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:o},m=ka.runKernel(Fe,h,f);return d?wo(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const oi=Oa({diag_:function(e){const t={x:Ra(e,"x","diag")};return ka.runKernel(Pe,t)}});const ii=Oa({dilation2d_:function(e,t,n,r,a=[1,1],s="NHWC"){const o=Ra(e,"x","dilation2d"),i=Ra(t,"filter","dilation2d");u(3===o.rank||4===o.rank,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),u(3===i.rank,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),u("NHWC"===s,()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=o,c=!1;3===o.rank&&(l=wo(o,[1,o.shape[0],o.shape[1],o.shape[2]]),c=!0),u(l.shape[3]===i.shape[2],()=>`Error in dilation2d:  input and filter must have the same depth: ${l.shape[3]} vs ${i.shape[2]}`);const d={x:l,filter:i},p={strides:n,pad:r,dilations:a},h=ka.runKernel(Le,d,p);return c?wo(h,[h.shape[1],h.shape[2],h.shape[3]]):h}});function ui(e,t){const n=e.length,r=[];for(let a=0;a<n;a++){const s=n-1-a,o=e[s]||1;(t[t.length-1-a]||1)>1&&1===o&&r.unshift(s)}return r}function li(e,t){const n=[];for(let r=0;r<t.length;r++){const a=e[e.length-r-1],s=t.length-r-1,o=t[s];(null==a||1===a&&o>1)&&n.unshift(s)}return n}function ci(e,t){const n=Math.max(e.length,t.length),r=new Array(n);for(let a=0;a<n;a++){let s=e[e.length-a-1];null==s&&(s=1);let o=t[t.length-a-1];if(null==o&&(o=1),1===s)r[n-a-1]=o;else if(1===o)r[n-a-1]=s;else{if(s!==o){throw Error(`Operands could not be broadcast together with shapes ${e} and ${t}.`)}r[n-a-1]=s}}return r}const di=Oa({equal_:function(e,t){let n=Ra(e,"a","equal","string_or_numeric"),r=Ra(t,"b","equal","string_or_numeric");[n,r]=ma(n,r),ci(n.shape,r.shape);const a={a:n,b:r};return ka.runKernel(qe,a)}});const pi=Oa({where_:function(e,t,n){const r=Ra(t,"a","where"),a=Ra(n,"b","where"),s=Ra(e,"condition","where","bool"),o=ci(ci(s.shape,r.shape),a.shape),i={condition:Lo(s,o),t:Lo(r,o),e:Lo(a,o)};return ka.runKernel(gn,i)}});const hi=Oa({zerosLike_:function(e){const t={x:Ra(e,"x","zerosLike")};return ka.runKernel(Qn,t)}});const fi=Oa({divNoNan_:function(e,t){let n=Ra(e,"a","div"),r=Ra(t,"b","div");[n,r]=ma(n,r);const a=Gs(n,r),s=hi(a),o=di(r,s);return pi(o,s,a)}});const mi=Oa({dot_:function(e,t){const n=Ra(e,"t1","dot"),r=Ra(t,"t2","dot");u((1===n.rank||2===n.rank)&&(1===r.rank||2===r.rank),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${r.rank}.`);const a=1===n.rank?n.size:n.shape[1],s=1===r.rank?r.size:r.shape[0];if(u(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),1===n.rank&&1===r.rank){const e=wo(n,[1,-1]),t=wo(r,[-1,1]),a=So(e,t);return wo(a,[])}if(1===n.rank&&2===r.rank){const e=wo(n,[1,-1]),t=wo(r,[r.shape[0],r.shape[1]]),a=So(e,t);return wo(a,[a.size])}if(2===n.rank&&1===r.rank){const e=wo(r,[-1,1]),t=So(n,e);return wo(t,[t.size])}{const e=wo(r,[r.shape[0],r.shape[1]]);return So(n,e)}}});const gi=Oa({einsum_:function(e,...t){const n=t.map((e,t)=>Ra(e,`tensors${t}`,"einsum")),r={equation:e};return ka.runKernel(Ue,n,r)}});const yi=Oa({elu_:function(e){const t={x:Ra(e,"x","elu","float32")};return ka.runKernel(Ge,t)}});const bi=Oa({ensureShape_:function(e,t){const n=Ra(e,"x","ensureShape","string_or_numeric");if(!function(e,t){if(e===t)return!0;if(null==e||null==t)return!1;if(e.length!==t.length)return!1;for(let n=0;n<e.length;n++)if(null!==e[n]&&null!==t[n]&&e[n]!==t[n])return!1;return!0}(n.shape,t))throw new Error(`EnsureShape: Shape of tensor ${n.shape} is not compatible with expected shape ${t}`);return e}});const xi=Oa({erf_:function(e){let t=Ra(e,"x","erf");u("int32"===t.dtype||"float32"===t.dtype,()=>"Input dtype must be `int32` or `float32`."),"int32"===t.dtype&&(t=Bs(t,"float32"));const n={x:t};return ka.runKernel(je,n)}});function vi(e,t){for(let n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function wi(e,t,n){const r=e.length+t.length,a=[];let s=0,o=0;for(let i=0;i<r;i++)-1===n.indexOf(i)?a.push(e[s++]):a.push(t[o++]);return a}function ki(e,t){const n=[],r=e.length;for(let a=0;a<r;a++)-1===t.indexOf(a)&&n.push(e[a]);return[n,t.map(t=>e[t])]}function Ii(e,t){return wi(e,t.map(e=>1),t)}function Ni(e,t,n){u(vi(t,n),()=>`${e} supports only inner-most axes for now. 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the value must be `Uint8Array`.");return Da(e,[],[],t)}const Ai=Oa({sqrt_:function(e){const t={x:Ra(e,"x","sqrt","float32")};return ka.runKernel(Nn,t)}});const Oi=Oa({square_:function(e){const t=Ra(e,"x","square");return ka.runKernel("Square",{x:t},{})}});const Fi=Oa({sum_:function(e,t=null,n=!1){let r=Ra(e,"x","sum");"bool"===r.dtype&&(r=Bs(r,"int32"));const a={x:r},s={axis:t,keepDims:n};return ka.runKernel(Sn,a,s)}});function Di(e,t,n=null){if(0===e.rank)return js(e);if(1!==e.rank&&null===n)return Di(wo(e,[-1]),t,n);if(1===e.rank||"number"===typeof n||Array.isArray(n)&&1===n.length){if(1===t)return Fi(js(e),n);if(t===1/0)return $i(js(e),n);if(t===-1/0)return Ei(js(e),n);if("euclidean"===t||2===t)return Ai(Fi(Ri(js(e),_i(2,"int32")),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}if(Array.isArray(n)&&2===n.length){if(1===t)return $i(Fi(js(e),n[0]),n[1]-1);if(t===1/0)return $i(Fi(js(e),n[1]),n[0]);if(t===-1/0)return Ei(Fi(js(e),n[1]),n[0]);if("fro"===t||"euclidean"===t)return Ai(Fi(Oi(e),n));throw new Error(`Error in norm: invalid ord value: ${t}`)}throw new Error(`Error in norm: invalid axis: ${n}`)}const Mi=Oa({norm_:function(e,t="euclidean",n=null,r=!1){const a=Di(e=Ra(e,"x","norm"),t,n);let s=a.shape;if(r){const t=b(n,e.shape);s=Ii(a.shape,t)}return wo(a,s)}});const Pi=Oa({euclideanNorm_:function(e,t=null,n=!1){return Mi(e,"euclidean",t,n)}});const Li=Oa({exp_:function(e){const t={x:Ra(e,"x","exp")};return ka.runKernel(Ke,t)}});const Bi=Oa({expandDims_:function(e,t=0){const n=Ra(e,"x","expandDims","string_or_numeric");u(t<=n.rank,()=>"Axis must be <= rank of the tensor");const r={input:n},a={dim:t};return ka.runKernel(Xe,r,a)}});const Vi=Oa({expm1_:function(e){const t={x:Ra(e,"x","expm1")};return ka.runKernel(Ye,t)}});const Wi=Oa({tile_:function(e,t){const n=Ra(e,"x","tile","string_or_numeric");u(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);const r={x:n},a={reps:t};return ka.runKernel(Gn,r,a)}});const zi=Oa({eye_:function(e,t,n,r="float32"){null==t&&(t=e);const a=Ls([e,t],r),s=e<=t?e:t;for(let i=0;i<s;++i)a.set(1,i,i);const o=wo(a.toTensor(),[e,t]);if(null==n)return o;if(1===n.length)return Wi(Bi(o,0),[n[0],1,1]);if(2===n.length)return Wi(Bi(Bi(o,0),0),[n[0],n[1],1,1]);if(3===n.length)return Wi(Bi(Bi(Bi(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}});const Ui=Oa({floor_:function(e){const t={x:Ra(e,"x","floor","float32")};return ka.runKernel(et,t)}});const Gi=Oa({gather_:function(e,t,n=0,r=0){const a={x:Ra(e,"x","gather"),indices:Ra(t,"indices","gather","int32")},s={axis:n,batchDims:r};return ka.runKernel(rt,a,s)}});const Hi=Oa({greater_:function(e,t){let n=Ra(e,"a","greater","string_or_numeric"),r=Ra(t,"b","greater","string_or_numeric");[n,r]=ma(n,r),ci(n.shape,r.shape);const a={a:n,b:r};return ka.runKernel(st,a)}});const ji=Oa({greaterEqual_:function(e,t){let n=Ra(e,"a","greaterEqual","string_or_numeric"),r=Ra(t,"b","greaterEqual","string_or_numeric");[n,r]=ma(n,r),ci(n.shape,r.shape);const a={a:n,b:r};return ka.runKernel(ot,a)}});const qi=Oa({imag_:function(e){const t={input:Ra(e,"input","imag")};return ka.runKernel(lt,t)}});const Ki=Oa({isFinite_:function(e){const t={x:Ra(e,"x","isFinite")};return ka.runKernel(ct,t)}});const Xi=Oa({isInf_:function(e){const t={x:Ra(e,"x","isInf")};return ka.runKernel(dt,t)}});const Yi=Oa({isNaN_:function(e){const t={x:Ra(e,"x","isNaN")};return ka.runKernel(pt,t)}});const Qi=Oa({leakyRelu_:function(e,t=.2){const n={x:Ra(e,"x","leakyRelu")},r={alpha:t};return ka.runKernel(ht,n,r)}});const Zi=Oa({less_:function(e,t){let n=Ra(e,"a","less","string_or_numeric"),r=Ra(t,"b","less","string_or_numeric");[n,r]=ma(n,r),ci(n.shape,r.shape);const a={a:n,b:r};return ka.runKernel(ft,a)}});const Ji=Oa({lessEqual_:function(e,t){let n=Ra(e,"a","lessEqual","string_or_numeric"),r=Ra(t,"b","lessEqual","string_or_numeric");[n,r]=ma(n,r),ci(n.shape,r.shape);const a={a:n,b:r};return ka.runKernel(mt,a)}});function eu(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");const r={start:e,stop:t,num:n};return ka.runKernel(gt,{},r)}const tu=Oa({localResponseNormalization_:function(e,t=5,n=1,r=1,a=.5){const s=Ra(e,"x","localResponseNormalization");u(4===s.rank||3===s.rank,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${s.rank}.`),u(h(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=s,i=!1;3===s.rank&&(i=!0,o=wo(s,[1,s.shape[0],s.shape[1],s.shape[2]]));const l={x:o},c={depthRadius:t,bias:n,alpha:r,beta:a},d=ka.runKernel(kt,l,c);return i?wo(d,[d.shape[1],d.shape[2],d.shape[3]]):d}});const nu=Oa({log_:function(e){const t={x:Ra(e,"x","log","float32")};return ka.runKernel(yt,t)}});const ru=Oa({log1p_:function(e){const t={x:Ra(e,"x","log1p")};return ka.runKernel(bt,t)}});function au(e,t){u(T(e),()=>"The f passed in variableGrads(f) must be a function"),u(null==t||Array.isArray(t)&&t.every(e=>e instanceof Jr),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");const n=null!=t;if(!n){t=[];for(const e in ka.registeredVariables)t.push(ka.registeredVariables[e])}const r=n?t.filter(e=>!e.trainable):null,a=t.length;u((t=t.filter(e=>e.trainable)).length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${a} variables is trainable.`);const{value:s,grads:o}=ka.gradients(e,t,null,!0);u(o.some(e=>null!=e),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). 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s=Ra(e,"v","movingAverage"),o=Ra(t,"x","movingAverage"),i=Ra(n,"decay","movingAverage");var l,c;c=o,u((l=s).dtype===c.dtype,()=>`The dtypes of the first(${l.dtype}) and second(${c.dtype}) input must match`),u(p(s.shape,o.shape),()=>"Shape mismatch in v and x");const d=_i(1),h=lu(d,i);let f=Hs(lu(o,s),h);if(a){u(null!=r,()=>"When using zeroDebias: true, step is required.");const e=Ra(r,"step","movingAverage");f=Gs(f,lu(d,Ri(i,e)))}return zs(s,f)}});const Cc=Oa({scatterND_:function(e,t,n){F(n);const r=Ra(e,"indices","scatterND","int32"),a=Ra(t,"updates","scatterND");pc(a,r,n);const s={indices:r,updates:a},o={shape:n};return ka.runKernel(hn,s,o)}});const $c=Oa({sparseToDense_:function(e,t,n,r=0){F(n);const a=Ra(e,"sparseIndices","sparseToDense","int32"),s=Ra(t,"sparseValues","sparseToDense","string_or_numeric"),o=Ra(r,"defaultValue","sparseToDense",s.dtype);!function(e,t,n,r){if("int32"!==e.dtype)throw new Error(`tf.sparseToDense() expects the indices to be int32 type, but the dtype was 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must be rank 4, but got shape ${l.shape}.`),u(4===n.length,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);const c="NHWC"===s?i.shape[3]:i.shape[1],d="NHWC"===s?l.shape[3]:l.shape[1];u(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),u(d===n[3],()=>`Error in conv2dDerFilter: depth of dy (${d}) must match output depth for filter (${n[3]}).`),vo("conv2dDerFilter",a,o);const p={x:i,dy:l},h={strides:r,pad:a,dataFormat:s,dimRoundingMode:o,filterShape:n};return ka.runKernel(ke,p,h)}});function Dc(e,t,n){if(null==n||"linear"===n)return e;if("relu"===n)return Hs(e,nc(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Mc(e,t){let n=t;const r=li(e.shape,t.shape);return r.length>0&&(n=Fi(n,r)),wo(n,e.shape)}function Pc(e,t,n,r){if("linear"===t)return e;if("relu"===t)return $l(e);if("elu"===t)return yi(e);if("relu6"===t)return El(e);if("prelu"===t)return Gu(e,n);if("leakyrelu"===t)return Qi(e,r);if("sigmoid"===t)return To(e);throw new Error(`Unknown fused activation ${t}.`)}const Lc=(e,t)=>!(e>0)||"linear"===t;const Bc=Oa({fusedConv2d_:function({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:o,bias:i,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:d}){if(l=l||"linear",!1===Lc(ka.state.gradientDepth,l)){u("NHWC"===a,()=>`Error in fused conv2d: got dataFormat of ${a} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);let p=jo(e,t,n,r,a,s,o);return null!=i&&(p=zs(p,i)),Pc(p,l,c,d)}const p=Ra(e,"x","conv2d","float32"),h=Ra(t,"filter","conv2d","float32");let f=p,m=!1;3===p.rank&&(m=!0,f=wo(p,[1,p.shape[0],p.shape[1],p.shape[2]])),u(4===f.rank,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),u(4===h.rank,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${h.rank}.`),vo("fused conv2d",r,o);const g="NHWC"===a?f.shape[3]:f.shape[1];u(h.shape[2]===g,()=>`Error in conv2d: depth of input (${g}) must match input depth for filter ${h.shape[2]}.`),u(yo(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);const y=uo(f.shape,h.shape,n,s,r,o);let b,x;if(null!=i&&(b=Ra(i,"bias","fused conv2d"),[b]=ma(b,p),"NHWC"===a?ci(y.outShape,b.shape):(u(b.shape.length<=1,()=>`Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${b.shape.length}.`),u(0===b.shape.length||b.shape[0]===y.outChannels||1===b.shape[0],()=>`Error in fused conv2d: bias shape (${b.shape}) is not compatible with the number of output channels (${y.outChannels})`))),null!=c){const e=c.shape;if(u(e.length<=1||3===e.length,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${e.length}.`),1===e.length)u(1===e[0]||e[0]===y.outChannels,()=>`Error in fused conv2d: PReLU activation weights (${e}) is not compatible with the number of output channels (${y.outChannels}).`);else if(3===e.length)try{ci(e,y.outShape)}catch(I){const t=`Error in fused conv2d: PReLU activation weights (${e}) is not compatible with the output shape of the conv2d (${y.outShape}).`;throw Error(t)}x=Ra(c,"prelu weights","fused conv2d")}const v=(e,t)=>{u("NHWC"===a,()=>`Error in gradient of fused conv2D: got dataFormat of ${a} but only NHWC is currently supported.`);const[o,i,c,d]=t,p=Dc(e,c,l);u(go(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. 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p=n?l.shape[l.rank-2]:l.shape[l.rank-1],h=r?c.shape[c.rank-1]:c.shape[c.rank-2],f=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=r?c.shape[c.rank-2]:c.shape[c.rank-1],g=l.shape.slice(0,-2),y=c.shape.slice(0,-2),b=d(g),x=d(y);u(p===h,()=>`Error in fused matMul: inner shapes (${p}) and (${h}) of Tensors with shapes ${l.shape} and ${c.shape} and transposeA=${n} and transposeB=${r} must match.`);const v=ci(l.shape.slice(0,-2),c.shape.slice(0,-2)).concat([f,m]),w=wo(l,n?[b,p,f]:[b,f,p]),k=wo(c,r?[x,m,h]:[x,h,m]);let I,N;null!=a&&(I=Ra(a,"bias","fused matMul"),[I]=ma(I,l),ci(v,I.shape)),null!=o&&(N=Ra(o,"prelu weights","fused matMul"));const S=(e,t)=>{const[o,i,u,l]=t,c=Dc(wo(e,u.shape),u,s);let d,p;if(n||r?!n&&r?(d=So(c,i,!1,!1),p=So(c,o,!0,!1)):n&&!r?(d=So(i,c,!1,!0),p=So(o,c,!1,!1)):(d=So(i,c,!0,!0),p=So(c,o,!0,!0)):(d=So(c,i,!1,!0),p=So(o,c,!0,!1)),null!=a){return[d,p,Mc(l,c)]}return[d,p]},T={a:w,b:k,bias:I,preluActivationWeights:N},C={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:i};if(null==a){return su((e,t,n)=>{const r=ka.runKernel(tr,T,C);return n([e,t,r]),{value:wo(r,v),gradFunc:S}})(w,k)}return su((e,t,n,r)=>{const a=ka.runKernel(tr,T,C);return r([e,t,a,n]),{value:wo(a,v),gradFunc:S}})(w,k,I)}}),Gc=Object.freeze(Object.defineProperty({__proto__:null,conv2d:Bc,depthwiseConv2d:zc,matMul:Uc},Symbol.toStringTag,{value:"Module"}));const Hc=Oa({hammingWindow_:function(e){return Ac(e,.54,.46)}});const jc=Oa({hannWindow_:function(e){return Ac(e,.5,.5)}});const qc=Oa({frame_:function(e,t,n,r=!1,a=0){let s=0;const o=[];for(;s+t<=e.size;)o.push(Co(e,s,t)),s+=n;if(r)for(;s<e.size;){const r=s+t-e.size,i=No([Co(e,s,t-r),Vo([r],a)]);o.push(i),s+=n}return 0===o.length?oc([],[0,t]):wo(No(o),[o.length,t])}});const Kc=Oa({stft_:function(e,t,n,r,a=jc){null==r&&(r=_c(t));const s=qc(e,t,n),o=Hs(s,a(t));return Zl(o,r)}});const Xc=Oa({cropAndResize_:function(e,t,n,r,a="bilinear",s=0){const o=Ra(e,"image","cropAndResize"),i=Ra(t,"boxes","cropAndResize","float32"),l=Ra(n,"boxInd","cropAndResize","int32"),c=i.shape[0];u(4===o.rank,()=>`Error in cropAndResize: image must be rank 4,but got rank ${o.rank}.`),u(2===i.rank&&4===i.shape[1],()=>`Error in cropAndResize: boxes must be have size [${c},4] but had shape ${i.shape}.`),u(1===l.rank&&l.shape[0]===c,()=>`Error in cropAndResize: boxInd must be have size [${c}] but had shape ${i.shape}.`),u(2===r.length,()=>`Error in cropAndResize: cropSize must be of length 2, but got length ${r.length}.`),u(r[0]>=1&&r[1]>=1,()=>`cropSize must be atleast [1,1], but was ${r}`),u("bilinear"===a||"nearest"===a,()=>`method must be bilinear or nearest, but was ${a}`);const d={image:o,boxes:i,boxInd:l},p={method:a,extrapolationValue:s,cropSize:r};return ka.runKernel(_e,d,p)}});const Yc=Oa({flipLeftRight_:function(e){const t=Ra(e,"image","flipLeftRight","float32");u(4===t.rank,()=>`Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);const n={image:t};return ka.runKernel(Je,n,{})}});const Qc=Oa({grayscaleToRGB_:function(e){const t=Ra(e,"image","grayscaleToRGB"),n=t.rank-1,r=t.shape[n];u(t.rank>=2,()=>`Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`),u(1===r,()=>`Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${r}.`);const a=new Array(t.rank);return a.fill(1,0,n),a[n]=3,Wi(t,a)}});const Zc=Oa({rgbToGrayscale_:function(e){const t=Ra(e,"image","RGBToGrayscale"),n=t.rank-1,r=t.shape[n];u(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),u(3===r,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be 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was ${e.shape[1]}`),u(1===t.rank,()=>"scores must be a 1D tensor"),u(t.shape[0]===o,()=>`scores has incompatible shape with boxes. 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g=0;g<t.length;g++)t[g]>a&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(cd);const c=s>0?-.5/s:0,d=[],p=[];for(;d.length<n&&l.length>0;){const t=l.pop(),{score:n,boxIndex:s,suppressBeginIndex:o}=t;if(n<a)break;let i=!1;for(let u=d.length-1;u>=o;--u){const n=ud(e,s,d[u]);if(n>=r){i=!0;break}if(t.score=t.score*ld(r,c,n),t.score<=a)break}t.suppressBeginIndex=d.length,i||(t.score===n?(d.push(s),p.push(t.score)):t.score>a&&nd(l,t,cd))}const h=d.length,f=n-h;i&&f>0&&(d.push(...new Array(f).fill(0)),p.push(...new Array(f).fill(0)));const m={selectedIndices:d};return o&&(m.selectedScores=p),u&&(m.validOutputs=h),m}function ud(e,t,n){const r=e.subarray(4*t,4*t+4),a=e.subarray(4*n,4*n+4),s=Math.min(r[0],r[2]),o=Math.min(r[1],r[3]),i=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(a[0],a[2]),c=Math.min(a[1],a[3]),d=Math.max(a[0],a[2]),p=Math.max(a[1],a[3]),h=(i-s)*(u-o),f=(d-l)*(p-c);if(h<=0||f<=0)return 0;const m=Math.max(s,l),g=Math.max(o,c),y=Math.min(i,d),b=Math.min(u,p),x=Math.max(y-m,0)*Math.max(b-g,0);return x/(h+f-x)}function ld(e,t,n){const r=Math.exp(t*n*n);return n<=e?r:0}function cd(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}const dd=function(t,n,r){return e(this,arguments,function*(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){const s=Ra(e,"boxes","nonMaxSuppressionAsync"),o=Ra(t,"scores","nonMaxSuppressionAsync"),i=ed(s,o,n,r,a);n=i.maxOutputSize,r=i.iouThreshold,a=i.scoreThreshold;const u=yield Promise.all([s.data(),o.data()]),l=u[0],c=u[1],{selectedIndices:d}=ad(l,c,n,r,a);return s!==e&&s.dispose(),o!==t&&o.dispose(),sc(d,"int32")})};const pd=Oa({nonMaxSuppressionWithScore_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){const o=Ra(e,"boxes","nonMaxSuppression"),i=Ra(t,"scores","nonMaxSuppression"),u=ed(o,i,n,r,a,s),l={boxes:o,scores:i},c={maxOutputSize:n=u.maxOutputSize,iouThreshold:r=u.iouThreshold,scoreThreshold:a=u.scoreThreshold,softNmsSigma:s=u.softNmsSigma},d=ka.runKernel(zt,l,c);return{selectedIndices:d[0],selectedScores:d[1]}}});const hd=function(t,n,r){return e(this,arguments,function*(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){const o=Ra(e,"boxes","nonMaxSuppressionAsync"),i=Ra(t,"scores","nonMaxSuppressionAsync"),u=ed(o,i,n,r,a,s);n=u.maxOutputSize,r=u.iouThreshold,a=u.scoreThreshold,s=u.softNmsSigma;const l=yield Promise.all([o.data(),i.data()]),c=l[0],d=l[1],{selectedIndices:p,selectedScores:h}=od(c,d,n,r,a,s);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:sc(p,"int32"),selectedScores:sc(h)}})};const fd=Oa({nonMaxSuppressionPadded_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){const o=Ra(e,"boxes","nonMaxSuppression"),i=Ra(t,"scores","nonMaxSuppression"),u=ed(o,i,n,r,a,null),l={boxes:o,scores:i},c={maxOutputSize:u.maxOutputSize,iouThreshold:u.iouThreshold,scoreThreshold:u.scoreThreshold,padToMaxOutputSize:s},d=ka.runKernel(Wt,l,c);return{selectedIndices:d[0],validOutputs:d[1]}}});const md=function(t,n,r){return e(this,arguments,function*(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){const o=Ra(e,"boxes","nonMaxSuppressionAsync"),i=Ra(t,"scores","nonMaxSuppressionAsync"),u=ed(o,i,n,r,a,null),l=u.maxOutputSize,c=u.iouThreshold,d=u.scoreThreshold,[p,h]=yield Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=sd(p,h,l,c,d,s);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:sc(f,"int32"),validOutputs:_i(m,"int32")}})};const gd=Oa({resizeBilinear_:function(e,t,n=!1,r=!1){const a=Ra(e,"images","resizeBilinear");u(3===a.rank||4===a.rank,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),u(2===t.length,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),u(!1===r||!1===n,()=>"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");let s=a,o=!1;3===a.rank&&(o=!0,s=wo(a,[1,a.shape[0],a.shape[1],a.shape[2]]));const i={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=ka.runKernel(on,i,l);return o?wo(c,[c.shape[1],c.shape[2],c.shape[3]]):c}});const yd=Oa({resizeNearestNeighbor_:function(e,t,n=!1,r=!1){const a=Ra(e,"images","resizeNearestNeighbor");u(3===a.rank||4===a.rank,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),u(2===t.length,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),u("float32"===a.dtype||"int32"===a.dtype,()=>"`images` must have `int32` or `float32` as dtype"),u(!1===r||!1===n,()=>"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");let s=a,o=!1;3===a.rank&&(o=!0,s=wo(a,[1,a.shape[0],a.shape[1],a.shape[2]]));const i={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=ka.runKernel(an,i,l);return o?wo(c,[c.shape[1],c.shape[2],c.shape[3]]):c}});const bd=Oa({threshold_:function(e,t="binary",n=!1,r=.5){const a=Ra(e,"image","threshold"),s=a.shape[0]*a.shape[1];let o,i,l,c,d=Hs(sc([r]),255);if(u(3===a.rank,()=>`Error in threshold: image must be rank 3,but got rank ${a.rank}.`),u(3===a.shape[2]||1===a.shape[2],()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${a.shape[2]}.`),u("int32"===a.dtype||"float32"===a.dtype,()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${a.dtype}.`),u("otsu"===t||"binary"===t,()=>`Method must be binary or otsu, but was ${t}`),3===a.shape[2]){[o,i,l]=Ql(a,[1,1,1],-1);const e=Hs(o,.2989),t=Hs(i,.587),n=Hs(l,.114);c=zs(zs(e,t),n)}else c=e;if("otsu"===t){d=function(e,t){let n,r,a,s,o,i,u=sc([-1]),l=sc([0]),c=sc([0]);for(let d=0;d<e.size-1;d++){n=Co(e,0,d+1),r=Co(e,d+1),o=Gs(Fi(n),t),i=Gs(Fi(r),t);const p=Fi(Hs(n,Sl(0,n.size)));a=Gs(p,Fi(n));const h=Vo(r.shape,n.size),f=zs(Sl(0,r.size),h),m=Hs(r,f);s=Gs(Fi(m),Fi(r));const g=lu(a,s),y=lu(a,s),b=Hs(o,i);c=Hs(Hs(b,g),y);const x=Hi(c,l);l=pi(x,c,l),u=pi(x,sc([d]),u)}return u}(Do(Bs(Dl(c),"int32"),Ma([]),256),s)}const p=n?Ji(c,d):Hi(c,d);return Bs(Hs(p,255),"int32")}});const xd=Oa({transform_:function(e,t,n="nearest",r="constant",a=0,s){const o=Ra(e,"image","transform","float32"),i=Ra(t,"transforms","transform","float32");u(4===o.rank,()=>`Error in transform: image must be rank 4,but got rank ${o.rank}.`),u(2===i.rank&&(i.shape[0]===o.shape[0]||1===i.shape[0])&&8===i.shape[1],()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),u(null==s||2===s.length,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);const l={image:o,transforms:i},c={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return ka.runKernel(jn,l,c)}});const vd=Oa({bandPart_:function(e,t,n){const r=Ra(e,"a","bandPart");u(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);const a=r.shape,[s,o]=r.shape.slice(-2);let i,l;"number"===typeof t?(u(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),u(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),i=Ra(t<0?s:t,"numLower","bandPart")):(u("int32"===t.dtype,()=>"bandPart(): numLower's dtype must be an int32."),i=pi(Zi(t,0),s,Cu(t,s))),"number"===typeof n?(u(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`),u(n<=o,()=>`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`),l=Ra(n<0?o:n,"numUpper","bandPart")):(u("int32"===n.dtype,()=>"bandPart(): numUpper's dtype must be an int32."),l=pi(Zi(n,0),o,Cu(n,o)));const c=wo(Sl(0,s,1,"int32"),[-1,1]),d=Sl(0,o,1,"int32"),p=lu(c,d),h=pu(Ji(p,i),ji(p,ou(l))),f=Nu([s,o],r.dtype);return wo(tc(xc(wo(r,[-1,s,o])).map(e=>pi(h,e,f))),a)}});const wd=Oa({gramSchmidt_:function(e){let t;if(Array.isArray(e)){t=!1,u(null!=e&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const n=e[0].shape[0];for(let t=1;t<e.length;++t)u(e[t].shape[0]===n,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[t].shape[0]} vs. ${n})`)}else t=!0,e=Ql(e,e.shape[0],0).map(e=>ec(e,[0]));u(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],r=e;for(let a=0;a<e.length;++a)n.push(ka.tidy(()=>{let e=r[a];if(a>0)for(let t=0;t<a;++t){const r=Hs(Fi(Hs(n[t],e)),n[t]);e=lu(e,r)}return Gs(e,Mi(e,"euclidean"))}));return t?tc(n,0):n}});function kd(e,t=!1){return ka.tidy(()=>{u(2===e.shape.length,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);const n=e.shape[0],r=e.shape[1];let a=zi(n),s=Vs(e);const o=oc([[1]],[1,1]);let i=Vs(o);const l=n>=r?r:n;for(let e=0;e<l;++e){const t=s,u=i,l=a;[i,s,a]=ka.tidy(()=>{const t=Co(s,[e,e],[n-e,1]),u=Mi(t),l=Co(s,[e,e],[1,1]),c=pi(Hi(l,0),oc([[-1]]),oc([[1]])),d=lu(l,Hs(c,u)),p=Gs(t,d);i=1===p.shape[0]?Vs(o):No([o,Co(p,[1,0],[p.shape[0]-1,p.shape[1]])],0);const h=ou(Gs(So(c,d),u)),f=Co(s,[e,0],[n-e,r]),m=Hs(h,i),g=Sc(i);if(0===e)s=lu(f,So(m,So(g,f)));else{const t=lu(f,So(m,So(g,f)));s=No([Co(s,[0,0],[e,r]),t],0)}const y=Sc(m),b=Co(a,[0,e],[n,a.shape[1]-e]);if(0===e)a=lu(b,So(So(b,i),y));else{const t=lu(b,So(So(b,i),y));a=No([Co(a,[0,0],[n,e]),t],1)}return[i,s,a]}),Wa([t,u,l])}return!t&&n>r&&(a=Co(a,[0,0],[n,r]),s=Co(s,[0,0],[r,r])),[a,s]})}const Id=Oa({qr_:function(e,t=!1){if(u(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),2===e.rank)return kd(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((e,t)=>e*t),r=xc(wo(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(e=>{const[n,r]=kd(e,t);a.push(n),s.push(r)});return[wo(tc(a,0),e.shape),wo(tc(s,0),e.shape)]}}});var Nd,Sd;(Sd=Nd||(Nd={}))[Sd.NONE=0]="NONE",Sd[Sd.MEAN=1]="MEAN",Sd[Sd.SUM=2]="SUM",Sd[Sd.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";const Td=Oa({computeWeightedLoss_:function(e,t,n=Nd.SUM_BY_NONZERO_WEIGHTS){const r=Ra(e,"losses","computeWeightedLoss");let a=null;null!=t&&(a=Ra(t,"weights","computeWeightedLoss"));const s=null==a?r:Hs(r,a);if(n===Nd.NONE)return s;if(n===Nd.SUM)return Fi(s);if(n===Nd.MEAN){if(null==a)return Iu(s);{const e=r.size/a.size,t=Gs(Fi(s),Fi(a));return e>1?Gs(t,_i(e)):t}}if(n===Nd.SUM_BY_NONZERO_WEIGHTS){if(null==a)return Gs(Fi(s),_i(r.size));{const e=Hs(a,Su(r.shape)),t=Bs(Fi(Ou(e,_i(0))),"float32");return Gs(Fi(s),t)}}throw Error(`Unknown reduction: ${n}`)}});const Cd=Oa({absoluteDifference_:function(e,t,n,r=Nd.SUM_BY_NONZERO_WEIGHTS){const a=Ra(e,"labels","absoluteDifference"),s=Ra(t,"predictions","absoluteDifference");let o=null;null!=n&&(o=Ra(n,"weights","absoluteDifference")),l(a.shape,s.shape,"Error in absoluteDifference: ");const i=js(lu(a,s));return Td(i,o,r)}});const $d=Oa({cosineDistance_:function(e,t,n,r,a=Nd.SUM_BY_NONZERO_WEIGHTS){const s=Ra(e,"labels","cosineDistance"),o=Ra(t,"predictions","cosineDistance");let i=null;null!=r&&(i=Ra(r,"weights","cosineDistance")),l(s.shape,o.shape,"Error in cosineDistance: ");const u=_i(1),c=lu(u,Fi(Hs(s,o),n,!0));return Td(c,i,a)}});const Ed=Oa({hingeLoss_:function(e,t,n,r=Nd.SUM_BY_NONZERO_WEIGHTS){let a=Ra(e,"labels","hingeLoss");const s=Ra(t,"predictions","hingeLoss");let o=null;null!=n&&(o=Ra(n,"weights","hingeLoss")),l(a.shape,s.shape,"Error in hingeLoss: ");const i=_i(1);a=lu(Hs(_i(2),a),i);const u=$l(lu(i,Hs(a,s)));return Td(u,o,r)}});const Rd=Oa({huberLoss_:function(e,t,n,r=1,a=Nd.SUM_BY_NONZERO_WEIGHTS){const s=Ra(e,"labels","huberLoss"),o=Ra(t,"predictions","huberLoss");let i=null;null!=n&&(i=Ra(n,"weights","huberLoss")),l(s.shape,o.shape,"Error in huberLoss: ");const u=_i(r),c=js(lu(o,s)),d=Cu(c,u),p=lu(c,d),h=zs(Hs(_i(.5),Oi(d)),Hs(u,p));return Td(h,i,a)}});const _d=Oa({logLoss_:function(e,t,n,r=1e-7,a=Nd.SUM_BY_NONZERO_WEIGHTS){const s=Ra(e,"labels","logLoss"),o=Ra(t,"predictions","logLoss");let i=null;null!=n&&(i=Ra(n,"weights","logLoss")),l(s.shape,o.shape,"Error in logLoss: ");const u=_i(1),c=_i(r),d=ou(Hs(s,nu(zs(o,c)))),p=Hs(lu(u,s),nu(zs(lu(u,o),c))),h=lu(d,p);return Td(h,i,a)}});const Ad=Oa({meanSquaredError_:function(e,t,n,r=Nd.SUM_BY_NONZERO_WEIGHTS){const a=Ra(e,"labels","meanSquaredError"),s=Ra(t,"predictions","meanSquaredError");let o=null;null!=n&&(o=Ra(n,"weights","meanSquaredError")),l(a.shape,s.shape,"Error in meanSquaredError: ");const i=Jl(a,s);return Td(i,o,r)}});const Od=Oa({sigmoidCrossEntropy_:function(e,t,n,r=0,a=Nd.SUM_BY_NONZERO_WEIGHTS){let s=Ra(e,"multiClassLabels","sigmoidCrossEntropy");const o=Ra(t,"logits","sigmoidCrossEntropy");let i=null;if(null!=n&&(i=Ra(n,"weights","sigmoidCrossEntropy")),l(s.shape,o.shape,"Error in sigmoidCrossEntropy: "),r>0){const e=_i(r),t=_i(1),n=_i(.5);s=zs(Hs(s,lu(t,e)),Hs(n,e))}const u=function(e,t){const n=Ra(e,"labels","sigmoidCrossEntropyWithLogits"),r=Ra(t,"logits","sigmoidCrossEntropyWithLogits");l(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");const a=$l(r),s=Hs(r,n),o=ru(Li(ou(js(r))));return zs(lu(a,s),o)}(s,o);return Td(u,i,a)}});const Fd=Oa({softmaxCrossEntropy_:function(e,t,n,r=0,a=Nd.SUM_BY_NONZERO_WEIGHTS){let s=Ra(e,"onehotLabels","softmaxCrossEntropy");const o=Ra(t,"logits","softmaxCrossEntropy");let i=null;if(null!=n&&(i=Ra(n,"weights","softmaxCrossEntropy")),l(s.shape,o.shape,"Error in softmaxCrossEntropy: "),r>0){const e=_i(r),t=_i(1),n=_i(s.shape[1]);s=zs(Hs(s,lu(t,e)),Gs(e,n))}const u=function(e,t,n=-1){if(-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank ${t.rank} and dim was ${n}`);return su((e,t,r)=>{const a=du(t,[n],!0),s=lu(Bs(t,"float32"),a);r([e,s]);const o=ou(Hs(s,e));return{value:Fi(o,[n]),gradFunc:(e,t)=>{const[r,a]=t,s=Ii(e.shape,[n]);return[Hs(wo(e,s),lu(Bs(r,"float32"),Li(a))),Hs(wo(e,s),lu(Li(a),Bs(r,"float32")))]}}})(e,t)}(s,o);return Td(u,i,a)}});const Dd={fft:Kl,ifft:Xl,rfft:Zl,irfft:Yl},Md={hammingWindow:Hc,hannWindow:jc,frame:qc,stft:Kc},Pd={flipLeftRight:Yc,grayscaleToRGB:Qc,resizeNearestNeighbor:yd,resizeBilinear:gd,rgbToGrayscale:Zc,rotateWithOffset:Jc,cropAndResize:Xc,nonMaxSuppression:td,nonMaxSuppressionAsync:dd,nonMaxSuppressionWithScore:pd,nonMaxSuppressionWithScoreAsync:hd,nonMaxSuppressionPadded:fd,nonMaxSuppressionPaddedAsync:md,threshold:bd,transform:xd},Ld={bandPart:vd,gramSchmidt:wd,qr:Id},Bd={absoluteDifference:Cd,computeWeightedLoss:Td,cosineDistance:$d,hingeLoss:Ed,huberLoss:Rd,logLoss:_d,meanSquaredError:Ad,sigmoidCrossEntropy:Od,softmaxCrossEntropy:Fd},Vd={sparseFillEmptyRows:Oa({sparseFillEmptyRows_:function(e,t,n,r){const a=Ra(e,"indices","sparseFillEmptyRows","int32"),s=Ra(t,"values","sparseFillEmptyRows"),o=Ra(n,"denseShape","sparseFillEmptyRows","int32"),i=Ra(r,"defaultValue","sparseFillEmptyRows",s.dtype);if(2!==a.rank)throw new Error(`Indices should be Tensor2D but received shape\n        ${a.shape}`);if(1!==s.rank)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(1!==o.rank)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(0!==i.rank)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);const u={indices:a,values:s,denseShape:o,defaultValue:i},l=ka.runKernel(En,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}}),sparseReshape:Oa({sparseReshape_:function(e,t,n){const r=Ra(e,"inputIndices","sparseReshape","int32"),a=Ra(t,"inputShape","sparseReshape","int32"),s=Ra(n,"newShape","sparseReshape","int32");if(2!==r.rank)throw new Error(`Input indices should be Tensor2D but received shape\n        ${r.shape}`);if(1!==a.rank)throw new Error(`Input shape should be Tensor1D but received shape ${a.shape}`);if(1!==s.rank)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);const o={inputIndices:r,inputShape:a,newShape:s},i=ka.runKernel(Rn,o);return{outputIndices:i[0],outputShape:i[1]}}}),sparseSegmentMean:Oa({sparseSegmentMean_:function(e,t,n){const r=Ra(e,"data","sparseSegmentMean"),a=Ra(t,"indices","sparseSegmentMean","int32"),s=Ra(n,"segmentIds","sparseSegmentMean","int32");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.rank)throw new Error(`Indices should be Tensor1D but received shape\n          ${a.shape}`);if(1!==s.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n          ${s.shape}`);const o={data:r,indices:a,segmentIds:s};return ka.runKernel(_n,o)}}),sparseSegmentSum:Oa({sparseSegmentSum_:function(e,t,n){const r=Ra(e,"data","sparseSegmentSum"),a=Ra(t,"indices","sparseSegmentSum","int32"),s=Ra(n,"segmentIds","sparseSegmentSum","int32");if(r.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.rank)throw new Error(`Indices should be Tensor1D but received shape\n         ${a.shape}`);if(1!==s.rank)throw new Error(`Segment ids should be Tensor1D but received shape\n         ${s.shape}`);const o={data:r,indices:a,segmentIds:s};return ka.runKernel(An,o)}})},Wd={stringNGrams:Oa({stringNGrams_:function(e,t,n,r,a,s,o,i){const u=Ra(e,"data","stringNGrams","string");if("string"!==u.dtype)throw new Error("Data must be of datatype string");if(1!==u.shape.length)throw new Error(`Data must be a vector, saw: ${u.shape}`);const l=Ra(t,"dataSplits","stringNGrams");if("int32"!==l.dtype)throw new Error("Data splits must be of datatype int32");const c={separator:n,nGramWidths:r,leftPad:a,rightPad:s,padWidth:o,preserveShortSequences:i},d={data:u,dataSplits:l},p=ka.runKernel(Ln,d,c);return{nGrams:p[0],nGramsSplits:p[1]}}}),stringSplit:Oa({stringSplit_:function(e,t,n=!0){const r=Ra(e,"input","stringSplit","string"),a=Ra(t,"delimiter","stringSplit","string");if(1!==r.rank)throw new Error(`Input should be Tensor1D but received shape ${r.shape}`);if(0!==a.rank)throw new Error(`Delimiter should be a scalar but received shape ${a.shape}`);const s={skipEmpty:n},o={input:r,delimiter:a},i=ka.runKernel(Bn,o,s);return{indices:i[0],values:i[1],shape:i[2]}}}),stringToHashBucketFast:Oa({stringToHashBucketFast_:function(e,t){const n=Ra(e,"input","stringToHashBucketFast","string"),r={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");const a={input:n};return ka.runKernel(Vn,a,r)}}),staticRegexReplace:Oa({staticRegexReplace_:function(e,t,n,r=!0){const a=Ra(e,"input","staticRegexReplace","string"),s={pattern:t,rewrite:n,replaceGlobal:r};return ka.runKernel(Mn,{x:a},s)}})},zd=new Map,Ud=new Map;class Gd{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class Hd{constructor(){this.classNameMap={}}static getMap(){return null==Hd.instance&&(Hd.instance=new Hd),Hd.instance}static register(e){Hd.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function jd(e,t,n){u(null!=e.className,()=>"Class being registered does not have the static className property defined."),u("string"===typeof e.className,()=>"className is required to be a string, but got type "+typeof e.className),u(e.className.length>0,()=>"Class being registered has an empty-string as its className, which is disallowed."),"undefined"===typeof t&&(t="Custom"),"undefined"===typeof n&&(n=e.className);const r=t+">"+n;return Hd.register(e),zd.set(r,e),Ud.set(e,r),e}class qd extends Gd{minimize(e,t=!1,n){const{value:r,grads:a}=this.computeGradients(e,n);if(null!=n){const e=n.map(e=>({name:e.name,tensor:a[e.name]}));this.applyGradients(e)}else this.applyGradients(a);return Wa(a),t?r:(r.dispose(),null)}get iterations(){return null==this.iterations_&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return au(e,t)}dispose(){null!=this.iterations_&&Wa(this.iterations_)}saveIterations(){return e(this,null,function*(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:_i(this.iterations_,"int32")}})}getWeights(){return e(this,null,function*(){throw new Error("getWeights() is not implemented for this optimizer yet.")})}setWeights(t){return e(this,null,function*(){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)})}extractIterations(t){return e(this,null,function*(){return this.iterations_=(yield t[0].tensor.data())[0],t.slice(1)})}}Object.defineProperty(qd,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class Kd extends qd{static get className(){return"Adadelta"}constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=ka.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=ka.registeredVariables[t],a=!1;null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:Va(()=>hi(r).variable(a))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:Va(()=>hi(r).variable(a))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedGrads[n].variable,i=this.accumulatedUpdates[n].variable;Va(()=>{const e=zs(Hs(o,this.rho),Hs(Oi(s),1-this.rho)),t=Hs(Gs(Ai(zs(i,this.epsilon)),Ai(zs(o,this.epsilon))),s),n=zs(Hs(i,this.rho),Hs(Oi(t),1-this.rho));o.assign(e),i.assign(n);const a=zs(Hs(t,-this.learningRate),r);r.assign(a)})}),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(Wa(this.accumulatedGrads.map(e=>e.variable)),Wa(this.accumulatedUpdates.map(e=>e.variable)))}getWeights(){return e(this,null,function*(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[yield this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))})}setWeights(t){return e(this,null,function*(){const e=(t=yield this.extractIterations(t)).length/2,n=!1;this.accumulatedGrads=t.slice(0,e).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.accumulatedUpdates=t.slice(e,2*e).map(e=>({originalName:e.name,variable:e.tensor.variable(n)}))})}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}class Xd extends qd{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=ka.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:Va(()=>Vo(r.shape,this.initialAccumulatorValue).variable(e))}}const a=Array.isArray(e)?e[n].tensor:e[t];if(null==a)return;const s=this.accumulatedGrads[n].variable;Va(()=>{const e=zs(s,Oi(a));s.assign(e);const t=zs(Hs(Gs(a,Ai(zs(e,ka.backend.epsilon()))),-this.learningRate),r);r.assign(t)})}),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&Wa(this.accumulatedGrads.map(e=>e.variable))}getWeights(){return e(this,null,function*(){return[yield this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))})}setWeights(t){return e(this,null,function*(){t=yield this.extractIterations(t);this.accumulatedGrads=t.map(e=>({originalName:e.name,variable:e.tensor.variable(false)}))})}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}}class Yd extends qd{static get className(){return"Adam"}constructor(e,t,n,r=null){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],Va(()=>{this.accBeta1=_i(t).variable(),this.accBeta2=_i(n).variable()}),null==r&&(this.epsilon=ka.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(e=>e.name):Object.keys(e);Va(()=>{const n=lu(1,this.accBeta1),r=lu(1,this.accBeta2);t.forEach((t,a)=>{const s=ka.registeredVariables[t],o=!1;null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Va(()=>hi(s).variable(o))}),null==this.accumulatedSecondMoment[a]&&(this.accumulatedSecondMoment[a]={originalName:`${t}/v`,variable:Va(()=>hi(s).variable(o))});const i=Array.isArray(e)?e[a].tensor:e[t];if(null==i)return;const u=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,c=zs(Hs(u,this.beta1),Hs(i,1-this.beta1)),d=zs(Hs(l,this.beta2),Hs(Oi(i),1-this.beta2)),p=Gs(c,n),h=Gs(d,r);u.assign(c),l.assign(d);const f=zs(Hs(Gs(p,zs(Ai(h),this.epsilon)),-this.learningRate),s);s.assign(f)}),this.accBeta1.assign(Hs(this.accBeta1,this.beta1)),this.accBeta2.assign(Hs(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&Wa(this.accumulatedFirstMoment.map(e=>e.variable)),null!=this.accumulatedSecondMoment&&Wa(this.accumulatedSecondMoment.map(e=>e.variable))}getWeights(){return e(this,null,function*(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[yield this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))})}setWeights(t){return e(this,null,function*(){t=yield this.extractIterations(t),Va(()=>{this.accBeta1.assign(Ri(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ri(this.beta2,this.iterations_+1))});const e=t.length/2,n=!1;this.accumulatedFirstMoment=t.slice(0,e).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.accumulatedSecondMoment=t.slice(e,2*e).map(e=>({originalName:e.name,variable:e.tensor.variable(n)}))})}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}}class Qd extends qd{static get className(){return"Adamax"}constructor(e,t,n,r=null,a=0){super(),this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=r,this.decay=a,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],Va(()=>{this.iteration=_i(0).variable(),this.accBeta1=_i(t).variable()}),null==r&&(this.epsilon=ka.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(e=>e.name):Object.keys(e);Va(()=>{const n=lu(1,this.accBeta1),r=Gs(-this.learningRate,zs(Hs(this.iteration,this.decay),1));t.forEach((t,a)=>{const s=ka.registeredVariables[t],o=!1;null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:hi(s).variable(o)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:hi(s).variable(o)});const i=Array.isArray(e)?e[a].tensor:e[t];if(null==i)return;const u=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,c=zs(Hs(u,this.beta1),Hs(i,1-this.beta1)),d=Hs(l,this.beta2),p=js(i),h=ku(d,p);u.assign(c),l.assign(h);const f=zs(Hs(Gs(r,n),Gs(c,zs(h,this.epsilon))),s);s.assign(f)}),this.iteration.assign(zs(this.iteration,1)),this.accBeta1.assign(Hs(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&Wa(this.accumulatedFirstMoment.map(e=>e.variable)),null!=this.accumulatedWeightedInfNorm&&Wa(this.accumulatedWeightedInfNorm.map(e=>e.variable))}getWeights(){return e(this,null,function*(){throw new Error("getWeights() is not implemented for Adamax yet.")})}setWeights(t){return e(this,null,function*(){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}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}}class Zd extends qd{static get className(){return"SGD"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=Array.isArray(e)?e[n].tensor:e[t];if(null==r)return;const a=ka.registeredVariables[t];Va(()=>{const e=zs(Hs(this.c,r),a);a.assign(e)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=za(_i(-e))}dispose(){this.c.dispose()}getWeights(){return e(this,null,function*(){return[yield this.saveIterations()]})}setWeights(t){return e(this,null,function*(){if(0!==(t=yield this.extractIterations(t)).length)throw new Error("SGD optimizer does not have settable weights.")})}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}}class Jd extends Zd{static get className(){return"Momentum"}constructor(e,t,n=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=_i(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=ka.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:Va(()=>hi(r).variable(e))}}const a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];null!=s&&Va(()=>{let e;const t=zs(Hs(this.m,a),s);e=this.useNesterov?zs(Hs(this.c,zs(s,Hs(t,this.m))),r):zs(Hs(this.c,t),r),a.assign(t),r.assign(e)})}),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&Wa(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}getWeights(){return e(this,null,function*(){return[yield this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))})}setWeights(t){return e(this,null,function*(){t=yield this.extractIterations(t);this.accumulations=t.map(e=>({originalName:e.name,variable:e.tensor.variable(false)}))})}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}class ep extends qd{static get className(){return"RMSProp"}constructor(e,t=.9,n=0,r=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==r&&(this.epsilon=ka.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=ka.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:Va(()=>hi(r).variable(a))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:Va(()=>hi(r).variable(a))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:Va(()=>hi(r).variable(a))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedMeanSquares[n].variable,i=this.accumulatedMoments[n].variable;Va(()=>{const e=zs(Hs(o,this.decay),Hs(Oi(s),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,a=zs(Hs(t,this.decay),Hs(s,1-this.decay)),u=Gs(Hs(s,this.learningRate),Ai(lu(e,zs(Oi(a),this.epsilon)))),l=zs(Hs(i,this.momentum),u);o.assign(e),t.assign(a),i.assign(l);const c=lu(r,l);r.assign(c)}else{const 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e=this.centered?t.length/3:t.length/2,n=!1;this.accumulatedMeanSquares=t.slice(0,e).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.accumulatedMoments=t.slice(e,2*e).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=t.slice(2*e,3*e).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})))})}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}}const tp=[Kd,Xd,Yd,Qd,Jd,ep,Zd];function np(e){return new Promise(e=>setTimeout(e)).then(e)}class rp{constructor(e){if(!W().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");e.startsWith(rp.URL_SCHEME)&&(e=e.slice(rp.URL_SCHEME.length)),null!=e&&0!==e.length||(e="model"),this.modelJsonFileName=e+".json",this.weightDataFileName=e+".weights.bin"}save(t){return e(this,null,function*(){if("undefined"===typeof document)throw new Error("Browser downloads are not supported in this environment since `document` is not present");const e=La.join(t.weightData),n=window.URL.createObjectURL(new Blob([e],{type:"application/octet-stream"}));if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");{const e=[{paths:["./"+this.weightDataFileName],weights:t.weightSpecs}],r=rs(t,e),a=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),s=null==this.modelJsonAnchor?document.createElement("a"):this.modelJsonAnchor;if(s.download=this.modelJsonFileName,s.href=a,yield np(()=>s.dispatchEvent(new MouseEvent("click"))),null!=t.weightData){const 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BrowserFiles supports loading Keras-style tf.Model artifacts only.`),n.readAsText(this.jsonFile)})})}loadWeights(e){const t=[],n=[];for(const s of e)t.push(...s.weights),n.push(...s.paths);const r=this.checkManifestAndWeightFiles(e),a=n.map(e=>this.loadWeightsFile(e,r[e]));return Promise.all(a).then(e=>[t,e])}loadWeightsFile(e,t){return new Promise((n,r)=>{const a=new FileReader;a.onload=e=>{const t=e.target.result;n(t)},a.onerror=t=>r(`Failed to weights data from file of path '${e}'.`),a.readAsArrayBuffer(t)})}checkManifestAndWeightFiles(e){const t=[],n=this.weightsFiles.map(e=>ns(e.name)),r={};for(const a of e)a.paths.forEach(e=>{const a=ns(e);if(-1!==t.indexOf(a))throw new Error(`Duplicate file basename found in weights manifest: '${a}'`);if(t.push(a),-1===n.indexOf(a))throw new Error(`Weight file with basename '${a}' is not provided.`);r[e]=this.weightsFiles[n.indexOf(a)]});if(t.length!==this.weightsFiles.length)throw new Error(`Mismatch in the number of files in weights manifest (${t.length}) and the number of weight files provided (${this.weightsFiles.length}).`);return r}}function sp(e,t,n,r){var a,s,o;u(null!=(a=e)&&Array.isArray(a)&&a.length>0,()=>"promises must be a none empty array"),o=r=null==r?1:r,u((s=n=null==n?0:n)>=0&&s<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${s}`),u(o>=0&&o<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${o}`),u(o>=s,()=>`startFraction must be no more than endFraction, but got startFraction ${s} and endFraction ${o}`);let i=0;return Promise.all(e.map(a=>(a.then(a=>{const s=n+ ++i/e.length*(r-n);return t(s),a}),a)))}function op(t,n){return e(this,null,function*(){null==n&&(n={});const e=null==n.fetchFunc?W().platform.fetch:n.fetchFunc,r=t.map(t=>e(t,n.requestInit,{isBinary:!0})),a=(null==n.onProgress?yield Promise.all(r):yield sp(r,n.onProgress,0,.5)).map(e=>e.arrayBuffer());return null==n.onProgress?yield Promise.all(a):yield sp(a,n.onProgress,.5,1)})}function ip(t){return(n,r="",a)=>e(null,null,function*(){const e=n.map(()=>!1),s={},o=null!=a?a.map(()=>!1):[],i=[];if(n.forEach((t,n)=>{let r=0;t.weights.forEach(t=>{const u="quantization"in t?t.quantization.dtype:t.dtype,l=Pa[u]*d(t.shape),c=()=>{e[n]=!0,null==s[n]&&(s[n]=[]),s[n].push({manifestEntry:t,groupOffset:r,sizeBytes:l})};null!=a?a.forEach((e,n)=>{e===t.name&&(c(),o[n]=!0)}):c(),i.push(t.name),r+=l})}),!o.every(e=>e)){const e=a.filter((e,t)=>!o[t]);throw new Error(`Could not find weights in manifest with names: ${e.join(", ")}. \nManifest JSON has weights with names: ${i.join(", ")}.`)}const u=e.reduce((e,t,n)=>(t&&e.push(n),e),[]),l=[];u.forEach(e=>{n[e].paths.forEach(e=>{const t=r+(r.endsWith("/")?"":"/")+e;l.push(t)})});const c=yield t(l),p={};let h=0;return u.forEach(e=>{const t=n[e].paths.length,r=new La(c.slice(h,h+t));s[e].forEach(e=>{const t=qa(r.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(const n in t)p[n]=t[n]}),h+=t}),p})}us.registerSaveRouter(e=>W().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(rp.URL_SCHEME)?function(e="model"){return new rp(e)}(e.slice(rp.URL_SCHEME.length)):null);class up{constructor(e,t){if(this.DEFAULT_METHOD="POST",null==t&&(t={}),this.weightPathPrefix=t.weightPathPrefix,this.weightUrlConverter=t.weightUrlConverter,null!=t.fetchFunc?(u("function"===typeof t.fetchFunc,()=>"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetch=t.fetchFunc):this.fetch=W().platform.fetch,u(null!=e&&e.length>0,()=>"URL path for http must not be null, undefined or empty."),Array.isArray(e)&&u(2===e.length,()=>`URL paths for http must have a length of 2, (actual length is ${e.length}).`),this.path=e,null!=t.requestInit&&null!=t.requestInit.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t.requestInit||{},this.loadOptions=t}save(t){return e(this,null,function*(){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");const e=Object.assign({method:this.DEFAULT_METHOD},this.requestInit);e.body=new FormData;const n=[{paths:["./model.weights.bin"],weights:t.weightSpecs}],r=rs(t,n);if(e.body.append("model.json",new Blob([JSON.stringify(r)],{type:"application/json"}),"model.json"),null!=t.weightData){const n=La.join(t.weightData);e.body.append("model.weights.bin",new Blob([n],{type:"application/octet-stream"}),"model.weights.bin")}const a=yield this.fetch(this.path,e);if(a.ok)return{modelArtifactsInfo:os(t),responses:[a]};throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ${a.status}.`)})}loadModelJSON(){return e(this,null,function*(){const e=yield this.fetch(this.path,this.requestInit);if(!e.ok)throw new Error(`Request to ${this.path} failed with status code ${e.status}. Please verify this URL points to the model JSON of the model to load.`);let t;try{t=yield e.json()}catch(a){let e=`Failed to parse model JSON of response from ${this.path}.`;throw this.path.endsWith(".pb")?e+=" Your path contains a .pb file extension. Support for .pb models have been removed in TensorFlow.js 1.0 in favor of .json models. 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e=this.tensors.map(e=>wo(e,n));return No(e,0)})}}const Mf=(t,n,r)=>e(null,null,function*(){switch(t.op){case"If":case"StatelessIf":{const e=Bh("thenBranch",t,n,r),a=Bh("elseBranch",t,n,r),s=Bh("cond",t,n,r),o=Bh("args",t,n,r);return(yield s.data())[0]?r.functionMap[e].executeFunctionAsync(o,r.tensorArrayMap,r.tensorListMap):r.functionMap[a].executeFunctionAsync(o,r.tensorArrayMap,r.tensorListMap)}case"While":case"StatelessWhile":{const e=Bh("body",t,n,r),a=Bh("cond",t,n,r),s=Bh("args",t,n,r),o=yield r.functionMap[a].executeFunctionAsync(s,r.tensorArrayMap,r.tensorListMap),i=s.map(e=>e.id);let u=yield o[0].data();o.forEach(e=>{e.kept||-1!==i.indexOf(e.id)||e.dispose()});let l=s;for(;u[0];){const t=l;l=yield r.functionMap[e].executeFunctionAsync(l,r.tensorArrayMap,r.tensorListMap);const n=l.map(e=>e.id);t.forEach(e=>{e.kept||-1!==i.indexOf(e.id)||-1!==n.indexOf(e.id)||e.dispose()});const s=yield r.functionMap[a].executeFunctionAsync(l,r.tensorArrayMap,r.tensorListMap);u=yield s[0].data(),s.forEach(e=>{e.kept||-1!==i.indexOf(e.id)||-1!==n.indexOf(e.id)||e.dispose()})}return l}case"LoopCond":return[jh(Bh("pred",t,n,r))];case"Switch":{const e=Bh("pred",t,n,r);let a=Bh("data",t,n,r);return a.kept||(a=jh(a)),(yield e.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{const e=t.inputNames.find(e=>void 0!==Vh(e,n,r));if(e){return[jh(Vh(e,n,r))]}return}case"Enter":{const e=Bh("frameName",t,n,r),a=Bh("tensor",t,n,r);return r.enterFrame(e),[jh(a)]}case"Exit":{const e=Bh("tensor",t,n,r);return r.exitFrame(),[jh(e)]}case"NextIteration":{const e=Bh("tensor",t,n,r);return r.nextIteration(),[jh(e)]}case"TensorArrayV3":{const e=Bh("size",t,n,r),a=Bh("dtype",t,n,r),s=Bh("elementShape",t,n,r),o=Bh("dynamicSize",t,n,r),i=Bh("clearAfterRead",t,n,r),u=Bh("identicalElementShapes",t,n,r),l=Bh("name",t,n,r),c=new Ff(l,a,e,s,u,o,i);return r.addTensorArray(c),[c.idTensor,_i(1)]}case"TensorArrayWriteV3":{const e=Bh("tensorArrayId",t,n,r),a=Bh("index",t,n,r),s=Bh("tensor",t,n,r),o=r.getTensorArray(e.id);return o.write(a,s),[o.idTensor]}case"TensorArrayReadV3":{const e=Bh("tensorArrayId",t,n,r),a=Bh("index",t,n,r);return[r.getTensorArray(e.id).read(a)]}case"TensorArrayGatherV3":{const e=Bh("tensorArrayId",t,n,r),a=Bh("indices",t,n,r),s=Bh("dtype",t,n,r);return[r.getTensorArray(e.id).gather(a,s)]}case"TensorArrayScatterV3":{const e=Bh("tensorArrayId",t,n,r),a=Bh("indices",t,n,r),s=Bh("tensor",t,n,r),o=r.getTensorArray(e.id);return o.scatter(a,s),[o.idTensor]}case"TensorArrayConcatV3":{const e=Bh("tensorArrayId",t,n,r),a=r.getTensorArray(e.id),s=Bh("dtype",t,n,r);return[a.concat(s)]}case"TensorArraySplitV3":{const e=Bh("tensorArrayId",t,n,r),a=Bh("tensor",t,n,r),s=Bh("lengths",t,n,r),o=r.getTensorArray(e.id);return o.split(s,a),[o.idTensor]}case"TensorArraySizeV3":{const e=Bh("tensorArrayId",t,n,r);return[_i(r.getTensorArray(e.id).size(),"int32")]}case"TensorArrayCloseV3":{const e=Bh("tensorArrayId",t,n,r),a=r.getTensorArray(e.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{const e=Bh("tensorListId",t,n,r),a=Bh("index",t,n,r),s=Bh("tensor",t,n,r),o=r.getTensorList(e.id);return o.setItem(a,s),[o.idTensor]}case"TensorListGetItem":{const e=Bh("tensorListId",t,n,r),a=Bh("index",t,n,r),s=Bh("elementShape",t,n,r),o=Bh("elementDType",t,n,r);return[r.getTensorList(e.id).getItem(a,s,o)]}case"TensorListScatterV2":case"TensorListScatter":{const e=Bh("indices",t,n,r),a=function(e,t,n,r){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);const a=Math.max(...t);if(null!=r&&-1!==r&&a>=r)throw new Error(`Max index must be < array size (${a}  vs. ${r})`);const s=new Df([],n,e.dtype,r),o=xc(e,0);return t.forEach((e,t)=>{s.setItem(e,o[t])}),s}(Bh("tensor",t,n,r),e,Bh("elementShape",t,n,r),Bh("numElements",t,n,r));return r.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{const e=Bh("elementShape",t,n,r),a=Bh("elementDType",t,n,r);let s;s="TensorListReserve"===t.op?"numElements":"maxNumElements";const o=Bh(s,t,n,r),i=function(e,t,n,r){return new Df([],e,t,r)}(e,a,0,"TensorListReserve"===t.op?-1:o);return r.addTensorList(i),[i.idTensor]}case"TensorListGather":{const e=Bh("tensorListId",t,n,r),a=Bh("indices",t,n,r),s=Bh("elementShape",t,n,r),o=Bh("elementDType",t,n,r);return[r.getTensorList(e.id).gather(a,o,s)]}case"TensorListStack":{const e=Bh("tensorListId",t,n,r),a=Bh("elementShape",t,n,r),s=Bh("elementDType",t,n,r),o=Bh("numElements",t,n,r);return[r.getTensorList(e.id).stack(a,s,o)]}case"TensorListFromTensor":{const e=function(e,t,n){const r=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);Rf(e.shape.slice(1),t,"TensorList shape mismatch: ");const a=xc(e);return new Df(a,t,r)}(Bh("tensor",t,n,r),Bh("elementShape",t,n,r),Bh("elementDType",t,n,r));return r.addTensorList(e),[e.idTensor]}case"TensorListConcat":case"TensorListConcatV2":{const e=Bh("tensorListId",t,n,r),a=r.getTensorList(e.id),s=Bh("dtype",t,n,r),o=Bh("elementShape",t,n,r);return[a.concat(s,o)]}case"TensorListPushBack":{const e=Bh("tensorListId",t,n,r),a=Bh("tensor",t,n,r),s=r.getTensorList(e.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{const e=Bh("tensorListId",t,n,r),a=Bh("elementShape",t,n,r),s=Bh("elementDType",t,n,r);return[r.getTensorList(e.id).popBack(a,s)]}case"TensorListSplit":{const e=Bh("tensor",t,n,r),a=Bh("elementShape",t,n,r),s=function(e,t,n){let r=0;const a=t.map(e=>(r+=e,r));if(r!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to\n          tensor.shape[0], but sum of lengths is\n        ${r}, and tensor's shape is: ${e.shape}`);const s=Of(e.shape.slice(1),n),o=0===r?0:e.size/r,i=Va(()=>{const n=[];e=wo(e,[1,r,o]);for(let r=0;r<t.length;++r){const i=[0,0===r?0:a[r-1],0],u=[1,t[r],o];n[r]=wo(Co(e,i,u),s)}return e.dispose(),n}),u=new Df([],n,e.dtype,t.length);for(let l=0;l<i.length;l++)u.setItem(l,i[l]);return u}(e,Bh("lengths",t,n,r),a);return r.addTensorList(s),[s.idTensor]}case"TensorListLength":{const e=Bh("tensorListId",t,n,r);return[_i(r.getTensorList(e.id).size(),"int32")]}case"TensorListResize":{const e=Bh("tensorListId",t,n,r),a=Bh("size",t,n,r),s=r.getTensorList(e.id).resize(a);return r.addTensorList(s),[s.idTensor]}default:throw TypeError(`Node type ${t.op} is not implemented`)}});function Pf(e,t,n){const[r,a]=Bh("fusedOps",e,t,n),s="biasadd"===r,o=!s,i="prelu"===a,u="fusedbatchnorm"===r,l=Bh("numArgs",e,t,n);if(s){if(i&&2!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&s&&1!==l)throw new Error("FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.")}if(u)throw new Error("FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported");const c=Bh("strides",e,t,n),d=Hh(e,t,n),p=Bh("dataFormat",e,t,n).toUpperCase(),h=Bh("dilations",e,t,n);let[f,m]=Bh("args",e,t,n);o&&(m=f,f=void 0);return{stride:c,pad:d,dataFormat:p,dilations:h,biasArg:f,preluArg:m,activationFunc:a,leakyreluAlpha:Bh("leakyreluAlpha",e,t,n)}}function Lf(e,t,n){return{boxes:Bh("boxes",e,t,n),scores:Bh("scores",e,t,n),maxOutputSize:Bh("maxOutputSize",e,t,n),iouThreshold:Bh("iouThreshold",e,t,n),scoreThreshold:Bh("scoreThreshold",e,t,n),softNmsSigma:Bh("softNmsSigma",e,t,n)}}class Bf{get id(){return this.handle.id}constructor(e,t){this.keyDType=e,this.valueDType=t,this.handle=_i(0),this.tensorMap=new Map,za(this.handle)}clearAndClose(){this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return _i(this.size(),"int32")}import(t,n){return e(this,null,function*(){this.checkKeyAndValueTensor(t,n);const e=yield t.data();return this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),Va(()=>{const t=xc(n),r=e.length,a=t.length;u(r===a,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);for(let n=0;n<r;n++){const r=e[n],a=t[n];za(a),this.tensorMap.set(r,a)}return this.handle})})}find(t,n){return e(this,null,function*(){this.checkKeyAndValueTensor(t,n);const e=yield t.data();return Va(()=>{const t=[];for(let r=0;r<e.length;r++){const a=e[r],s=this.findWithDefault(a,n);t.push(s)}return tc(t)})})}findWithDefault(e,t){const n=this.tensorMap.get(e);return null!=n?n:t}checkKeyAndValueTensor(e,t){if(e.dtype!==this.keyDType)throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);if(t.dtype!==this.valueDType)throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`)}}function Vf(t,n,r,a,s=Va){const o=((t,n,r)=>{switch(t.category){case"arithmetic":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[r.add(Bh("a",e,t,n),Bh("b",e,t,n))];case"AddN":return[r.addN(Bh("tensors",e,t,n))];case"FloorMod":case"Mod":return[r.mod(Bh("a",e,t,n),Bh("b",e,t,n))];case"Mul":return[r.mul(Bh("a",e,t,n),Bh("b",e,t,n))];case"RealDiv":case"Div":return[r.div(Bh("a",e,t,n),Bh("b",e,t,n))];case"DivNoNan":return[r.divNoNan(Bh("a",e,t,n),Bh("b",e,t,n))];case"FloorDiv":return[r.floorDiv(Bh("a",e,t,n),Bh("b",e,t,n))];case"Sub":return[r.sub(Bh("a",e,t,n),Bh("b",e,t,n))];case"Minimum":return[r.minimum(Bh("a",e,t,n),Bh("b",e,t,n))];case"Maximum":return[r.maximum(Bh("a",e,t,n),Bh("b",e,t,n))];case"Pow":return[r.pow(Bh("a",e,t,n),Bh("b",e,t,n))];case"SquaredDifference":return[r.squaredDifference(Bh("a",e,t,n),Bh("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"basic_math":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"Abs":case"ComplexAbs":return[r.abs(Bh("x",e,t,n))];case"Acos":return[r.acos(Bh("x",e,t,n))];case"Acosh":return[r.acosh(Bh("x",e,t,n))];case"Asin":return[r.asin(Bh("x",e,t,n))];case"Asinh":return[r.asinh(Bh("x",e,t,n))];case"Atan":return[r.atan(Bh("x",e,t,n))];case"Atan2":return[r.atan2(Bh("x",e,t,n),Bh("y",e,t,n))];case"Atanh":return[r.atanh(Bh("x",e,t,n))];case"Ceil":return[r.ceil(Bh("x",e,t,n))];case"Complex":return[r.complex(Bh("real",e,t,n),Bh("imag",e,t,n))];case"Cos":return[r.cos(Bh("x",e,t,n))];case"Cosh":return[r.cosh(Bh("x",e,t,n))];case"Elu":return[r.elu(Bh("x",e,t,n))];case"Erf":return[r.erf(Bh("x",e,t,n))];case"Exp":return[r.exp(Bh("x",e,t,n))];case"Expm1":return[r.expm1(Bh("x",e,t,n))];case"Floor":return[r.floor(Bh("x",e,t,n))];case"Log":return[r.log(Bh("x",e,t,n))];case"Log1p":return[r.log1p(Bh("x",e,t,n))];case"Imag":return[r.imag(Bh("x",e,t,n))];case"Neg":return[r.neg(Bh("x",e,t,n))];case"Reciprocal":return[r.reciprocal(Bh("x",e,t,n))];case"Real":return[r.real(Bh("x",e,t,n))];case"Relu":return[r.relu(Bh("x",e,t,n))];case"Round":return[r.round(Bh("x",e,t,n))];case"Selu":return[r.selu(Bh("x",e,t,n))];case"Sigmoid":return[r.sigmoid(Bh("x",e,t,n))];case"Sin":return[r.sin(Bh("x",e,t,n))];case"Sign":return[r.sign(Bh("x",e,t,n))];case"Sinh":return[r.sinh(Bh("x",e,t,n))];case"Softplus":return[r.softplus(Bh("x",e,t,n))];case"Sqrt":return[r.sqrt(Bh("x",e,t,n))];case"Square":return[r.square(Bh("x",e,t,n))];case"Tanh":return[r.tanh(Bh("x",e,t,n))];case"Tan":return[r.tan(Bh("x",e,t,n))];case"ClipByValue":return[r.clipByValue(Bh("x",e,t,n),Bh("clipValueMin",e,t,n),Bh("clipValueMax",e,t,n))];case"Relu6":return[r.relu6(Bh("x",e,t,n))];case"Rsqrt":return[r.rsqrt(Vh(e.inputNames[0],t,n))];case"LeakyRelu":return[r.leakyRelu(Bh("x",e,t,n),Bh("alpha",e,t,n))];case"Prelu":return[r.prelu(Bh("x",e,t,n),Bh("alpha",e,t,n))];case"IsNan":return[r.isNaN(Vh(e.inputNames[0],t,n))];case"IsInf":return[r.isInf(Vh(e.inputNames[0],t,n))];case"IsFinite":return[r.isFinite(Vh(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"control":return Mf(t,n,r);case"convolution":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"Conv1D":{const a=Bh("stride",e,t,n),s=Bh("pad",e,t,n),o=Bh("dataFormat",e,t,n).toUpperCase(),i=Bh("dilation",e,t,n);return[r.conv1d(Bh("x",e,t,n),Bh("filter",e,t,n),a,s,o,i)]}case"Conv2D":{const a=Bh("strides",e,t,n),s=Hh(e,t,n),o=Bh("dataFormat",e,t,n).toUpperCase(),i=Bh("dilations",e,t,n);return[r.conv2d(Bh("x",e,t,n),Bh("filter",e,t,n),[a[1],a[2]],s,o,[i[1],i[2]])]}case"_FusedConv2D":{const{stride:a,pad:s,dataFormat:o,dilations:i,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:d}=Pf(e,t,n);return[r.fused.conv2d({x:Bh("x",e,t,n),filter:Bh("filter",e,t,n),strides:[a[1],a[2]],pad:s,dataFormat:o,dilations:[i[1],i[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:d})]}case"FusedDepthwiseConv2dNative":{const{stride:a,pad:s,dataFormat:o,dilations:i,biasArg:u,preluArg:l,activationFunc:c,leakyreluAlpha:d}=Pf(e,t,n);return[r.fused.depthwiseConv2d({x:Bh("x",e,t,n),filter:Bh("filter",e,t,n),strides:[a[1],a[2]],pad:s,dataFormat:o,dilations:[i[1],i[2]],bias:u,activation:c,preluActivationWeights:l,leakyreluAlpha:d})]}case"Conv2DBackpropInput":case"Conv2dTranspose":{const a=Bh("outputShape",e,t,n),s=Bh("strides",e,t,n),o=Hh(e,t,n);return[r.conv2dTranspose(Bh("x",e,t,n),Bh("filter",e,t,n),a,[s[1],s[2]],o)]}case"DepthwiseConv2dNative":case"DepthwiseConv2d":{const a=Bh("strides",e,t,n),s=Hh(e,t,n),o=Bh("dilations",e,t,n),i=Bh("dataFormat",e,t,n).toUpperCase();return[r.depthwiseConv2d(Bh("input",e,t,n),Bh("filter",e,t,n),[a[1],a[2]],s,i,[o[1],o[2]])]}case"Conv3D":{const a=Bh("strides",e,t,n),s=Bh("pad",e,t,n),o=Bh("dataFormat",e,t,n).toUpperCase(),i=Bh("dilations",e,t,n);return[r.conv3d(Bh("x",e,t,n),Bh("filter",e,t,n),[a[1],a[2],a[3]],s,o,[i[1],i[2],i[3]])]}case"AvgPool":{const a=Bh("strides",e,t,n),s=Bh("pad",e,t,n),o=Bh("kernelSize",e,t,n);return[r.avgPool(Bh("x",e,t,n),[o[1],o[2]],[a[1],a[2]],s)]}case"MaxPool":{const a=Bh("strides",e,t,n),s=Bh("pad",e,t,n),o=Bh("kernelSize",e,t,n);return[r.maxPool(Bh("x",e,t,n),[o[1],o[2]],[a[1],a[2]],s)]}case"MaxPoolWithArgmax":{const a=Bh("strides",e,t,n),s=Bh("pad",e,t,n),o=Bh("kernelSize",e,t,n),i=Bh("includeBatchInIndex",e,t,n),{result:u,indexes:l}=r.maxPoolWithArgmax(Bh("x",e,t,n),[o[1],o[2]],[a[1],a[2]],s,i);return[u,l]}case"AvgPool3D":{const a=Bh("strides",e,t,n),s=Bh("pad",e,t,n),o=Bh("kernelSize",e,t,n);return[r.avgPool3d(Bh("x",e,t,n),[o[1],o[2],o[3]],[a[1],a[2],a[3]],s)]}case"MaxPool3D":{const a=Bh("strides",e,t,n),s=Bh("pad",e,t,n),o=Bh("kernelSize",e,t,n);return[r.maxPool3d(Bh("x",e,t,n),[o[1],o[2],o[3]],[a[1],a[2],a[3]],s)]}case"Dilation2D":{const a=Bh("strides",e,t,n),s=Bh("pad",e,t,n),o=Bh("dilations",e,t,n),i=a[1],u=a[2],l=o[1],c=o[2];return[r.dilation2d(Bh("x",e,t,n),Bh("filter",e,t,n),[i,u],s,[l,c],"NHWC")]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"creation":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"Fill":{const a=Bh("shape",e,t,n),s=Bh("dtype",e,t,n),o=Bh("value",e,t,n);return[r.fill(a,o,s)]}case"LinSpace":{const a=Bh("start",e,t,n),s=Bh("stop",e,t,n),o=Bh("num",e,t,n);return[r.linspace(a,s,o)]}case"Multinomial":{const a=Bh("logits",e,t,n),s=Bh("numSamples",e,t,n),o=Bh("seed",e,t,n);return[r.multinomial(a,s,o)]}case"OneHot":{const a=Bh("indices",e,t,n),s=Bh("depth",e,t,n),o=Bh("onValue",e,t,n),i=Bh("offValue",e,t,n),u=Bh("dtype",e,t,n);return[r.oneHot(a,s,o,i,u)]}case"Ones":return[r.ones(Bh("shape",e,t,n),Bh("dtype",e,t,n))];case"OnesLike":return[r.onesLike(Bh("x",e,t,n))];case"RandomStandardNormal":return[r.randomStandardNormal(Bh("shape",e,t,n),Bh("dtype",e,t,n),Bh("seed",e,t,n))];case"RandomUniform":return[r.randomUniform(Bh("shape",e,t,n),Bh("minval",e,t,n),Bh("maxval",e,t,n),Bh("dtype",e,t,n))];case"RandomUniformInt":return[r.randomUniformInt(Bh("shape",e,t,n),Bh("minval",e,t,n),Bh("maxval",e,t,n),Bh("seed",e,t,n))];case"Range":{const a=Bh("start",e,t,n),s=Bh("stop",e,t,n),o=Bh("step",e,t,n);return[r.range(a,s,o,Bh("dtype",e,t,n))]}case"TruncatedNormal":{const a=Bh("shape",e,t,n),s=Bh("mean",e,t,n),o=Bh("stdDev",e,t,n),i=Bh("seed",e,t,n);return[r.truncatedNormal(a,s,o,Bh("dtype",e,t,n),i)]}case"Zeros":return[r.zeros(Bh("shape",e,t,n),Bh("dtype",e,t,n))];case"ZerosLike":return[r.zerosLike(Bh("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"dynamic":return((t,n,r,a,...s)=>e(null,[t,n,r,a,...s],function*(e,t,n,r,a=Ef){switch(e.op){case"NonMaxSuppressionV5":{const{boxes:r,scores:s,maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=Lf(e,t,n),c=yield a.image.nonMaxSuppressionWithScoreAsync(r,s,o,i,u,l);return[c.selectedIndices,c.selectedScores]}case"NonMaxSuppressionV4":{const{boxes:r,scores:s,maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=Lf(e,t,n),l=Bh("padToMaxOutputSize",e,t,n),c=yield 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a=Bh("x",e,t,n),s=r.unique(a);return[s.values,s.indices]}case"UniqueV2":{const a=Bh("x",e,t,n),s=Bh("axis",e,t,n),o=r.unique(a,s);return[o.values,o.indices]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"image":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"ResizeBilinear":{const a=Bh("images",e,t,n),s=Bh("size",e,t,n),o=Bh("alignCorners",e,t,n),i=Bh("halfPixelCenters",e,t,n);return[r.image.resizeBilinear(a,[s[0],s[1]],o,i)]}case"ResizeNearestNeighbor":{const a=Bh("images",e,t,n),s=Bh("size",e,t,n),o=Bh("alignCorners",e,t,n),i=Bh("halfPixelCenters",e,t,n);return[r.image.resizeNearestNeighbor(a,[s[0],s[1]],o,i)]}case"CropAndResize":{const a=Bh("image",e,t,n),s=Bh("boxes",e,t,n),o=Bh("boxInd",e,t,n),i=Bh("cropSize",e,t,n),u=Bh("method",e,t,n),l=Bh("extrapolationValue",e,t,n);return[r.image.cropAndResize(a,s,o,i,u,l)]}case"ImageProjectiveTransformV3":{const 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s=Bh("x",e,t,n),o=Bh("data",e,t,n),i=Bh("message",e,t,n),u=Bh("summarize",e,t,n);console.warn("The graph has a tf.print() operation,usually used for debugging, which slows down performance."),console.log(i);for(let e=0;e<o.length;e++)console.log(Array.prototype.slice.call(o[e].dataSync()).slice(0,u));return[s];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"logical":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"Equal":return[r.equal(Bh("a",e,t,n),Bh("b",e,t,n))];case"NotEqual":return[r.notEqual(Bh("a",e,t,n),Bh("b",e,t,n))];case"Greater":return[r.greater(Bh("a",e,t,n),Bh("b",e,t,n))];case"GreaterEqual":return[r.greaterEqual(Bh("a",e,t,n),Bh("b",e,t,n))];case"Less":return[r.less(Bh("a",e,t,n),Bh("b",e,t,n))];case"LessEqual":return[r.lessEqual(Bh("a",e,t,n),Bh("b",e,t,n))];case"LogicalAnd":return[r.logicalAnd(Bh("a",e,t,n),Bh("b",e,t,n))];case"LogicalNot":return[r.logicalNot(Bh("a",e,t,n))];case"LogicalOr":return[r.logicalOr(Bh("a",e,t,n),Bh("b",e,t,n))];case"Select":case"SelectV2":return[r.where(Bh("condition",e,t,n),Bh("a",e,t,n),Bh("b",e,t,n))];case"BitwiseAnd":return[r.bitwiseAnd(Bh("a",e,t,n),Bh("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"matrices":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[r.matMul(Bh("a",e,t,n),Bh("b",e,t,n),Bh("transposeA",e,t,n),Bh("transposeB",e,t,n))];case"Einsum":return[r.einsum(Bh("equation",e,t,n),...Bh("tensors",e,t,n))];case"Transpose":return[r.transpose(Bh("x",e,t,n),Bh("perm",e,t,n))];case"_FusedMatMul":const[a,s]=Bh("fusedOps",e,t,n),o="biasadd"===a,i="prelu"===s,u=Bh("numArgs",e,t,n),l=Bh("leakyreluAlpha",e,t,n);if(o){if(i&&2!==u)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!i&&1!==u)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}const[c,d]=Bh("args",e,t,n);return[r.fused.matMul({a:Bh("a",e,t,n),b:Bh("b",e,t,n),transposeA:Bh("transposeA",e,t,n),transposeB:Bh("transposeB",e,t,n),bias:c,activation:s,preluActivationWeights:d,leakyreluAlpha:l})];case"MatrixBandPart":return[r.linalg.bandPart(Bh("a",e,t,n),Bh("numLower",e,t,n),Bh("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"normalization":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(Bh("x",e,t,n),Bh("axis",e,t,n),Bh("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[r.batchNorm(Bh("x",e,t,n),Bh("mean",e,t,n),Bh("variance",e,t,n),Bh("offset",e,t,n),Bh("scale",e,t,n),Bh("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(Bh("x",e,t,n),Bh("radius",e,t,n),Bh("bias",e,t,n),Bh("alpha",e,t,n),Bh("beta",e,t,n))];case"Softmax":return[r.softmax(Bh("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(Bh("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"ragged":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"RaggedGather":{const{outputNestedSplits:a,outputDenseValues:s}=r.raggedGather(Bh("paramsNestedSplits",e,t,n),Bh("paramsDenseValues",e,t,n),Bh("indices",e,t,n),Bh("outputRaggedRank",e,t,n));return 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s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"FFT":return[r.fft(Bh("x",e,t,n))];case"IFFT":return[r.ifft(Bh("x",e,t,n))];case"RFFT":return[r.rfft(Bh("x",e,t,n))];case"IRFFT":return[r.irfft(Bh("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"string":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"StaticRegexReplace":return[r.string.staticRegexReplace(Bh("input",e,t,n),Bh("pattern",e,t,n),Bh("rewrite",e,t,n),Bh("replaceGlobal",e,t,n))];case"StringNGrams":{const{nGrams:a,nGramsSplits:s}=r.string.stringNGrams(Bh("data",e,t,n),Bh("dataSplits",e,t,n),Bh("separator",e,t,n),Bh("nGramWidths",e,t,n),Bh("leftPad",e,t,n),Bh("rightPad",e,t,n),Bh("padWidth",e,t,n),Bh("preserveShortSequences",e,t,n));return[a,s]}case"StringSplit":{const{indices:a,values:s,shape:o}=r.string.stringSplit(Bh("input",e,t,n),Bh("delimiter",e,t,n),Bh("skipEmpty",e,t,n));return[a,s,o]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(Bh("input",e,t,n),Bh("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(t,n,r));case"transformation":return s(()=>((e,t,n,r=Ef)=>{switch(e.op){case"Cast":return[r.cast(Bh("x",e,t,n),Bh("dtype",e,t,n))];case"ExpandDims":{const 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type ${e.op} is not implemented`)}})(t,n,r));case"hash_table":return((t,n,r,a)=>e(null,null,function*(){switch(t.op){case"HashTable":case"HashTableV2":{const e=a.getHashTableHandleByName(t.name);if(null!=e)return[e];{const e=Bh("keyDType",t,n,r),s=Bh("valueDType",t,n,r),o=new Bf(e,s);return a.addHashTable(t.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{const e=Bh("tableHandle",t,n,r,a),s=Bh("keys",t,n,r),o=Bh("values",t,n,r),i=a.getHashTableById(e.id);return[yield i.import(s,o)]}case"LookupTableFind":case"LookupTableFindV2":{const e=Bh("tableHandle",t,n,r,a),s=Bh("keys",t,n,r),o=Bh("defaultValue",t,n,r),i=a.getHashTableById(e.id);return[yield i.find(s,o)]}case"LookupTableSize":case"LookupTableSizeV2":{const e=Bh("tableHandle",t,n,r,a);return[a.getHashTableById(e.id).tensorSize()]}default:throw TypeError(`Node type ${t.op} is not implemented`)}}))(t,n,r,a);case"custom":const o=Lh(t.op);if(o&&o.customExecutor)return o.customExecutor(new $f(t,n,r));throw TypeError(`Custom op ${t.op} is not registered.`);default:throw TypeError(`Unknown op '${t.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(t,n,r);return P(o)?o.then(e=>[].concat(e)):[].concat(o)}class Wf{constructor(e={},t={},n={},r={},a){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=r,this.parseNodeNameCache=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){const e=[];for(let t=0;t<this.contexts.length-1;t++){const n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(e=>0===e.id&&0===e.iterationId?"":`${e.frameName}-${e.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(!(this.contexts&&this.contexts.length>1))throw new Error("Cannot exit frame, the context is empty");this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift()}nextIteration(){if(!(this.contexts&&this.contexts.length>0))throw new Error("Cannot increase frame iteration, the context is empty");{this.contexts=this.contexts.slice(),this.lastId++;const e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(const t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(const t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}}function zf(e,t,n,r){const a=new Set,s=[];let o=null,i=null;const u=new Set,l=new Set(Object.keys(e).map(e=>Gh(e)[0]));r=r||[];const c=new Set(r.map(e=>Gh(e.name)[0])),d=[...t];for(;d.length>0;){const e=d.pop();(Kf(e)||Xf(e)||Yf(e))&&null==o&&(o=e,i=o.children.map(e=>e.name).filter(e=>a.has(e))),a.add(e.name),null==n[e.name]&&(l.has(e.name)||c.has(e.name)||(0!==e.inputs.length?e.inputs.forEach(e=>{u.has(e.name)||(u.add(e.name),d.push(e))}):s.push(e.name)))}return{inputs:e,outputs:t,usedNodes:a,missingInputs:s,dynamicNode:o,syncInputs:i}}function Uf(e,t){const{usedNodes:n,inputs:r}=t,a=Object.keys(r).map(e=>Gh(e)[0]).map(t=>e.nodes[t]),s=e.initNodes||[],o=e=>n.has("string"===typeof e?e:e.name);function i(e){return[...new Map(e.map(e=>[e.name,e])).values()]}const u=i([...a,...e.weights,...s]).filter(o),l=i([...u,...Object.values(e.nodes)]).filter(o),c=new Map(l.map(e=>[e.name,e])),d={};for(const m of l){d[m.name]=d[m.name]||0;for(const e of m.children)o(e)||(d[e.name]=Number.POSITIVE_INFINITY),d[e.name]=(d[e.name]||0)+1}const p=Object.entries(d).filter(([,e])=>0===e).map(([e])=>e),h=[...p];for(;p.length>0;){const e=p.pop(),t=c.get(e);for(const n of t.children.filter(o))0===--d[n.name]&&(h.push(n.name),p.push(n.name))}const f=function(e,t){const n=new Map(e.map(e=>[e.name,e])),r=t.map(e=>e.name),a=new Set(r);for(;r.length>0;){const e=r.pop(),t=n.get(e);for(const s of t.children)n.has(s.name)&&!a.has(s.name)&&(a.add(s.name),r.push(s.name))}const s=e.filter(e=>a.has(e.name));return s}(h.map(e=>c.get(e)),u);return function(e,t){const n=new Map(e.map((e,t)=>[e.name,t])),r=new Set(t.map(e=>e.name)),a=e=>r.has("string"===typeof e?e:e.name),s=new Set(e.map(e=>e.name)),o=e=>s.has("string"===typeof e?e:e.name);for(const i of e){for(const e of i.children.filter(o)){if(!n.has(e.name))throw new Gf(`Child ${e.name} of node ${i.name} is unreachable.`);if(n.get(i.name)>n.get(e.name))throw new Gf(`Node ${i.name} is scheduled to run after its child ${e.name}.`)}if(!a(i))for(const e of i.inputs){if(!n.has(e.name))throw new Gf(`Input ${e.name} of node ${i.name} is unreachable.`);if(n.get(e.name)>n.get(i.name))throw new Gf(`Node ${i.name} is scheduled to run before its input ${e.name}.`)}}}(f,u),f}class Gf extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}}const Hf=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),jf=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),qf=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function Kf(e){return Hf.has(e.op)}function Xf(e){return jf.has(e.op)}function Yf(e){return qf.has(e.op)}class Qf{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(t=>e[t].map(e=>e.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach(t=>{this._functionExecutorMap[t]=new Qf(e.functions[t],this)})}getCompilationKey(e,t){const n=e.map(e=>e.name).sort(),r=t.map(e=>e.name).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(e,t){const n=zf(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(null!=a)throw new Error(`This execution contains the node '${a.name}', which has the dynamic op '${a.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(r.length>0){const n=t.map(e=>e.name),a=Object.keys(e);throw new Error(`Cannot compute the outputs [${n}] from the provided inputs [${a}]. Missing the following inputs: [${r}]`)}const o=Uf(this.graph,n),i=function(e){const t=new Map(e.map((e,t)=>[e.name,t])),n=Number.MAX_SAFE_INTEGER,r=e.map((e,t)=>Kf(e)?n:t),a=e=>{const n=r[t.get(e.name)];return null==n?-1:n},s=e.map((e,t)=>e.children.map(a).reduce((e,t)=>Math.max(e,t),r[t])),o=new Map;for(let i=0;i<e.length;++i){const t=s[i];if(t===n)continue;const r=e[i],a=e[t];o.has(a.name)||o.set(a.name,[]),o.get(a.name).push(r)}return o}(o);return{orderedNodes:o,nodeLiveUntilMap:i}}cloneAndKeepTensor(e){if(null==e)return null;const t=e.clone();return za(t),t}cloneTensorList(e){if(!e)return null;return e.map(e=>this.cloneAndKeepTensor(e))}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([e,t])=>[e,this.cloneTensorList(t)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const r=n.map(e=>this.graph.nodes[Gh(e)[0]]),a=t.map(e=>Gh(e)[0]),s=new Set(a);let o=a.map(e=>this.graph.nodes[e]);0===o.length&&(o=this._outputs);const i=this.getCompilationKey(r,o);let u=this.compiledMap.get(i);null==u&&(u=this.compile(e,o),this.compiledMap.set(i,u));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}const l={},c={};return Va(()=>{const n=new Wf(this.weightMap,l,c,this.functionExecutorMap,this.parseNodeNameCache),r=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(t=>{const[a,s]=Gh(t,n),o=[];o[s]=e[t],r[a]=o,this.keepIntermediateTensors&&(this.clonedTensorsMap[a]=this.cloneTensorList(o))});const a=this.getFrozenTensorIds(r),{orderedNodes:o,nodeLiveUntilMap:i}=u;for(const e of o){if(r[e.name])continue;const t=Vf(e,r,n,this._resourceManager);if(P(t))throw new Error(`The execution of the op '${e.op}' returned a promise. Please use model.executeAsync() instead.`);r[e.name]=t,this.keepIntermediateTensors&&(this.clonedTensorsMap[e.name]=this.cloneTensorList(t)),this.checkTensorForDisposalWithNodeLiveUntilInfo(e,r,n,a,s,i.get(e.name))}return null==this.parent&&n.dispose(a),t.map(e=>Vh(e,r,n))})}getFrozenTensorIds(e){const t=[].concat.apply([],Object.keys(e).map(t=>e[t]).map(e=>e.map(e=>e.id)));return new Set(t)}checkTensorForDisposal(e,t,n,r,a,s,o){if(!Kf(t)&&!s.has(e)){for(const r of n[e])null!=r&&(o[r.id]=(o[r.id]||0)+t.children.length);for(const e of t.inputs){if(Kf(e))continue;const t=Wh(e.name,n,r);if(null!=t)for(const e of t){if(!e||e.kept||a.has(e.id))continue;const t=o[e.id];1===t?(e.dispose(),delete o[e.id]):null!=t&&o[e.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,n,r,a,s){function o(e){return Kf(e)||a.has(e.name)}if(!Kf(e)&&null!=s)for(const i of s){if(o(i))continue;const e=Wh(i.name,t,n);for(const t of e)!t||t.kept||r.has(t.id)||t.dispose()}}executeAsync(t,n){return e(this,null,function*(){return this._executeAsync(t,n)})}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(const t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}_executeAsync(t,n){return e(this,arguments,function*(e,t,n=!1,r={},a={}){this.disposeIntermediateTensors(),n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=W().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}const s=new Wf(this.weightMap,r,a,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));const o=yield this.executeWithControlFlow(e,s,t,n),i=t.map(e=>Vh(e,o,s)),u=i.map(e=>e.id),l=Object.keys(e).map(t=>e[t].id),c=new Set([...u,...l,...this.weightIds]);return Object.values(o).forEach(e=>{e.forEach(e=>{!e||e.isDisposed||c.has(e.id)||e.dispose()})}),null==this.parent&&s.dispose(c),i})}executeFunctionAsync(t,n,r){return e(this,null,function*(){const e=t.reduce((e,t,n)=>(e[this.inputs[n].name]=t,e),{});return this._executeAsync(e,this.outputNodes,!0,n,r)})}executeWithControlFlow(t,n,r,a){return e(this,null,function*(){const e=Object.keys(t),s=e.map(e=>this.graph.nodes[Gh(e)[0]]),o=r.map(e=>Gh(e)[0]),i=new Set(o);let u=o.map(e=>this.graph.nodes[e]);0===u.length&&(u=this._outputs);const{usedNodes:l,missingInputs:c,dynamicNode:d,syncInputs:p}=zf(t,u,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(e=>({node:e,contexts:n.currentContext})),f=Object.assign({},this.weightMap);Object.keys(t).forEach(e=>{const[n,r]=Gh(e),a=[];a[r]=t[e],f[n]=a});const m={},g=this.getFrozenTensorIds(f),y={};for(;h.length>0;){const e=this.processStack(s,h,n,f,y,g,i,m,l);yield Promise.all(e)}null!=d||a||console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");const b=u.filter(e=>!Kf(e)&&!Vh(e.name,f,n)).map(e=>e.name);if(b.length>0){let t="";throw null!=d&&(t=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${e}]. Consider providing the following inputs: [${c}]. ${t}`)}return f})}processStack(e,t,n,r,a,s,o,i,u){const l=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&Bh("isConstant",e.node,r,n)&&([c]=zh(e.node.name,n)),null==r[e.node.name]){const d=Vf(e.node,r,n,this._resourceManager);c||([c]=zh(e.node.name,n));const p=n.currentContext;P(d)?l.push(d.then(l=>(r[c]=l,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(l)),n.currentContext=p,this.checkTensorForDisposal(c,e.node,r,n,s,o,i),this.processChildNodes(e.node,t,n,r,a,u),l))):(r[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,e.node,r,n,s,o,i),this.processChildNodes(e.node,t,n,r,a,u))}else this.processChildNodes(e.node,t,n,r,a,u)}return l}processChildNodes(e,t,n,r,a,s){e.children.forEach(e=>{const[o]=zh(e.name,n);!a[o]&&s.has(e.name)&&("Merge"===e.op?e.inputNames.some(e=>!!Vh(e,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every(e=>!!Vh(e,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:e})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(e=>e.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[r]=Gh(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value;u(e.length===n.shape.length&&n.shape.every((t,n)=>-1===e[n]||e[n]===t),()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&u(n.dtype===a.attrParams.dtype.value,()=>`The dtype of dict['${a.name}'] provided in model.execute(dict) must be ${a.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){var t,n;const r={};for(const a in e){const s=null===(n=null===(t=this._signature)||void 0===t?void 0:t.inputs)||void 0===n?void 0:n[a];null!=s?r[s.name]=e[a]:r[a]=e[a]}return r}checkInputs(e){const t=Object.keys(e).filter(e=>{const[t]=Gh(e);return null==this.graph.nodes[t]});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(e=>{var t,n;const r=null===(n=null===(t=this._signature)||void 0===t?void 0:t.outputs)||void 0===n?void 0:n[e];return null!=r?r.name:e},{})}checkOutputs(e){e.forEach(e=>{const[t]=Gh(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)})}}class Zf{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(const e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(const e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}}const Jf="?tfjs-format=file",em="model.json";class tm{get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}get modelStructuredOutputKeys(){return this.structuredOutputKeys}constructor(e,t={},n=gp){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new Zf}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else if(null!=this.loadOptions.requestInit)this.handler=this.io.browserHTTPRequest(e,this.loadOptions);else{const t=this.io.getLoadHandlers(e,this.loadOptions);if(0===t.length)t.push(this.io.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}load(){if(this.findIOHandler(),null==this.handler.load)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");const e=this.handler.load();return P(e)?e.then(e=>null==e.getWeightStream?this.loadSync(e):this.loadStreaming(e)):this.loadSync(e)}loadSync(e){const t=this.io.decodeWeights(e.weightData,e.weightSpecs);return this.loadWithWeightMap(e,t)}loadStreaming(t){return e(this,null,function*(){if(null==t.getWeightStream)throw new Error("Model artifacts missing streamWeights function");const e=yield Za(t.getWeightStream(),t.weightSpecs);return this.loadWithWeightMap(t,e)})}loadWithWeightMap(e,t){this.artifacts=e;const n=this.artifacts.modelTopology;let r=this.artifacts.signature;if(null!=this.artifacts.userDefinedMetadata){const e=this.artifacts.userDefinedMetadata;null!=e.signature&&(r=e.signature),null!=e.structuredOutputKeys&&(this.structuredOutputKeys=e.structuredOutputKeys)}if(this.signature=r,this.version=`${n.versions.producer}.${n.versions.minConsumer}`,this.executor=new Qf(hf.Instance.transformGraph(n,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,null!=e.modelInitializer&&null!=e.modelInitializer.node){const t=hf.Instance.transformGraph(e.modelInitializer);this.initializer=new Qf(t),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}save(t,n){return e(this,null,function*(){if("string"===typeof t){const e=this.io.getSaveHandlers(t);if(0===e.length)throw new Error(`Cannot find any save handlers for URL '${t}'`);if(e.length>1)throw new Error(`Found more than one (${e.length}) save handlers for URL '${t}'`);t=e[0]}if(null==t.save)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return t.save(this.artifacts)})}addStructuredOutputNames(e){if(this.structuredOutputKeys){const t={};return(e instanceof Qr?[e]:e).forEach((e,n)=>t[this.structuredOutputKeys[n]]=e),t}return e}predict(e,t){const n=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(n)}predictAsync(t,n){return e(this,null,function*(){const e=yield this.executeAsync(t,this.outputNodes);return this.addStructuredOutputNames(e)})}normalizeInputs(e){var t;if(!(e instanceof Qr)&&!Array.isArray(e)){const n=null===(t=this.signature)||void 0===t?void 0:t.inputs;if(null!=n)for(const t in n){const r=n[t];null!=r.resourceId&&(e[t]=this.resourceIdToCapturedInput[r.resourceId])}return e}e=Array.isArray(e)?e:[e];const n=Object.keys(this.resourceIdToCapturedInput).length;if(e.length+n!==this.inputNodes.length)throw new Error(`Input tensor count mismatch, the graph model has ${this.inputNodes.length-n} non-resource placeholders, while there are ${e.length} input tensors provided.`);let r=0;return this.inputNodes.reduce((t,n)=>{var a,s,o;const i=null===(o=null===(s=null===(a=this.signature)||void 0===a?void 0:a.inputs)||void 0===s?void 0:s[n])||void 0===o?void 0:o.resourceId;return t[n]=null!=i?this.resourceIdToCapturedInput[i]:e[r++],t},{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}executeInitializerGraph(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.execute({},[]):this.initializer.execute({},Object.keys(this.initializerSignature.outputs))}executeInitializerGraphAsync(){return e(this,null,function*(){return null==this.initializer?[]:null==this.initializerSignature?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))})}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){const t=this.initializerSignature.outputs,n=Object.keys(t);for(let r=0;r<n.length;r++){const a=t[n[r]];this.resourceIdToCapturedInput[a.resourceId]=e[r]}}}execute(e,t){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(this.executeInitializerGraph()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);const n=this.executor.execute(e,t);return n.length>1?n:n[0]}executeAsync(t,n){return e(this,null,function*(){null==this.resourceIdToCapturedInput&&this.setResourceIdToCapturedInput(yield this.executeInitializerGraphAsync()),t=this.normalizeInputs(t),n=this.normalizeOutputs(n);const e=yield this.executor.executeAsync(t,n);return e.length>1?e:e[0]})}getIntermediateTensors(){return 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HTMLCanvasElement||"undefined"!=typeof OffscreenCanvas&&e instanceof OffscreenCanvas||"undefined"!=typeof HTMLImageElement&&e instanceof HTMLImageElement)return function(e){if("offsetHeight"in e&&0!==e.offsetHeight&&"offsetWidth"in e&&0!==e.offsetWidth)return[e.offsetHeight,e.offsetWidth];if(null!=e.height&&null!=e.width)return[e.height,e.width];throw new Error("HTMLImageElement must have height and width attributes set.")}(e);if("undefined"!=typeof ImageData&&e instanceof ImageData)return[e.height,e.width];if("undefined"!=typeof HTMLVideoElement&&e instanceof HTMLVideoElement)return(t=e).hasAttribute("height")&&t.hasAttribute("width")?[t.height,t.width]:[t.videoHeight,t.videoWidth];var t;if(e instanceof Qr)return[e.shape[0],e.shape[1]];throw new Error("error: Unknown input type: "+e+".")}function ng(e,t){return(e-1)%t==0?e:Math.floor(e/t)*t+1}var rg={low:"low",medium:"medium",high:"high",full:"full"},ag=((Ym={})[rg.low]=.25,Ym[rg.medium]=.5,Ym[rg.high]=.75,Ym[rg.full]=1,Ym);function sg(e,t,n){var r=n[0],a=n[1],s=function(e){if("string"==typeof e){var t=ag[e];return u("number"==typeof t,function(){return"string value of inputResolution must be one of "+Object.values(rg).join(",")+" but was "+e+"."}),t}return u("number"==typeof e&&e<=2&&e>=.1,function(){return"inputResolution must be a string or number between 0.1 and 2, but was "+e}),e}(e);return[ng(r*s,t),ng(a*s,t)]}function og(e,t,n,r,a){var s=t[0],o=t[1],i=n[0],u=n[1],l=r[0],c=l[0],d=l[1],p=r[1],h=p[0],f=p[1];return Va(function(){var t=Pd.resizeBilinear(e,[i,u],!0);return function(e,t,n){var r=t[0],a=t[1],s=n[0],o=s[0],i=s[1],u=n[1],l=u[0],c=u[1];return Va(function(){var t=Bi(e);return ec(Pd.cropAndResize(t,[[o/(r+o+i-1),l/(a+l+c-1),(o+r-1)/(r+o+i-1),(l+a-1)/(a+l+c-1)]],[0],[r,a]),[0])})}(t=To(t),[s,o],[[c,d],[h,f]])})}function ig(e,t){var n=t[0],r=t[1],a=tg(e),s=a[0],o=a[1],i=r/n,u=[0,0,0,0],l=u[0],c=u[1],d=u[2],p=u[3];return o/s<i?(l=0,c=0,d=Math.round(.5*(i*s-o)),p=Math.round(.5*(i*s-o))):(l=Math.round(.5*(1/i*o-s)),c=Math.round(.5*(1/i*o-s)),d=0,p=0),{resized:Va(function(){var t,a=(t=e)instanceof Qr?t:vp(t);return a=Vu(a,[[l,c],[d,p],[0,0]]),Pd.resizeBilinear(a,[n,r])}),padding:{top:l,left:d,right:p,bottom:c}}}function ug(e){return cm(this,0,void 0,function(){return dm(this,function(t){return[2,Promise.all(e.map(function(e){return e.buffer()}))]})})}function lg(e,t,n,r,a){var s,o,i,u,l,c=t[0],d=t[1],p=n[0],h=n[1],f=(s=e,o=(c+r.top+r.bottom)/p,i=(d+r.left+r.right)/h,u=-r.top,l=-r.left,void 0===u&&(u=0),void 0===l&&(l=0),1===i&&1===o&&0===u&&0===l?s:s.map(function(e){return n=o,r=i,void 0===(a=u)&&(a=0),void 0===(s=l)&&(s=0),{score:(t=e).score,keypoints:t.keypoints.map(function(e){var t=e.score,o=e.part,i=e.position;return{score:t,part:o,position:{x:i.x*r+s,y:i.y*n+a}}})};var t,n,r,a,s}));return a?function(e,t){return t<=0?e:e.map(function(e){return r=t,{score:(n=e).score,keypoints:n.keypoints.map(function(e){var t=e.score,n=e.part,a=e.position;return{score:t,part:n,position:{x:r-1-a.x,y:a.y}}})};var n,r})}(f,d):f}var cg={architecture:"MobileNetV1",outputStride:16,quantBytes:4,multiplier:.75},dg=["MobileNetV1","ResNet50"],pg={MobileNetV1:[8,16,32],ResNet50:[32,16]},hg={MobileNetV1:[.5,.75,1],ResNet50:[1]},fg=[1,2,4],mg={flipHorizontal:!1,internalResolution:"medium",segmentationThreshold:.7,maxDetections:10,scoreThreshold:.4,nmsRadius:20},gg={flipHorizontal:!1,internalResolution:"medium",segmentationThreshold:.7,maxDetections:10,scoreThreshold:.4,nmsRadius:20,minKeypointScore:.3,refineSteps:10};function yg(e){var t=e.segmentationThreshold,n=e.maxDetections,r=e.scoreThreshold,a=e.nmsRadius;if(t<0||t>1)throw new Error("segmentationThreshold "+t+". 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(a<=0)throw new Error("Invalid nmsRadius "+a+".")}function bg(e){var t=e.segmentationThreshold,n=e.maxDetections,r=e.scoreThreshold,a=e.nmsRadius,s=e.minKeypointScore,o=e.refineSteps;if(t<0||t>1)throw new Error("segmentationThreshold "+t+". 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(a<=0)throw new Error("Invalid nmsRadius "+a+".");if(s<0||s>1)throw new Error("Invalid minKeypointScore "+s+".Should be in range [0.0, 1.0]");if(o<=0||o>20)throw new Error("Invalid refineSteps "+o+".Should be in range [1, 20]")}var xg=function(){function e(e){this.baseModel=e}return e.prototype.predictForPersonSegmentation=function(e){var t=this.baseModel.predict(e);return{segmentLogits:t.segmentation,heatmapScores:t.heatmapScores,offsets:t.offsets,displacementFwd:t.displacementFwd,displacementBwd:t.displacementBwd}},e.prototype.predictForPersonSegmentationAndPart=function(e){var t=this.baseModel.predict(e);return{segmentLogits:t.segmentation,partHeatmapLogits:t.partHeatmaps,heatmapScores:t.heatmapScores,offsets:t.offsets,displacementFwd:t.displacementFwd,displacementBwd:t.displacementBwd}},e.prototype.predictForMultiPersonInstanceSegmentationAndPart=function(e){var t=this.baseModel.predict(e);return{segmentLogits:t.segmentation,longOffsets:t.longOffsets,heatmapScores:t.heatmapScores,offsets:t.offsets,displacementFwd:t.displacementFwd,displacementBwd:t.displacementBwd,partHeatmaps:t.partHeatmaps}},e.prototype.segmentPersonActivation=function(e,t,n){var r=this;void 0===n&&(n=.5);var a=tg(e),s=a[0],o=a[1],i=sg(t,this.baseModel.outputStride,[s,o]),u=ig(e,i),l=u.resized,c=u.padding,d=Va(function(){var e=r.predictForPersonSegmentation(l),t=e.segmentLogits,a=e.heatmapScores,i=e.offsets,u=e.displacementFwd,d=e.displacementBwd,p=l.shape,h=p[0],f=p[1],m=og(t,[s,o],[h,f],[[c.top,c.bottom],[c.left,c.right]]);return{segmentation:bm(ec(m),n),heatmapScores:a,offsets:i,displacementFwd:u,displacementBwd:d}}),p=d.segmentation,h=d.heatmapScores,f=d.offsets,m=d.displacementFwd,g=d.displacementBwd;return l.dispose(),{segmentation:p,heatmapScores:h,offsets:f,displacementFwd:m,displacementBwd:g,padding:c,internalResolutionHeightAndWidth:i}},e.prototype.segmentPerson=function(e,t){return void 0===t&&(t=mg),cm(this,0,void 0,function(){var n,r,a,s,o,i,u,l,c,d,p,h,f,m,g,y,b,x;return dm(this,function(v){switch(v.label){case 0:return yg(t=lm(lm({},mg),t)),n=this.segmentPersonActivation(e,t.internalResolution,t.segmentationThreshold),r=n.segmentation,a=n.heatmapScores,s=n.offsets,o=n.displacementFwd,i=n.displacementBwd,u=n.padding,l=n.internalResolutionHeightAndWidth,c=r.shape,d=c[0],p=c[1],[4,r.data()];case 1:return h=v.sent(),r.dispose(),[4,ug([a,s,o,i])];case 2:return f=v.sent(),m=f[0],g=f[1],y=f[2],b=f[3],x=lg(x=Xm(m,g,y,b,this.baseModel.outputStride,t.maxDetections,t.scoreThreshold,t.nmsRadius),[d,p],l,u,!1),a.dispose(),s.dispose(),o.dispose(),i.dispose(),[2,{height:d,width:p,data:h,allPoses:x}]}})})},e.prototype.segmentMultiPerson=function(e,t){return void 0===t&&(t=gg),cm(this,0,void 0,function(){var n,r,a,s,o,i,u,l,c,d,p,h,f,m,g,y,b,x,v,w,k,I=this;return dm(this,function(N){switch(N.label){case 0:return bg(t=lm(lm({},gg),t)),n=tg(e),r=n[0],a=n[1],s=sg(t.internalResolution,this.baseModel.outputStride,[r,a]),o=ig(e,s),i=o.resized,u=o.padding,l=Va(function(){var e,n=I.predictForMultiPersonInstanceSegmentationAndPart(i),o=n.segmentLogits,l=n.longOffsets,c=n.heatmapScores,d=n.offsets,p=n.displacementFwd,h=n.displacementBwd,f=og(o,[r,a],s,[[u.top,u.bottom],[u.left,u.right]]);return e=l,{segmentation:bm(ec(f),t.segmentationThreshold),longOffsets:e,heatmapScoresRaw:c,offsetsRaw:d,displacementFwdRaw:p,displacementBwdRaw:h}}),c=l.segmentation,d=l.longOffsets,p=l.heatmapScoresRaw,h=l.offsetsRaw,f=l.displacementFwdRaw,m=l.displacementBwdRaw,[4,ug([p,h,f,m])];case 1:return g=N.sent(),y=g[0],b=g[1],x=g[2],v=g[3],w=lg(w=Xm(y,b,x,v,this.baseModel.outputStride,t.maxDetections,t.scoreThreshold,t.nmsRadius),[r,a],s,u,!1),[4,Mm(c,d,w,r,a,this.baseModel.outputStride,s,u,t.scoreThreshold,t.refineSteps,t.minKeypointScore,t.maxDetections)];case 2:return k=N.sent(),i.dispose(),c.dispose(),d.dispose(),p.dispose(),h.dispose(),f.dispose(),m.dispose(),[2,k]}})})},e.prototype.segmentPersonPartsActivation=function(e,t,n){var r=this;void 0===n&&(n=.5);var a=tg(e),s=a[0],o=a[1],i=sg(t,this.baseModel.outputStride,[s,o]),u=ig(e,i),l=u.resized,c=u.padding,d=Va(function(){var e=r.predictForPersonSegmentationAndPart(l),t=e.segmentLogits,a=e.partHeatmapLogits,i=e.heatmapScores,u=e.offsets,d=e.displacementFwd,p=e.displacementBwd,h=l.shape,f=h[0],m=h[1],g=og(t,[s,o],[f,m],[[c.top,c.bottom],[c.left,c.right]]),y=og(a,[s,o],[f,m],[[c.top,c.bottom],[c.left,c.right]]);return{partSegmentation:xm(bm(ec(g),n),y),heatmapScores:i,offsets:u,displacementFwd:d,displacementBwd:p}}),p=d.partSegmentation,h=d.heatmapScores,f=d.offsets,m=d.displacementFwd,g=d.displacementBwd;return l.dispose(),{partSegmentation:p,heatmapScores:h,offsets:f,displacementFwd:m,displacementBwd:g,padding:c,internalResolutionHeightAndWidth:i}},e.prototype.segmentPersonParts=function(e,t){return void 0===t&&(t=mg),cm(this,0,void 0,function(){var n,r,a,s,o,i,u,l,c,d,p,h,f,m,g,y,b,x;return dm(this,function(v){switch(v.label){case 0:return yg(t=lm(lm({},mg),t)),n=this.segmentPersonPartsActivation(e,t.internalResolution,t.segmentationThreshold),r=n.partSegmentation,a=n.heatmapScores,s=n.offsets,o=n.displacementFwd,i=n.displacementBwd,u=n.padding,l=n.internalResolutionHeightAndWidth,c=r.shape,d=c[0],p=c[1],[4,r.data()];case 1:return h=v.sent(),r.dispose(),[4,ug([a,s,o,i])];case 2:return f=v.sent(),m=f[0],g=f[1],y=f[2],b=f[3],x=lg(x=Xm(m,g,y,b,this.baseModel.outputStride,t.maxDetections,t.scoreThreshold,t.nmsRadius),[d,p],l,u,!1),a.dispose(),s.dispose(),o.dispose(),i.dispose(),[2,{height:d,width:p,data:h,allPoses:x}]}})})},e.prototype.segmentMultiPersonParts=function(e,t){return void 0===t&&(t=gg),cm(this,0,void 0,function(){var n,r,a,s,o,i,u,l,c,d,p,h,f,m,g,y,b,x,v,w,k,I,N=this;return dm(this,function(S){switch(S.label){case 0:return bg(t=lm(lm({},gg),t)),n=tg(e),r=n[0],a=n[1],s=sg(t.internalResolution,this.baseModel.outputStride,[r,a]),o=ig(e,s),i=o.resized,u=o.padding,l=Va(function(){var e=N.predictForMultiPersonInstanceSegmentationAndPart(i),n=e.segmentLogits,o=e.longOffsets,l=e.heatmapScores,c=e.offsets,d=e.displacementFwd,p=e.displacementBwd,h=e.partHeatmaps,f=og(n,[r,a],s,[[u.top,u.bottom],[u.left,u.right]]),m=og(h,[r,a],s,[[u.top,u.bottom],[u.left,u.right]]),g=o;return{segmentation:bm(ec(f),t.segmentationThreshold),longOffsets:g,heatmapScoresRaw:l,offsetsRaw:c,displacementFwdRaw:d,displacementBwdRaw:p,partSegmentation:function(e){var t=e.shape,n=t[0],r=t[1],a=t[2];return Va(function(){var t=ym(e),s=Bi(Sl(0,a,1,"int32"),1),o=Bs(So(t,s),"int32");return wo(o,[n,r])})}(m)}}),c=l.segmentation,d=l.longOffsets,p=l.heatmapScoresRaw,h=l.offsetsRaw,f=l.displacementFwdRaw,m=l.displacementBwdRaw,g=l.partSegmentation,[4,ug([p,h,f,m])];case 1:return y=S.sent(),b=y[0],x=y[1],v=y[2],w=y[3],k=lg(k=Xm(b,x,v,w,this.baseModel.outputStride,t.maxDetections,t.scoreThreshold,t.nmsRadius),[r,a],s,u,!1),[4,Pm(c,d,g,k,r,a,this.baseModel.outputStride,s,u,t.scoreThreshold,t.refineSteps,t.minKeypointScore,t.maxDetections)];case 2:return I=S.sent(),i.dispose(),c.dispose(),d.dispose(),p.dispose(),h.dispose(),f.dispose(),m.dispose(),g.dispose(),[2,I]}})})},e.prototype.dispose=function(){this.baseModel.dispose()},e}();function vg(e){return cm(this,0,void 0,function(){var t,n,r,a,s,o;return dm(this,function(i){switch(i.label){case 0:if(t=e.outputStride,n=e.quantBytes,r=e.multiplier,null==Oh)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 a=function(e,t,n){var r={1:"100",.75:"075",.5:"050"},a="model-stride"+e+".json";return 4===n?eg+"float/"+r[t]+"/"+a:eg+"quant"+n+"/"+r[t]+"/"+a}(t,r,n),[4,nm(e.modelUrl||a)];case 1:return s=i.sent(),o=new wm(s,t),[2,new xg(o)]}})})}function wg(e){return cm(this,0,void 0,function(){var t,n,r,a,s;return dm(this,function(o){switch(o.label){case 0:if(t=e.outputStride,n=e.quantBytes,null==Oh)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 r=function(e,t){var n="model-stride"+e+".json";return 4===t?Jm+"float/"+n:Jm+"quant"+t+"/"+n}(t,n),[4,nm(e.modelUrl||r)];case 1:return a=o.sent(),s=new Zm(a,t),[2,new xg(s)]}})})}function kg(e){return void 0===e&&(e=cg),cm(this,0,void 0,function(){return dm(this,function(t){return"ResNet50"===(e=function(e){if(null==(e=e||cg).architecture&&(e.architecture="MobileNetV1"),dg.indexOf(e.architecture)<0)throw new Error("Invalid architecture "+e.architecture+". Should be one of "+dg);if(null==e.outputStride&&(e.outputStride=16),pg[e.architecture].indexOf(e.outputStride)<0)throw new Error("Invalid outputStride "+e.outputStride+". Should be one of "+pg[e.architecture]+" for architecture "+e.architecture+".");if(null==e.multiplier&&(e.multiplier=1),hg[e.architecture].indexOf(e.multiplier)<0)throw new Error("Invalid multiplier "+e.multiplier+". Should be one of "+hg[e.architecture]+" for architecture "+e.architecture+".");if(null==e.quantBytes&&(e.quantBytes=4),fg.indexOf(e.quantBytes)<0)throw new Error("Invalid quantBytes "+e.quantBytes+". 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e.prototype.toCanvasImageSource=function(){return cm(this,0,void 0,function(){return dm(this,function(e){return[2,this.mask]})})},e.prototype.toImageData=function(){return cm(this,0,void 0,function(){return dm(this,function(e){return[2,fm(this.mask)]})})},e.prototype.toTensor=function(){return cm(this,0,void 0,function(){return dm(this,function(e){return[2,mm(this.mask)]})})},e.prototype.getUnderlyingType=function(){return"canvasimagesource"},e}();function _g(e){return gm(e),"person"}var Ag=function(){function e(e){var t,n,r=this;this.selfieMode=!1,this.selfieSegmentationSolution=new om.SelfieSegmentation({locateFile:null!==(t=e.locateFile)&&void 0!==t?t:function(t,n){return e.solutionPath?e.solutionPath.replace(/\/+$/,"")+"/"+t:n+"/"+t}}),n="landscape"===e.modelType?1:0,this.selfieSegmentationSolution.setOptions({modelSelection:n,selfieMode:this.selfieMode}),this.selfieSegmentationSolution.onResults(function(e){r.segmentation=[{maskValueToLabel:_g,mask:new 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Mg={runtime:"tfjs",modelType:"general",modelUrl:"https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/general/1"},Pg={flipHorizontal:!1},Lg={outputTensorSize:{width:256,height:256},outputTensorFloatRange:[0,1]},Bg={outputTensorSize:{width:256,height:144},outputTensorFloatRange:[0,1]},Vg={activation:"none"},Wg=function(){function e(e){this.mask=e}return e.prototype.toCanvasImageSource=function(){return cm(this,0,void 0,function(){return dm(this,function(e){return[2,hm(this.mask)]})})},e.prototype.toImageData=function(){return cm(this,0,void 0,function(){return dm(this,function(e){return[2,fm(this.mask)]})})},e.prototype.toTensor=function(){return cm(this,0,void 0,function(){return dm(this,function(e){return[2,this.mask]})})},e.prototype.getUnderlyingType=function(){return"tensor"},e}();function zg(e){return gm(e),"person"}var Ug,Gg,Hg=function(){function e(e,t){this.modelType=e,this.model=t}return e.prototype.segmentPeople=function(e,t){return cm(this,0,void 0,function(){var n,r=this;return dm(this,function(a){return t=function(e){if(null==e)return lm({},Pg);var t=lm({},e);return null==t.flipHorizontal&&(t.flipHorizontal=Pg.flipHorizontal),t}(t),null==e?(this.reset(),[2,[]]):(n=Va(function(){var t=Dg(e,"general"===r.modelType?Lg:Bg).imageTensor,n=Co(r.model.predict(t),[0,0,0,1],-1),a=Fg(e),s=function(e,t,n){return Va(function(){var r=ec(e,[0]),a=r.shape[2];if(1===a){var s=r;switch(t.activation){case"none":break;case"sigmoid":s=To(s);break;case"softmax":throw new Error("Softmax activation requires two channels.");default:throw new Error("Activation not supported ("+t.activation+")")}var o=n?Pd.resizeBilinear(s,[n.height,n.width]):s;return ec(o,[2])}throw new Error("Unsupported number of tensor channels "+a)})}(n,Vg,a),o=Bi(s,2),i=Pu(o,[[0,0],[0,0],[0,1]]);return $u(i,[[0,0],[0,0],[0,2]],"symmetric")}),[2,[{maskValueToLabel:zg,mask:new Wg(n)}]])})})},e.prototype.dispose=function(){this.model.dispose()},e.prototype.reset=function(){},e}();function jg(e){return cm(this,0,void 0,function(){var t,n,r;return dm(this,function(a){switch(a.label){case 0:return t=function(e){if(null==e)return lm({},Mg);var t=lm({},e);if(t.runtime="tfjs",null==t.modelType&&(t.modelType=Mg.modelType),"general"!==t.modelType&&"landscape"!==t.modelType)throw new Error("Model type must be one of general or landscape, but got "+t.modelType);return null==t.modelUrl&&("general"===t.modelType?t.modelUrl="https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/general/1":t.modelUrl="https://tfhub.dev/mediapipe/tfjs-model/selfie_segmentation/landscape/1"),t}(e),n="string"==typeof t.modelUrl&&t.modelUrl.indexOf("https://tfhub.dev")>-1,[4,nm(t.modelUrl,{fromTFHub:n})];case 1:return r=a.sent(),[2,new Hg(t.modelType,r)]}})})}(Gg=Ug||(Ug={})).BodyPix="BodyPix",Gg.MediaPipeSelfieSegmentation="MediaPipeSelfieSegmentation";var qg={};function Kg(e){if("undefined"!=typeof HTMLCanvasElement&&e instanceof HTMLCanvasElement||"undefined"!=typeof OffscreenCanvas&&e instanceof 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-= 1;\n      }\n      return res;\n    }\n\n    //Based on the work of Dave Hoskins\n    //https://www.shadertoy.com/view/4djSRW\n    #define HASHSCALE1 443.8975\n    float random(float seed){\n      vec2 p = resultUV * seed;\n      vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n      p3 += dot(p3, p3.yzx + 19.19);\n      return fract((p3.x + p3.y) * p3.z);\n    }\n\n    ${Jy}\n    ${eb}\n    ${tb}\n  `;return t}(i);t.isPacked?(l=function(e,t,n){switch(e.length){case 0:return rb();case 1:return function(e,t,n){const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(1===r[0])return n?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));\n      }\n    ":`\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${r[1]}.0);\n      }\n    `;if(1===r[1])return n?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));\n      }\n    ":`\n      int getOutputCoords() {\n        return 2 * 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ivec2(resultUV.yx * vec2(${r[0]}, ${r[1]}));\n      }\n    `;const a=Math.ceil(e[1]/2);if(n)return"\n    ivec2 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec2(r, c);\n    }\n  ";return`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${r[0]}, ${r[1]}));\n\n      int index = resTexRC.x * ${r[1]} + resTexRC.y;\n      int r = 2 * (index / ${a});\n      int c = imod(index, ${a}) * 2;\n\n      return ivec2(r, c);\n    }\n  `}(e,t,n);case 3:return function(e,t,n){if(n)return"\n    ivec3 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),s=a*Math.ceil(e[1]/2);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${r[0]}, ${r[1]}));\n      int index = resTexRC.x * ${r[1]} + resTexRC.y;\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${a});\n      int c = imod(index, ${a}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `}(e,t,n);default:return function(e,t,n){if(n)return"\n    ivec4 getOutputCoords() {\n      ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(packedTexShape[0], packedTexShape[1]));\n      int index = resTexRC.x * packedTexShape[1] + resTexRC.y;\n\n      int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));\n      int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));\n      int texelsInBatchN = texelsInBatch * outShape[1];\n\n      int b2 = index / texelsInBatchN;\n      index -= b2 * texelsInBatchN;\n\n      int b = index / texelsInBatch;\n      index -= b * texelsInBatch;\n\n      int r = 2 * (index / texelsInLogicalRow);\n      int c = imod(index, texelsInLogicalRow) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  ";const r=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),s=a*Math.ceil(e[e.length-2]/2);let o=s,i="",u="b, r, c";for(let l=2;l<e.length-1;l++)o*=e[e.length-l-1],i=`\n      int b${l} = index / ${o};\n      index -= b${l} * ${o};\n    `+i,u=`b${l}, `+u;return`\n    ivec${e.length} getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${r[0]}, ${r[1]}));\n      int index = resTexRC.x * ${r[1]} + resTexRC.y;\n\n      ${i}\n\n      int b = index / ${s};\n      index -= b * ${s};\n\n      int r = 2 * (index / ${a});\n      int c = imod(index, ${a}) * 2;\n\n      return ivec${e.length}(${u});\n    }\n  `}(e,t,n)}}(t.logicalShape,o,n.enableShapeUniforms),c=function(e){return`\n    void setOutput(vec4 val) {\n      ${e.output} = val;\n    }\n  `}(i)):(l=function(e,t,n){switch(e.length){case 0:return rb();case 1:return function(e,t,n){if(1===t[0])return n?"\n      int getOutputCoords() {\n        return int(resultUV.x * float(outTexShape[1]));\n      }\n    ":`\n      int getOutputCoords() {\n        return int(resultUV.x * ${t[1]}.0);\n      }\n    `;if(1===t[1])return n?"\n      int getOutputCoords() {\n        return int(resultUV.y * float(outTexShape[0]));\n      }\n    ":`\n      int getOutputCoords() {\n        return int(resultUV.y * ${t[0]}.0);\n      }\n    `;if(n)return"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      return resTexRC.x * outTexShape[1] + resTexRC.y;\n    }\n  ";return`\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      return resTexRC.x * ${t[1]} + resTexRC.y;\n    }\n  `}(0,t,n);case 2:return function(e,t,n){if(p(e,t))return n?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));\n      }\n    `;if(1===e[1])return n?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;if(1===e[0])return n?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(outTexShape[0], outTexShape[1]));\n        int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":`\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${t[0]}, ${t[1]}));\n        int index = resTexRC.x * ${t[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;if(n)return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      int r = index / outShape[1];\n      int c = index - r * outShape[1];\n      return ivec2(r, c);\n    }\n  ";return`\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      int r = index / ${e[1]};\n      int c = index - r * ${e[1]};\n      return ivec2(r, c);\n    }\n  `}(e,t,n);case 3:return function(e,t,n){if(n){return`\n  ivec3 getOutputCoords() {\n    ivec2 resTexRC = ivec2(resultUV.yx *\n                           vec2(outTexShape[0], outTexShape[1]));\n    int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n    ${Hy(["r","c","d"],e)}\n    return ivec3(r, c, d);\n  }\n`}const r=Gy(["r","c","d"],e);return`\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${r}\n      return ivec3(r, c, d);\n    }\n  `}(e,t,n);case 4:return function(e,t,n){if(n){return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(outTexShape[0], outTexShape[1]));\n      int index = resTexRC.x * outTexShape[1] + resTexRC.y;\n      ${Hy(["r","c","d","d2"],e)}\n      return ivec4(r, c, d, d2);\n    }\n  `}const r=Gy(["r","c","d","d2"],e);return`\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n      ${r}\n      return ivec4(r, c, d, d2);\n    }\n  `}(e,t,n);case 5:return function(e,t){const n=Gy(["r","c","d","d2","d3"],e);return`\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},\n                             ${t[1]}));\n\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `}(e,t);case 6:return function(e,t){const n=Gy(["r","c","d","d2","d3","d4"],e);return`\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${t[0]}, ${t[1]}));\n      int index = resTexRC.x * ${t[1]} + resTexRC.y;\n\n      ${n}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `}(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}(t.logicalShape,o,n.enableShapeUniforms),c=function(e){return`\n    void setOutput(float val) {\n      ${e.output} = vec4(val, 0, 0, 0);\n    }\n  `}(i)),n.packedInputs&&(h+=nb);return[h,u,c,a,l,s,n.userCode].join("\n")}function Qy(e,t=!1){const n=e.shapeInfo.logicalShape;switch(n.length){case 0:return function(e,t){const n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${r}() {return ${n};}`;const[a,s]=e.shapeInfo.texShape;if(1===a&&1===s)return`\n      float ${r}() {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;const o=ab(n);if(t)return`\n    float ${r}() {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `;const[i,u]=e.shapeInfo.texShape;return`\n    float ${r}() {\n      vec2 uv = uvFromFlat(${i}, ${u}, ${o});\n      return sampleTexture(${n}, uv);\n    }\n  `}(e,t);case 1:return function(e,t){const n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`\n      float ${r}(int index) {\n        ${sb(e)}\n      }\n    `;const a=e.shapeInfo.texShape,s=a[0],o=a[1];if(1===o&&1===s)return`\n      float ${r}(int index) {\n        return sampleTexture(${n}, halfCR);\n      }\n    `;const i=ab(n);if(1===o)return t?`\n      float ${r}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${r}(int index) {\n        vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${s}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(1===s)return t?`\n      float ${r}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `:`\n      float ${r}(int index) {\n        vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(t)return`\n    float ${r}(int index) {\n      vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `;return`\n    float ${r}(int index) {\n      vec2 uv = uvFromFlat(${s}, ${o}, index + ${i});\n      return sampleTexture(${n}, uv);\n    }\n  `}(e,t);case 2:return function(e,t){const n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape;if(null!=s&&p(n,s)){if(t)return`\n      float ${a}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `;const e=s[0];return`\n    float ${a}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${s[1]}.0, ${e}.0);\n      return sampleTexture(${r}, uv);\n    }\n  `}const{newShape:o,keptDims:i}=x(n),u=o;if(u.length<n.length){const n=["row","col"];return`\n      ${Qy(ub(e,u),t)}\n      float ${a}(int row, int col) {\n        return ${a}(${lb(n,i)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${a}(int row, int col) {\n        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));\n        ${sb(e)}\n      }\n    `;const l=s[0],c=s[1],d=ab(r);if(1===c)return t?`\n      float ${a}(int row, int col) {\n        float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));\n        vec2 uv = vec2(0.5, (index + 0.5) / float(${r}TexShape[0]));\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n    float ${a}(int row, int col) {\n      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);\n      return sampleTexture(${r}, uv);\n    }\n  `;if(1===l)return t?`\n      float ${a}(int row, int col) {\n        float index = dot(vec3(row, col, ${d}), vec3(${r}Shape[1], 1, 1));\n        vec2 uv = vec2((index + 0.5) / float(${r}TexShape[1]), 0.5);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n    float ${a}(int row, int col) {\n      float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));\n      vec2 uv = vec2((index + 0.5) / ${c}.0, 0.5);\n      return sampleTexture(${r}, uv);\n    }\n  `;if(t)return`\n      float ${a}(int row, int col) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${r}Shape[1] + col + ${d};\n        vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);\n        return sampleTexture(${r}, uv);\n      }\n    `;return`\n  float ${a}(int row, int col) {\n    // Explicitly use integer operations as dot() only works on floats.\n    int index = row * ${n[1]} + col + ${d};\n    vec2 uv = uvFromFlat(${l}, ${c}, index);\n    return sampleTexture(${r}, uv);\n  }\n`}(e,t);case 3:return function(e,t){const n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n[1]*n[2],o=n[2],{newShape:i,keptDims:u}=x(n),l=i;if(l.length<n.length){const n=["row","col","depth"];return`\n        ${Qy(ub(e,l),t)}\n        float ${a}(int row, int col, int depth) {\n          return ${a}(${lb(n,u)});\n        }\n      `}if(e.shapeInfo.isUniform)return`\n      float ${a}(int row, int col, int depth) {\n        int index = round(dot(vec3(row, col, depth),\n                          vec3(${s}, ${o}, 1)));\n        ${sb(e)}\n      }\n    `;const c=e.shapeInfo.texShape,d=c[0],p=c[1],h=e.shapeInfo.flatOffset;if(p===s&&null==h)return t?`\n      float ${a}(int row, int col, int depth) {\n        int stride1 = ${r}Shape[2];\n        float texR = float(row);\n        float texC = dot(vec2(col, depth), vec2(stride1, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n        float ${a}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${o}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${p}.0, ${d}.0);\n          return sampleTexture(${r}, uv);\n        }\n      `;if(p===o&&null==h)return t?`\n      float ${a}(int row, int col, int depth) {\n        float texR = dot(vec2(row, col), vec2(${r}Shape[1], 1));\n        float texC = float(depth);\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n    float ${a}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${n[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);\n      return sampleTexture(${r}, uv);\n    }\n  `;const f=ab(r);if(t)return`\n    float ${a}(int row, int col, int depth) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int stride0 = ${r}Shape[1] * ${r}Shape[2];\n      int stride1 = ${r}Shape[2];\n      int index = row * stride0 + col * stride1 + depth + ${f};\n      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index);\n      return sampleTexture(${r}, uv);\n    }\n    `;return`\n      float ${a}(int row, int col, int depth) {\n        // Explicitly use integer operations as dot() only works on floats.\n        int index = row * ${s} + col * ${o} + depth + ${f};\n        vec2 uv = uvFromFlat(${d}, ${p}, index);\n        return sampleTexture(${r}, uv);\n      }\n  `}(e,t);case 4:return function(e,t){const n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=n[3],o=n[2]*s,i=n[1]*o,{newShape:u,keptDims:l}=x(n);if(u.length<n.length){const n=["row","col","depth","depth2"];return`\n      ${Qy(ub(e,u),t)}\n      float ${a}(int row, int col, int depth, int depth2) {\n        return ${a}(${lb(n,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${a}(int row, int col, int depth, int depth2) {\n        int index = round(dot(vec4(row, col, depth, depth2),\n                          vec4(${i}, ${o}, ${s}, 1)));\n        ${sb(e)}\n      }\n    `;const c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${r}Shape[3];`,m=`int stride1 = ${r}Shape[2] * stride2;`,g=`int stride0 = ${r}Shape[1] * stride1;`;if(h===i&&null==c)return t?`\n      float ${a}(int row, int col, int depth, int depth2) {\n        ${f}\n        ${m}\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(stride1, stride2, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n      float ${a}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2),\n                vec3(${o}, ${s}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${p}.0);\n        return sampleTexture(${r}, uv);\n      }\n    `;if(h===s&&null==c)return t?`\n      float ${a}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${r}Shape[1] * ${r}Shape[2], ${r}Shape[2], 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${r}TexShape[1], ${r}TexShape[0]);\n        return sampleTexture(${r}, uv);\n      }\n    `:`\n      float ${a}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${n[1]*n[2]}, ${n[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${p}.0);\n        return sampleTexture(${r}, uv);\n      }\n    `;const y=ab(r);if(t)return`\n    float ${a}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      ${f}\n      ${m}\n      ${g}\n      int index = row * stride0 + col * stride1 +\n          depth * stride2 + depth2;\n      vec2 uv = uvFromFlat(${r}TexShape[0], ${r}TexShape[1], index + ${y});\n      return sampleTexture(${r}, uv);\n    }\n  `;return`\n    float ${a}(int row, int col, int depth, int depth2) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${o} +\n          depth * ${s} + depth2;\n      vec2 uv = uvFromFlat(${p}, ${h}, index + ${y});\n      return sampleTexture(${r}, uv);\n    }\n  `}(e,t);case 5:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=t[4],s=t[3]*a,o=t[2]*s,i=t[1]*o,{newShape:u,keptDims:l}=x(t);if(u.length<t.length){const t=["row","col","depth","depth2","depth3"];return`\n      ${Qy(ub(e,u))}\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        return ${r}(${lb(t,l)});\n      }\n    `}if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${i}, ${o}, ${s}, ${a})) +\n          depth3;\n        ${sb(e)}\n      }\n    `;const c=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&null==c)return`\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n                         vec4(${o}, ${s}, ${a}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${h}.0, ${p}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(h===a&&null==c)return`\n      float ${r}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]},\n               ${t[2]*t[3]}, ${t[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${h}.0, ${p}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;const f=ab(n);return`\n    float ${r}(int row, int col, int depth, int depth2, int depth3) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${i} + col * ${o} + depth * ${s} +\n          depth2 * ${a} + depth3 + ${f};\n      vec2 uv = uvFromFlat(${p}, ${h}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);case 6:return function(e){const t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:a,keptDims:s}=x(t);if(a.length<t.length){const t=["row","col","depth","depth2","depth3","depth4"];return`\n      ${Qy(ub(e,a))}\n      float ${r}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${r}(${lb(t,s)});\n      }\n    `}const o=t[5],i=t[4]*o,u=t[3]*i,l=t[2]*u,c=t[1]*l;if(e.shapeInfo.isUniform)return`\n      float ${r}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        int index = round(dot(\n          vec4(row, col, depth, depth2),\n          vec4(${c}, ${l}, ${u}, ${i})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${o}, 1)));\n        ${sb(e)}\n      }\n    `;const d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===c&&null==d)return`\n      float ${r}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(vec4(col, depth, depth2, depth3),\n          vec4(${l}, ${u}, ${i}, ${o})) +\n               float(depth4);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;if(f===o&&null==d)return`\n      float ${r}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(vec4(row, col, depth, depth2),\n          vec4(${t[1]*t[2]*t[3]*t[4]},\n               ${t[2]*t[3]*t[4]},\n               ${t[3]*t[4]},\n               ${t[4]})) + float(depth3);\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${f}.0, ${h}.0);\n        return sampleTexture(${n}, uv);\n      }\n    `;const m=ab(n);return`\n    float ${r}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      // Explicitly use integer operations as dot() only works on floats.\n      int index = row * ${c} + col * ${l} + depth * ${u} +\n          depth2 * ${i} + depth3 * ${o} + depth4 + ${m};\n      vec2 uv = uvFromFlat(${h}, ${f}, index);\n      return sampleTexture(${n}, uv);\n    }\n  `}(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function Zy(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return function(e){const t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=Uy();return`\n    vec4 ${n}() {\n      return ${r.texture2D}(${t}, halfCR);\n    }\n  `}(e);case 1:return function(e,t){const n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=e.shapeInfo.texShape,s=Uy();if(t)return`\n    vec4 ${r}(int index) {\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      vec2 uv = packedUVfrom1D(\n        packedTexShape[0], packedTexShape[1], index);\n      return ${s.texture2D}(${n}, uv);\n    }\n  `;const o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];return`\n    vec4 ${r}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${o[0]}, ${o[1]}, index);\n      return ${s.texture2D}(${n}, uv);\n    }\n  `}(e,t);case 2:return function(e,t){const n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,o=s[0],i=s[1],u=Uy();if(null!=s&&p(n,s))return t?`\n      vec4 ${a}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${r}TexShape[1], ${r}TexShape[0]);\n\n        return ${u.texture2D}(${r}, uv);\n      }\n    `:`\n      vec4 ${a}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);\n\n        return ${u.texture2D}(${r}, uv);\n      }\n    `;if(t)return`\n    vec4 ${a}(int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${r}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);\n      return ${u.texture2D}(${r}, uv);\n    }\n  `;const l=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],c=Math.ceil(n[1]/2);return`\n    vec4 ${a}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${c}, ${l[0]}, ${l[1]}, row, col);\n      return ${u.texture2D}(${r}, uv);\n    }\n  `}(e,t);case 3:return function(e,t){const n=e.shapeInfo.logicalShape,r=e.name,a="get"+r.charAt(0).toUpperCase()+r.slice(1),s=e.shapeInfo.texShape,o=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];if(1===n[0]){const r=[1,2],s=["b","row","col"];return`\n        ${Zy(ub(e,n.slice(1)),t)}\n        vec4 ${a}(int b, int row, int col) {\n          return ${a}(${lb(s,r)});\n        }\n      `}const i=Uy();if(t)return`\n    vec4 ${a}(int b, int row, int col) {\n      ivec2 packedTexShape = ivec2(ceil(float(${r}TexShape[0]) / 2.0), ceil(float(${r}TexShape[1]) / 2.0));\n      int valuesPerRow = int(ceil(float(${r}Shape[2]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${r}Shape[1]) / 2.0));\n      vec2 uv = packedUVfrom3D(\n        packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);\n      return ${i.texture2D}(${r}, uv);\n    }\n  `;const u=o[0],l=o[1],c=Math.ceil(n[2]/2),d=c*Math.ceil(n[1]/2);return`\n    vec4 ${a}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${u}, ${l}, ${d}, ${c}, b, row, col);\n      return ${i.texture2D}(${r}, uv);\n    }\n  `}(e,t);default:return function(e,t){const n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),a=Uy();if(t)return`\n    vec4 ${r}(int b2, int b, int row, int col) {\n      int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));\n      int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));\n      int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);\n      texelsInBatch *= ${n}Shape[1];\n      index = b2 * texelsInBatch + index;\n      ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));\n      int texR = index / packedTexShape[1];\n      int texC = index - texR * packedTexShape[1];\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${a.texture2D}(${n}, uv);\n    }\n  `;const s=e.shapeInfo.logicalShape,o=s.length,i=e.shapeInfo.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],l=u[0],c=u[1],d=Math.ceil(s[o-1]/2);let p=d*Math.ceil(s[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=s[o-m-1],f=`b${m} * ${p} + `+f;return`\n    vec4 ${r}(${h}) {\n      int index = ${f};\n      int texR = index / ${c};\n      int texC = index - texR * ${c};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${c}, ${l});\n      return ${a.texture2D}(${n}, uv);\n    }\n  `}(e,t)}}const Jy="\nvec2 uvFromFlat(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",eb="\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",tb="\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",nb="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function rb(){return"\n    int getOutputCoords() {\n      return 0;\n    }\n  "}function ab(e){return`offset${e}`}function sb(e){const t=e.name,n=d(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`\n    for (int i = 0; i < ${n}; i++) {\n      if (i == index) {\n        return ${t}[i];\n      }\n    }\n  `}function ob(e){if(e<=1)return"int";if(2===e)return"ivec2";if(3===e)return"ivec3";if(4===e)return"ivec4";if(5===e)return"ivec5";if(6===e)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function ib(e,t,n){const{newShape:r,keptDims:a}=x(t),s=t.length,o=e&&3===s&&1===t[0],i=o?t.slice(1):r,u=!e&&s>1&&!p(t,n)&&r.length<s||o;return{useSqueezeShape:u,uniformShape:u?i:t,keptDims:a}}function ub(e,t){const n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function lb(e,t){return t.map(t=>e[t]).join(", ")}function cb(e,t,n,r){const a=n.map((e,n)=>{const r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked,flatOffset:null};return null!=e.texData&&null!=e.texData.slice&&e.texData.slice.flatOffset>0&&(r.flatOffset=e.texData.slice.flatOffset),{name:t.variableNames[n],shapeInfo:r}}),s=a.map(e=>e.shapeInfo),o={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked,flatOffset:null},i=Yy(a,o,t),u=function(e,t){const n=Ey(e,()=>e.createShader(e.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(by(e,()=>e.shaderSource(n,t)),by(e,()=>e.compileShader(n)),W().get("ENGINE_COMPILE_ONLY"))return n;if(!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw ky(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}(e.gl,i),l=e.createProgram(u);return W().get("ENGINE_COMPILE_ONLY")?{program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:s,outShapeInfo:o,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(e.buildVao(l),Object.assign({program:t,fragmentShader:u,source:i,webGLProgram:l,inShapeInfos:s,outShapeInfo:o},db(e,t,l)))}function db(e,t,n){const r=[],a=[];let s,o,i,u=null,l=null;l=e.getUniformLocation(n,"NAN",!1),1===W().getNumber("WEBGL_VERSION")&&(u=e.getUniformLocation(n,"INFINITY",!1));const c=!1;for(const d of t.variableNames){const a={name:d,uniform:e.getUniformLocation(n,d,c),offset:e.getUniformLocation(n,`offset${d}`,c)};t.enableShapeUniforms&&(a.shape=e.getUniformLocation(n,`${d}Shape`,c),a.texShape=e.getUniformLocation(n,`${d}TexShape`,c)),r.push(a)}if(t.enableShapeUniforms&&(s=e.getUniformLocation(n,"outShape",c),i=e.getUniformLocation(n,"outShapeStrides",c),o=e.getUniformLocation(n,"outTexShape",c)),t.customUniforms)for(const d of t.customUniforms)a.push(e.getUniformLocation(n,d.name,c));return{variablesLocations:r,customUniformLocations:a,infLoc:u,nanLoc:l,outShapeLocation:s,outShapeStridesLocation:i,outTexShapeLocation:o}}function pb(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((e,n)=>{const r=e.logicalShape,a=t[n],s=a.shape;if(!p(r,s))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${s} must match`);if(e.isUniform&&a.isUniform)return;const o=e.texShape,i=a.isUniform?null:a.texData.texShape;if(!p(o,i))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${i} must match`)})}function hb(e){return W().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}class fb{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=uy.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=Uy();this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Hy(["r","c","d"],e):Gy(["r","c","d"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getA(rc.x, rc.y, rc.z);\n        }\n\n        ${t.output} = result;\n      }\n    `}}class mb{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=uy.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];const t=Uy();this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length),this.userCode=`\n      ivec3 outCoordsFromFlatIndex(int index) {\n        ${this.enableShapeUniforms?Hy(["r","c","d"],e):Gy(["r","c","d"],e)}\n        return ivec3(r, c, d);\n      }\n\n      void main() {\n        ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));\n        int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n        vec4 result = vec4(0.);\n\n        for (int i=0; i<4; i++) {\n          int flatIndex = index + i;\n          ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n          result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n        }\n\n        ${t.output} = result;\n      }\n    `}}class gb{constructor(e){this.variableNames=["A"],this.outTexUsage=cy.DOWNLOAD;const t=Uy();this.outputShape=e,this.userCode=`\n      ${Ky}\n\n      void main() {\n        float x = getAAtOutCoords();\n        ${t.output} = encode_float(x);\n      }\n    `}}class yb{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=cy.DOWNLOAD;const t=Uy();this.outputShape=e,this.userCode=`\n      ${Ky}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n        ${t.output} = encode_float(x);\n      }\n    `}}const bb={R:0,G:1,B:2,A:3};class xb{constructor(e,t=!1,n="RGBA"){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];const r=Uy();this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length);let a="result";t&&(a="floor(result * 255. + 0.5)");let s="";for(let o=0;o<n.length;o++){const e=n[o];s+=`\n          if(offset == ${o}) {\n            result = values[${bb[e]}];\n          }`}this.userCode=`\n      ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":qy(e)}\n\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int flatIndex = getFlatIndex(coords);\n        float result = 0.;\n        int offset = imod(flatIndex, ${n.length});\n\n        flatIndex = idiv(flatIndex, ${n.length}, 1.);\n\n        int r = flatIndex / texShape[1];\n        if (r < texShape[0]) {\n          int c = imod(flatIndex, texShape[1]);\n          vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n          vec4 values = ${r.texture2D}(A, uv);\n          ${s}\n        }\n        ${r.output} = vec4(${a}, 0., 0., 0.);\n      }\n    `}}class vb{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];const n=Uy();this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length);let r="",a="result";t&&(a="floor(result * 255. + 0.5)");for(let s=0;s<=1;s++)for(let t=0;t<=1;t++){const a=2*s+t;r+=`\n          localCoords = coords;\n          if(localCoords[2] + ${t} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {\n          localCoords[2] += ${t};\n          if (localCoords[1] + ${s} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {\n            localCoords[1] += ${s};\n\n            flatIndex = getFlatIndex(localCoords);\n            offset = imod(flatIndex, 4);\n\n            flatIndex = idiv(flatIndex, 4, 1.);\n\n            int r = flatIndex / texShape[1];\n            int c = imod(flatIndex, texShape[1]);\n            vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);\n            values = ${n.texture2D}(A, uv);\n\n            if (offset == 0) {\n              result[${a}] = values[0];\n            } else if (offset == 1) {\n              result[${a}] = values[1];\n            } else if (offset == 2) {\n              result[${a}] = values[2];\n            } else {\n              result[${a}] = values[3];\n            }\n          }\n        }\n        `}this.userCode=`\n        ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":qy(e)}\n\n        void main() {\n          ivec3 coords = getOutputCoords();\n\n          vec4 result = vec4(0.);\n          int flatIndex, r, c, offset;\n          ivec3 localCoords;\n          vec2 uv;\n          vec4 values;\n\n          ${r}\n\n          ${n.output} = ${a};\n        }\n    `}}function wb(e){const t=Uy();return function(e,t){const n=Ey(e,()=>e.createShader(e.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(by(e,()=>e.shaderSource(n,t)),by(e,()=>e.compileShader(n)),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}(e,`${t.version}\n    precision highp float;\n    ${t.attribute} vec3 clipSpacePos;\n    ${t.attribute} vec2 uv;\n    ${t.varyingVs} vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`)}function kb(e){return function(e,t){const n=Ey(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return by(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),by(e,()=>e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)),n}(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function Ib(e){return function(e,t){const n=Ey(e,()=>e.createBuffer(),"Unable to create WebGLBuffer");return by(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)),by(e,()=>e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)),n}(e,new Uint16Array([0,1,2,2,1,3]))}function Nb(e,t,n,r,a,s){!function(e,t){const n=W().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0)throw new Error(`Requested texture size [${e}x${t}] is invalid.`);if(e>n||t>n)throw new Error(`Requested texture size [${e}x${t}] greater than WebGL maximum on this browser / GPU [${n}x${n}].`)}(t,n);const o=function(e){return Ey(e,()=>e.createTexture(),"Unable to create WebGLTexture.")}(e),i=e.TEXTURE_2D;return by(e,()=>e.bindTexture(i,o)),by(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),by(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),by(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),by(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),1===W().getNumber("WEBGL_VERSION")?by(e,()=>e.texImage2D(i,0,r,t,n,0,a,s,null)):by(e,()=>e.texStorage2D(i,1,r,t,n)),by(e,()=>e.bindTexture(e.TEXTURE_2D,null)),{texture:o,texShape:[n,t]}}function Sb(e){return e.internalFormatFloat}function Tb(e){return e.internalFormatHalfFloat}function Cb(e){return e.downloadTextureFormat}function $b(e){return e.internalFormatPackedFloat}function Eb(e){return e.internalFormatPackedHalfFloat}function Rb(e,t,n,r,a,s,o,i){const u=e,l=new Float32Array(function(e,t){const[n,r]=gy(e,t);return n*r*4}(s,o));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}class _b{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];const t=W().getNumber("WEBGL_VERSION");if(null!=e?(this.gl=e,function(e,t){sy[e]=t}(t,e)):this.gl=iy(t),e=this.gl,2===W().getNumber("WEBGL_VERSION")){const t=e;this.createVertexArray=()=>by(t,()=>t.createVertexArray()),this.bindVertexArray=e=>by(t,()=>t.bindVertexArray(e)),this.deleteVertexArray=e=>by(t,()=>t.deleteVertexArray(e)),this.getVertexArray=()=>by(t,()=>t.getParameter(t.VERTEX_ARRAY_BINDING))}else if(null!=e){const t=e.getExtension("OES_vertex_array_object");if(null==t)throw new Error("All WebGL1 implementations are expected to offer OES_vertex_array_object.");this.createVertexArray=()=>by(e,()=>t.createVertexArrayOES()),this.bindVertexArray=n=>by(e,()=>t.bindVertexArrayOES(n)),this.deleteVertexArray=n=>by(e,()=>t.deleteVertexArrayOES(n)),this.getVertexArray=()=>by(e,()=>e.getParameter(t.VERTEX_ARRAY_BINDING_OES))}let n="WEBGL_color_buffer_float";const r="EXT_color_buffer_half_float";if(this.parallelCompilationExtension=this.gl.getExtension("KHR_parallel_shader_compile"),1===W().getNumber("WEBGL_VERSION")){const e="OES_texture_float",t="OES_texture_half_float";if(this.textureFloatExtension=vy(this.gl,e),Py(this.gl,t))this.textureHalfFloatExtension=vy(this.gl,t);else if(W().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.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Py(this.gl,r))this.colorBufferHalfFloatExtension=vy(this.gl,r);else if(W().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 if(n="EXT_color_buffer_float",Py(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else{if(!Py(this.gl,r))throw new Error("GL context does not support color renderable floats");this.colorBufferHalfFloatExtension=this.gl.getExtension(r)}this.vertexBuffer=kb(this.gl),this.indexBuffer=Ib(this.gl),this.framebuffer=function(e){return Ey(e,()=>e.createFramebuffer(),"Unable to create WebGLFramebuffer.")}(this.gl),this.textureConfig=yy(this.gl,this.textureHalfFloatExtension)}get debug(){return W().getBool("DEBUG")}dispose(){if(this.disposed)return;null!=this.program&&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."),null!=this.outputTexture&&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 e=this.gl;by(e,()=>e.finish()),by(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),by(e,()=>e.deleteFramebuffer(this.framebuffer)),by(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),by(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),by(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=fy(t,n);return Nb(e,a,s,Sb(r),r.textureFormatFloat,e.FLOAT)}(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=fy(t,n);return Nb(e,a,s,Tb(r),r.textureFormatFloat,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=fy(t,n);return Nb(e,a,s,Cb(r),e.RGBA,e.UNSIGNED_BYTE)}(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),function(e,t,n){by(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?2===W().getNumber("WEBGL_VERSION")?by(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)):by(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):2===W().getNumber("WEBGL_VERSION")?by(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)):by(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),by(e,()=>e.bindTexture(e.TEXTURE_2D,null))}(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,r){this.throwIfDisposed(),function(e,t,n,r,a,s){let o,i,u;by(e,()=>e.bindTexture(e.TEXTURE_2D,t)),a instanceof Uint8Array?(o=new Uint8Array(n*r*4),i=e.UNSIGNED_BYTE,u=e.RGBA):(o=new Float32Array(n*r*4),i=e.FLOAT,u=s.internalFormatPackedFloat),o.set(a),2===W().getNumber("WEBGL_VERSION")?by(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,e.RGBA,i,o)):by(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,r,0,e.RGBA,i,o)),by(e,()=>e.bindTexture(e.TEXTURE_2D,null))}(this.gl,e,t,n,r,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=gy(t,n);return Nb(e,a,s,Eb(r),e.RGBA,r.textureTypeHalfFloat)}(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),function(e,t,n,r){const[a,s]=gy(t,n);return Nb(e,a,s,$b(r),e.RGBA,e.FLOAT)}(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Cy(this.gl,this.framebuffer),this.outputTexture=null),by(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>function(e,t,n,r){const[a,s]=fy(t,n),o=new Uint8Array(t*n*4);return by(e,()=>e.readPixels(0,0,a,s,r.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,r,a,s){return Rb(this.gl,e,0,0,0,a,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return function(e,t,n){const r=e,a=new Float32Array(n);return r.bindBuffer(r.PIXEL_PACK_BUFFER,t),r.getBufferSubData(r.PIXEL_PACK_BUFFER,0,a),r.bindBuffer(r.PIXEL_PACK_BUFFER,null),a}(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);const r=function(e,t,n){const r=e.createBuffer();by(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));const a=16*t*n;return by(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,a,e.STREAM_READ)),by(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),by(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r}createAndWaitForFence(){const e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(W().getBool("WEBGL_FENCE_API_ENABLED")){const r=e,a=r.fenceSync(r.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{const e=r.clientWaitSync(a,0,0);return e===r.ALREADY_SIGNALED||e===r.CONDITION_SATISFIED},t=a}else W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>function(e,t,n){const r=new Float32Array(t*n*4);return by(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,r)),r}(this.gl,t,n))}createProgram(e){this.throwIfDisposed();const t=this.gl;null==this.vertexShader&&(this.vertexShader=wb(t));const n=function(e){return Ey(e,()=>e.createProgram(),"Unable to create WebGLProgram.")}(t);by(t,()=>t.attachShader(n,this.vertexShader)),by(t,()=>t.attachShader(n,e)),function(e,t){if(by(e,()=>e.linkProgram(t)),!W().get("ENGINE_COMPILE_ONLY")&&!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}(t,n);const r=Object.assign(n,{vao:this.createVertexArray()});return this.debug&&Iy(t,r),r}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);const t=this.gl;by(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),function(e,t,n){by(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Ny(e,t,"clipSpacePos",n,3,20,0)&&Ny(e,t,"uv",n,2,20,12)}(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&(by(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,null!=this.program&&this.debug&&Iy(this.gl,this.program),by(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?function(e,t,n){return Ey(e,()=>e.getUniformLocation(t,n),'uniform "'+n+'" not present in program.')}(this.gl,e,t):function(e,t,n){return e.getUniformLocation(t,n)}(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),by(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),function(e,t,n,r){by(e,()=>Sy(e,t,r)),by(e,()=>e.uniform1i(n,r))}(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();const[r,a]=gy(t,n);this.setOutputMatrixTextureDriver(e,r,a)}setOutputMatrixWriteRegion(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)}setOutputPackedMatrixWriteRegion(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){null!=this.program&&Iy(this.gl,this.program),$y(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();const e=this.gl;if(this.debug){const e=this.getVertexArray();console.assert(e===this.program.vao,"VAO changed between setProgram and executeProgram!"),this.debugValidate()}by(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),by(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=vy(this.gl,2===W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(2===W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}const e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(2===W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){const e=this.gl,t=this.getQueryTimerExtensionWebGL2();return void e.endQuery(t.TIME_ELAPSED_EXT)}const e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}waitForQueryAndGetTime(t){return e(this,null,function*(){return yield g(()=>this.disposed||this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})}getQueryTime(e,t){if(0===t)return null;if(2===t){const t=this.gl;return t.getQueryParameter(e,t.QUERY_RESULT)/1e6}{const t=this.getQueryTimerExtensionWebGL1();return t.getQueryObjectEXT(e,t.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(0===t)return!0;if(2===t){const t=this.gl,n=this.getQueryTimerExtensionWebGL2(),r=t.getQueryParameter(e,t.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),r&&!this.disjoint}{const 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2147483647");p[m+1]=p[m]+o}const h=w(n,p[d]);let f=0;for(let m=0;m<d;++m){const t=p[m+1]-p[m];let n=i?e[0]:e[m];const r=l?s[0]:s[m];for(let e=0;e<t;++e)h[f++]=n,n+=r}return[p,h]}var gv=Op;class yv{constructor(e,t,n,r,a,s,o,i,u,l){this.shape=e,this.shapeShape=t,this.values=n,this.valuesShape=r,this.valuesDType=a,this.defaultValue=s,this.defaultValueShape=o,this.rowPartitionValues=i,this.rowPartitionValuesShapes=u,this.rowPartitionTypes=Mp(l),this.raggedRank=Pp(this.rowPartitionTypes)}getRowPartitionTypeByDimension(e){return this.rowPartitionTypes[0]===gv.FIRST_DIM_SIZE?this.rowPartitionTypes[e+1]:this.rowPartitionTypes[e]}getRowPartitionTensor(e){return this.rowPartitionTypes[0]===gv.FIRST_DIM_SIZE?this.rowPartitionValues[e+1]:this.rowPartitionValues[e]}getMaxWidth(e){const t=this.getRowPartitionTensor(e-1);switch(this.getRowPartitionTypeByDimension(e-1)){case gv.VALUE_ROWIDS:return yv.getMaxWidthValueRowID(t);case gv.ROW_SPLITS:return yv.getMaxWidthRowSplit(t);default:throw new Error(`Cannot handle partition type ${gv[this.getRowPartitionTypeByDimension(e-1)]}`)}}static getMaxWidthRowSplit(e){const t=e.length;if(0===t||1===t)return 0;let n=0;for(let r=0;r<t-1;++r){const t=e[r+1]-e[r];t>n&&(n=t)}return n}static getMaxWidthValueRowID(e){const t=e.length;if(0===t)return 0;let n=0,r=e[0],a=0;for(let s=1;s<t;++s){const t=e[s];t!==r&&(r=t,a=Math.max(s-n,a),n=s)}return Math.max(t-n,a)}tensorShapeFromTensor(e,t,n=!0){if(0===t.length){if(-1===e[0])return[];throw new Error("The only valid scalar shape tensor is the fully unknown shape specified as -1.")}return xv(e,n)}calculateOutputSize(e){const t=this.valuesShape;Lp(this.defaultValueShape,t);const n=this.tensorShapeFromTensor(this.shape,this.shapeShape),r=Dp(this.raggedRank,n,t);r[0]<0&&(r[0]=e);for(let a=1;a<=this.raggedRank;++a)r[a]<0&&(r[a]=this.getMaxWidth(a));return r}calculateFirstParentOutputIndex(e,t,n){const r=Math.min(e,n),a=[];let s=0;for(let o=0;o<r;++o,s+=t)a.push(s);for(let o=r;o<e;++o)a.push(-1);return u(a.length===e,()=>"Final length of result must be equal to firstDimension."),a}calculateOutputIndexRowSplit(e,t,n,r){const a=e.length,s=[];for(let o=0;o<a-1;++o){const a=e[o+1]-e[o];let i=Math.min(r,a),u=t[o];-1===u&&(i=0);for(let e=0;e<i;++e)s.push(u),u+=n;for(let e=0;e<a-i;++e)s.push(-1)}if(a>0&&s.length!==e[a-1])throw new Error("Invalid row split size.");return s}calculateOutputIndexValueRowID(e,t,n,r){const a=e.length,s=[];if(0===a)return[];let o=0,i=e[0];if(i>=t.length)throw new Error(`Got currentValueRowId=${i}, which is not less than ${t.length}`);let u=t[i];s.push(u);for(let l=1;l<a;++l){const a=e[l];if(a===i)u>=0&&(++o,o<r?u+=n:u=-1);else{if(o=0,i=a,a>=t.length)throw new Error(`Got nextValueRowId=${a} which is not less than ${t.length}`);u=t[a]}s.push(u)}if(s.length!==e.length)throw new Error("Invalid row ids.");return s}calculateOutputIndex(e,t,n,r){const a=this.getRowPartitionTensor(e),s=this.getRowPartitionTypeByDimension(e);switch(s){case gv.VALUE_ROWIDS:return this.calculateOutputIndexValueRowID(a,t,n,r);case gv.ROW_SPLITS:if(a.length-1>t.length)throw new Error(`Row partition size is greater than output size: ${a.length-1} > ${t.length}`);return this.calculateOutputIndexRowSplit(a,t,n,r);default:throw new Error(`Unsupported partition type: ${gv[s]}`)}}getFirstDimensionSize(){const e=this.rowPartitionValues[0];if(0===this.rowPartitionTypes.length)throw new Error("No row_partition_types given.");const t=this.rowPartitionTypes[0];switch(t){case gv.FIRST_DIM_SIZE:return e[0];case gv.VALUE_ROWIDS:throw new Error("Cannot handle VALUE_ROWIDS in first dimension.");case gv.ROW_SPLITS:return this.rowPartitionValuesShapes[0][0]-1;default:throw new Error(`Cannot handle type ${gv[t]}`)}}compute(){if(this.rowPartitionValues[0].length<=0)throw new Error("Invalid first partition input. Tensor requires at least one element.");const e=this.getFirstDimensionSize(),t=this.calculateOutputSize(e),n=new Array(this.raggedRank+1);n[n.length-1]=1;for(let s=n.length-2;s>=0;--s)n[s]=n[s+1]*t[s+1];const r=xv(t,!1),a=w(this.valuesDType,d(r));if(n[0]*t[0]>0){let s=this.calculateFirstParentOutputIndex(e,n[0],t[0]);for(let e=1;e<=this.raggedRank;++e){s=this.calculateOutputIndex(e-1,s,n[e],t[e])}this.setOutput(this.raggedRank,s,a,r)}return[r,a]}setOutput(e,t,n,r){if(0===n.length)return;const a=this.values,s=n;let o=r.slice();o=o.slice(e+1);const i=d(o),u=t.length;let l=this.defaultValue;if(l.length!==i&&1!==l.length){const e=this.defaultValueShape;Va(()=>{const t=wo(l,e),n=Lo(t,o);l=n.dataSync()})}let c=0,p=0,h=0;for(let d=0;d<=u;++d){let e=d<u?t[d]:-1;if(e!==h){if(p<h){const e=a.subarray(c*i);bv(s.subarray(p*i),e,(h-p)*i)}if(d>=u){const t=n.length;e=Math.floor(t/i)}if(e>h)if(1===this.defaultValue.length)s.subarray(h*i,e*i).fill(this.defaultValue[0]),h=e;else for(;e>h;){bv(s.slice(h*i),l,i),++h}e<0?(c=d+1,p=h):(c=d,p=h,h=p+1)}else++h}}}function bv(e,t,n){for(let r=0;r<n;r++)e[r]=t[r]}function xv(e,t){const n=[];for(let r of e){if(r<0){if(!t)throw new Error(`Dimension ${r} must be >= 0`);if(r<-1)throw new Error(`Dimension ${r} must be >= -1`);r=-1}n.push(r)}return n}function vv(e,t,n,r,a,s,o,i,u,l){return new yv(e,t,n,r,a,s,o,i,u,l).compute()}function wv(e,t,n,r){if(e===t||e<t&&n<0||t<e&&n>1)return A(0,r);const a=A(Math.abs(Math.ceil((t-e)/n)),r);t<e&&1===n&&(n=-1),a[0]=e;for(let s=1;s<a.length;s++)a[s]=a[s-1]+n;return a}const kv=rx(e=>1/Math.sqrt(e)),Iv=sx(pn,kv),Nv={kernelName:pn,backendName:"cpu",kernelFunc:Iv};function Sv(e,t,n,r,a,s,o,i,u,l){const c=[r/a,a],d=e.values,p=t.values;if(0===r)return Ls(n,t.dtype);const h=u instanceof Kr?u:Ls(c,t.dtype);"string"===typeof u||"number"===typeof u?h.values.fill(u):"boolean"===typeof u&&h.values.fill(+u);for(let f=0;f<s;f++){const e=[];let s=0;for(let t=0;t<o;t++){const n=d[f*o+t];e.push(n),s+=n*i[t]}if(s<0||s>=r/a)throw new Error(`Invalid indices: ${e} does not index into ${n}`);for(let n=0;n<a;n++)l?h.values[s*a+n]+=p[f*a+n]:h.values[s*a+n]=0===t.rank?p[0]:p[f*a+n]}return h}const Tv=rx(e=>1/(1+Math.exp(-e))),Cv=ax(kn,e=>1/(1+Math.exp(-e))),$v={kernelName:kn,backendName:"cpu",kernelFunc:Cv};function Ev(e,t,n,r,a){const s=Np(r,t,n),o=d(n),i=$(r);if(s){const n=Sp(t,i);return"string"===a?e.slice(n,n+o):e.subarray(n,n+o)}const u=Ls(r,a,"string"===a?Rh(e):e),l=Ls(n,a);for(let c=0;c<l.size;++c){const e=l.indexToLoc(c),n=e.map((e,n)=>e+t[n]);l.set(u.get(...n),...e)}return"string"===a?_h(l.values):l.values}function Rv(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:o}=r;Ab(a,"slice");const[i,u]=Tp(a,s,o);kp(a,i,u);const l=Ev(n.data.get(a.dataId).values,i,u,a.shape,a.dtype);return n.makeTensorInfo(u,a.dtype,l)}const _v={kernelName:bn,backendName:"cpu",kernelFunc:Rv};function Av(e,t,n,r,a,s,o){const i=t[0],u=s[0],l=new Array(u),c=new Array(i),d=t[1];if(0===u){if(0!==i)throw new Error(yh(i));return[w(n,0),[0,d],w(a,0),l,c]}let p=!0,h=0;const f=new Array(u).fill(0);for(let g=0;g<i;++g){const t=e[g*d];if(t<0)throw new Error(bh(g,t));if(t>=u)throw new Error(xh(g,t,u));++f[t],p=p&&t>=h,h=t}let m=!0;for(let g=0;g<u;++g){const e=0===f[g];l[g]=e,m=m&&!e,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){const t=e,n=r;for(let e=0;e<i;++e)c[e]=e;return[t,[i,d],n,l,c]}{const t=f[u-1],s=w(n,t*d),p=w(a,t),h=new Array(u).fill(0);for(let n=0;n<i;++n){const t=e[n*d],a=h[t],o=(0===t?0:f[t-1])+a;h[t]++;for(let r=0;r<d;++r)s[o*d+r]=e[n*d+r];p[o]=r[n],c[n]=o}for(let e=0;e<u;++e){if(0===h[e]){const t=0===e?0:f[e-1];s[t*d+0]=e;for(let e=1;e<d;++e)s[t*d+e]=0;p[t]=o}}return[s,[t,d],p,l,c]}}function Ov(e,t,n,r,a){const s=d(r),o=t[0],i=a.length,u=[];let l=1,c=-1;for(let d=0;d<i;++d){const e=a[d];if(-1===e){if(-1!==c)throw new Error(vh(c,d));c=d,u.push(1)}else{if(e<0)throw new Error(wh(d,e));l*=e,u.push(e)}}if(-1!==c){if(l<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");const e=Math.trunc(s/l);if(l*e!==s)throw new Error(Ih(r,u));u[c]=e}if(d(u)!==s)throw new Error(Nh(r,u));const p=r.length,h=[];if(p>0){h[p-1]=1;for(let e=p-2;e>=0;--e)h[e]=h[e+1]*r[e+1]}const f=[];if(i>0){f[i-1]=1;for(let e=i-2;e>=0;--e)f[e]=f[e+1]*u[e+1]}const m=w(n,o*i);for(let d=0;d<o;++d){let t=0;for(let n=0;n<p;++n)t+=e[d*p+n]*h[n];for(let e=0;e<i;++e)m[d*i+e]=Math.trunc(t/f[e]),t%=f[e]}return[m,[o,i],u]}function Fv(e,t,n,r,a,s=!1,o=0){const i=r.length,u=[t[0],e.length/t[0]],l=u[1],c=i>0?a[i-1]+1:0;if(c<0)throw new Error("segment ids must be >= 0");const d=t.slice();d[0]=c;const p=w(n,d.reduce((e,t)=>e*t,1));if(0===i)return c>0&&p.fill(o),[p,d];if(c<=0)throw new Error("segment ids must be >= 0");let h=0,f=1,m=0,g=a[h];for(;;){let t=0;if(f<i){if(t=a[f],g===t){++f;continue}if(g>=t)throw new Error(Th())}if(g<0||g>=c)throw new Error(Ch(g,c));g>m&&p.fill(o,m*l,g*l);for(let n=h;n<f;++n){const t=r[n];if(t<0||t>=u[0])throw new Error($h(n,r[n],u[0]));for(let n=0;n<l;n++)p[g*l+n]+=e[t*l+n]}if(s)for(let e=0;e<l;e++)p[g*l+e]/=f-h;if(h=f,++f,m=g+1,g=t,f>i)break}return m<c&&p.fill(o,m*l,c*l),[p,d]}const Dv=rx(e=>Math.sqrt(e)),Mv=ax(Nn,e=>Math.sqrt(e)),Pv={kernelName:Nn,backendName:"cpu",kernelFunc:Mv},Lv=Db((e,t)=>{const n=e-t;return n*n}),Bv=jb(Fn,Lv),Vv={kernelName:Fn,backendName:"cpu",kernelFunc:Bv},Wv=rx((e,t)=>{const{pattern:n,replaceGlobal:r,rewrite:a}=t;return e.replace(new RegExp(n,r?"g":""),a)}),zv=sx(Mn,Wv),Uv={kernelName:Mn,backendName:"cpu",kernelFunc:zv};function Gv(e,t,n,r){const a=Ls(e,t.dtype);for(let s=0;s<a.size;s++){const e=a.indexToLoc(s),o=new Array(e.length);for(let t=0;t<o.length;t++)o[t]=e[t]*n[t]+r[t];a.set(t.get(...o),...e)}return a}class Hv{constructor(e,t,n,r,a,s){this.separator=Mr(e),this.nGramWidths=t,this.leftPad=Mr(n),this.rightPad=Mr(r),this.padWidth=a,this.preserveShort=s}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){const n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,r,a,s){for(let o=0;o<a;++o){const i=this.getPadWidth(s),u=Math.max(0,i-o),l=Math.max(0,i-(a-(o+1))),c=s-(u+l),d=t+(u>0?0:o-i);let p=0;p+=u*this.leftPad.length;for(let t=0;t<c;++t)p+=e[d+t].length;p+=l*this.rightPad.length;p+=(u+l+c-1)*this.separator.length,n[r+o]=new Uint8Array(p);const h=n[r+o];let f=0;const m=e=>e.forEach(e=>h[f++]=e);for(let e=0;e<u;++e)m(this.leftPad),m(this.separator);for(let t=0;t<c-1;++t)m(e[d+t]),m(this.separator);if(c>0){m(e[d+c-1]);for(let e=0;e<l;++e)m(this.separator),m(this.rightPad)}else{for(let e=0;e<l-1;++e)m(this.rightPad),m(this.separator);m(this.rightPad)}}}compute(e,t){const n=e.length,r=t.length;if(r>0){let e=t[0];if(0!==e)throw new Error(`First split value must be 0, got ${e}`);for(let a=1;a<r;++a){let r=t[a]>=e;if(r=r&&t[a]<=n,!r)throw new Error(`Invalid split value ${t[a]}, must be in [${e}, ${n}]`);e=t[a]}if(e!==n)throw new Error(`Last split value must be data size. Expected ${n}, got ${e}`)}const a=r-1,s=w("int32",r);if(0===n||0===r){const e=new Array(n);for(let t=0;t<=a;++t)s[t]=0;return[e,s]}s[0]=0;for(let i=1;i<=a;++i){const e=t[i]-t[i-1];let n=0;this.nGramWidths.forEach(t=>{n+=this.getNumNGrams(e,t)}),this.preserveShort&&e>0&&0===n&&(n=1),s[i]=s[i-1]+n}const o=new Array(s[a]);for(let i=0;i<a;++i){const n=t[i];let r=s[i];if(this.nGramWidths.forEach(a=>{const s=t[i+1]-t[i],u=this.getNumNGrams(s,a);this.createNGrams(e,n,o,r,u,a),r+=u}),this.preserveShort&&r===s[i]){const a=t[i+1]-t[i];if(0===a)continue;const s=a+2*this.padWidth,u=1;this.createNGrams(e,n,o,r,u,s)}}return[o,s]}}function jv(e,t,n,r,a,s,o,i){return new Hv(n,r,a,s,o,i).compute(e,t)}function qv(e,t,n,r){if(!e.length)return;if(0===t.length){for(let t=0;t<e.length;++t)r.push(e.subarray(t,t+1));return}if(1===t.length){const a=t[0];let s=e.indexOf(a);for(;-1!==s;){const t=e.subarray(0,s);n&&0===t.length||r.push(t),s=(e=e.subarray(s+1)).indexOf(a)}return void(n&&0===e.length||r.push(e))}let a=0;for(let s=0;s<e.length+1;s++)if(s===e.length||-1!==t.indexOf(e[s])){const t=e.subarray(a,s);n&&0===t.length||r.push(t),a=s+1}}function Kv(e,t,n){const r=e.length,a=[];let s=0,o=0;const i=new Array(r);for(let p=0;p<r;++p){const r=a.length;qv(e[p],t,n,a);const u=a.length-r;i[p]=u,s+=u,o=Math.max(o,u)}const u=w("int32",2*s),l=new Array(s),c=[r,o];let d=0;for(let p=0;p<r;++p)for(let e=0;e<i[p];++e)u[2*d]=p,u[2*d+1]=e,l[d]=a[d],++d;return[u,l,c]}function Xv(e,t){const n=w("int32",e.length);for(let r=0;r<e.length;++r)n[r]=Ar(e[r]).modulo(t).getLowBitsUnsigned();return n}const Yv=Db((e,t)=>e-t),Qv=qb((e,t,n,r)=>({real:e-n,imag:t-r})),Zv=jb(Wn,Yv,Qv),Jv={kernelName:Wn,backendName:"cpu",kernelFunc:Zv};function ew(e,t){const n=new Array(e.rank);for(let a=0;a<n.length;a++)n[a]=e.shape[a]*t[a];const r=Ls(n,e.dtype);for(let a=0;a<r.values.length;++a){const t=r.indexToLoc(a),n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=t[r]%e.shape[r];const s=e.locToIndex(n);r.values[a]=e.values[s]}return r}const tw=(e,t)=>{const n=t.value-e.value;return 0===n?e.index-t.index:n};function nw(e,t,n=0,r=e.length-1){for(;r>n;){if(r-n>600){const a=r-n+1,s=t-n+1,o=Math.log(a),i=.5*Math.exp(2*o/3),u=.5*Math.sqrt(o*i*(a-i)/a)*Math.sign(s-a/2);nw(e,t,Math.max(n,Math.floor(t-s*i/a+u)),Math.min(r,Math.floor(t+(a-s)*i/a+u)))}const a=e[t];let s=n,o=r;for(i(e,n,t),tw(e[r],a)>0&&i(e,n,r);s<o;){for(i(e,s,o),s++,o--;tw(e[s],a)<0;)s+=1;for(;tw(e[o],a)>0;)o-=1}0===tw(e[n],a)?i(e,n,o):(o+=1,i(e,o,r)),o<=t&&(n=o+1),t<=o&&(r=o-1)}}function rw(e,t,n,r,a){const s=t[t.length-1],[o,i]=[e.length/s,s],u=v(n,o*r),l=v("int32",o*r);for(let d=0;d<o;d++){const t=d*i,n=e.subarray(t,t+i);let s=new Array(n.length);n.forEach((e,t)=>s[t]={value:e,index:t}),r<s.length&&(nw(s,r),s=s.slice(0,r)),a&&s.sort(tw);const o=d*r,c=u.subarray(o,o+r),p=l.subarray(o,o+r);for(let e=0;e<r;e++)c[e]=s[e].value,p[e]=s[e].index}const c=t.slice();return c[c.length-1]=r,[Ls(c,n,u),Ls(c,"int32",l)]}function aw(e,t,n,r){const a=b(t,n)[0],s=[1,n[0],1];for(let f=0;f<a;f++)s[0]*=n[f];s[1]=n[a];for(let f=a+1;f<n.length;f++)s[2]*=n[f];const o=new Map,i=new Int32Array(n[a]),u=new Kr(s,r,e),l=[],c=1===s[0]&&1===s[2];for(let f=0;f<n[a];f++){let t;if(c)t=e[f].toString();else{const e=[];for(let t=0;t<s[0];t++)for(let n=0;n<s[2];n++)e.push(u.get(t,f,n));t=e.join(",")}const n=o.get(t);if(null!=n)i[f]=n;else{const e=o.size;o.set(t,e),i[f]=e,l.push(f)}}const d=s.slice();d[1]=o.size;const p=new Kr(d,r);l.forEach((e,t)=>{for(let n=0;n<s[0];n++)for(let r=0;r<s[2];r++)p.set(u.get(n,e,r),n,t,r)});const h=n.slice();return h[a]=d[1],{outputValues:p.values,outputShape:h,indices:i}}const sw=Object.freeze(Object.defineProperty({__proto__:null,addImpl:Kb,bincountImpl:Zb,bincountReduceImpl:Jb,bitwiseAndImpl:ex,castImpl:Ub,ceilImpl:ox,concatImpl:lx,equalImpl:cx,expImpl:hx,expm1Impl:gx,floorDivImpl:kx,floorImpl:xx,gatherNdImpl:Sx,gatherV2Impl:Tx,greaterEqualImpl:Rx,greaterImpl:Cx,lessEqualImpl:Mx,lessImpl:Ox,linSpaceImpl:Bx,logImpl:Vx,maxImpl:Ux,maximumImpl:Gx,minimumImpl:qx,multiplyImpl:Yx,negImpl:ev,notEqualImpl:nv,prodImpl:uv,raggedGatherImpl:hv,raggedRangeImpl:mv,raggedTensorToTensorImpl:vv,rangeImpl:wv,rsqrtImpl:kv,scatterImpl:Sv,sigmoidImpl:Tv,simpleAbsImpl:Ob,sliceImpl:Ev,sparseFillEmptyRowsImpl:Av,sparseReshapeImpl:Ov,sparseSegmentReductionImpl:Fv,sqrtImpl:Dv,squaredDifferenceImpl:Lv,staticRegexReplaceImpl:Wv,stridedSliceImpl:Gv,stringNGramsImpl:jv,stringSplitImpl:Kv,stringToHashBucketFastImpl:Xv,subImpl:Yv,tileImpl:ew,topKImpl:rw,transposeImpl:sv,uniqueImpl:aw},Symbol.toStringTag,{value:"Module"})),{addImpl:ow,bincountImpl:iw,bincountReduceImpl:uw,bitwiseAndImpl:lw,castImpl:cw,ceilImpl:dw,concatImpl:pw,equalImpl:hw,expImpl:fw,expm1Impl:mw,floorImpl:gw,gatherNdImpl:yw,gatherV2Impl:bw,greaterImpl:xw,greaterEqualImpl:vw,lessImpl:ww,lessEqualImpl:kw,linSpaceImpl:Iw,logImpl:Nw,maxImpl:Sw,maximumImpl:Tw,minimumImpl:Cw,multiplyImpl:$w,negImpl:Ew,notEqualImpl:Rw,prodImpl:_w,raggedGatherImpl:Aw,raggedRangeImpl:Ow,raggedTensorToTensorImpl:Fw,rangeImpl:Dw,rsqrtImpl:Mw,scatterImpl:Pw,sigmoidImpl:Lw,simpleAbsImpl:Bw,sliceImpl:Vw,sparseFillEmptyRowsImpl:Ww,sparseReshapeImpl:zw,sparseSegmentReductionImpl:Uw,sqrtImpl:Gw,staticRegexReplaceImpl:Hw,stridedSliceImpl:jw,stringNGramsImpl:qw,stringSplitImpl:Kw,stringToHashBucketFastImpl:Xw,subImpl:Yw,tileImpl:Qw,topKImpl:Zw,transposeImpl:Jw,uniqueImpl:ek}=sw;function tk(e,t){return["x","y","z","w","u","v"].slice(0,t).map(t=>`${e}.${t}`)}function nk(e,t){return 1===t?[e]:tk(e,t)}class rk{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=hb(this.outputShape.length),0===this.rank)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{const e=nk("rc",this.rank),t=ob(this.rank),n=this.getOutOfBoundsCondition(e),r=this.getSetup(e),a=this.getOutput(e);this.userCode=`\n        void main() {\n          ${t} rc = getOutputCoords();\n\n          if(${n}) {\n            setOutput(vec4(0));\n          } else {\n            ${r}\n\n            setOutput(vec4(${a}));\n          }\n        }\n      `}}getSourceCoordsArr(e){const t=[];for(let n=0;n<=1;n++)for(let r=0;r<=1;r++){let a=`${0===n?"r":"rp1"}, ${0===r?"c":"cp1"}`;for(let t=2;t<this.rank;t++)a=`${e[e.length-1-t]},`+a;t.push(a)}return t}getOutOfBoundsCondition(e){if(1===this.rank)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(1===this.rank)return"";const t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],r=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`\n      int r = ${t[0]};\n      int c = ${t[1]};\n      int rp1 = r + 1;\n      int cp1 = c + 1;\n\n      bool cEdge = cp1 >= ${n};\n      bool rEdge = rp1 >= ${r};\n    `}getOutput(e){const t=this.getSourceCoordsArr(e);if(1===this.rank){return`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`}return`getA(${t[0]}),\n            cEdge ? 0. : getA(${t[1]}),\n            rEdge ? 0. : getA(${t[2]}),\n            rEdge || cEdge ? 0. : getA(${t[3]})`}}class ak{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length);let n="";for(let s=0;s<4;s++){let e="thisRC = rc;";s%2===1&&(e+="thisRC.z += 1;"),s>1&&(e+="thisRC.y += 1;"),n+=`\n        ${e}\n        ${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${s}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${s>0?"}":""}\n      `}var r,a;this.userCode=`\n      ${r=t,a=this.enableShapeUniforms,`\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${a?jy(["r","c","d"],"inputShape"):Gy(["r","c","d"],r)}\n      return ivec3(r, c, d);\n    }\n  `}\n      ${this.enableShapeUniforms?"\n  int getFlatIndex(ivec3 coords) {\n    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;\n  }\n":qy(e)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};\n        int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};\n\n        ${n}\n\n        setOutput(result);\n      }\n    `}}class sk{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,n){const r=ik(t,n),a=uk(e,r,n);a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]);const s=ok(e,r,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();const e=this.freeTextures[a].pop();return this.usedTextures[a].push(e),e}let o;return r===py.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):r===py.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):r===py.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):r===py.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):r===py.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(o),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),o}releaseTexture(e,t,n,r){if(null==this.freeTextures)return;const a=ik(n,r),s=uk(t,a,r);s in this.freeTextures||(this.freeTextures[s]=[]);const o=ok(t,a,this.gpgpu.gl,this.gpgpu.textureConfig,r),i=W().getNumber("WEBGL_DELETE_TEXTURE_THRESHOLD");-1!==i&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=o):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;const u=this.usedTextures[s],l=u&&u.indexOf(e);if(null==l||l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u[l]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;const e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);const t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(null!=this.freeTextures){for(const e in this.freeTextures)this.freeTextures[e].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});for(const e in this.usedTextures)this.usedTextures[e].forEach(e=>{this.gpgpu.deleteMatrixTexture(e.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}}function ok(e,t,n,r,a){const s=function(e,t){switch(e){case py.PACKED_2X2_FLOAT32:return $b(t);case py.PACKED_2X2_FLOAT16:return Eb(t);case py.UNPACKED_FLOAT32:return Sb(t);case py.UNPACKED_FLOAT16:return Tb(t);case py.PACKED_4X1_UNSIGNED_BYTE:return Cb(t);default:throw new Error(`Unknown physical texture type ${e}`)}}(t,r);let o;if(a){const[t,n]=gy(e[0],e[1]);o=t*n}else{const[t,n]=fy(e[0],e[1]);o=t*n}const i=function(e,t){const n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}(n,s);return o*i}function ik(e,t){if(e===cy.UPLOAD)return py.PACKED_2X2_FLOAT32;if(e===cy.RENDER||null==e)return function(e){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?py.PACKED_2X2_FLOAT32:py.UNPACKED_FLOAT32:e?py.PACKED_2X2_FLOAT16:py.UNPACKED_FLOAT16}(t);if(e===cy.DOWNLOAD||e===cy.PIXELS)return py.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function uk(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}class lk{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length),this.userCode=`\n      float unaryOperation(float x) {\n        ${t}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}}const ck="if (isnan(x)) return x;",dk="return abs(x);",pk=ck+"\n  return (x < 0.0) ? 0.0 : x;\n",hk=ck+"\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n",fk="return x;";class mk{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length),this.userCode=`\n      vec4 unaryOperation(vec4 x) {\n        ${t}\n      }\n\n      void main() {\n        vec4 x = getAAtOutCoords();\n        vec4 y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `}}class gk{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=hb(this.outputShape.length);const t=e.length,n=nk("rc",t),r=ob(t),a=function(e,t){if(1===e)return"rc";let n="";for(let r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}(t,n),s=n.slice(-2),o=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`\n      void main() {\n        ${r} rc = getOutputCoords();\n        vec4 packedInput = getA(${a});\n\n        setOutput(getChannel(packedInput, ${o}));\n      }\n    `}}const yk=kc,bk={};const xk=W().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");class vk extends r{nextDataId(){return vk.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");let t;if(null!=e){if(e instanceof _b)t=e;else{const n=iy(W().getNumber("WEBGL_VERSION"),e);t=new _b(n)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{const e=iy(W().getNumber("WEBGL_VERSION"));t=new _b(e),this.binaryCache=((r=W().getNumber("WEBGL_VERSION"))in bk||(bk[r]={}),bk[r]),this.gpgpuCreatedLocally=!0}var r;this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new sk(this.gpgpu),this.numMBBeforeWarning=null==W().global.screen?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*600/1024/1024,this.texData=new n(this,Ba())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,n,r,a,s){const o=this.makeTensorInfo(t,n),i=this.texData.get(o.dataId);i.isPacked=!1,i.texture={texture:e,texShape:[r,a]},i.texShape=[r,a];const u=Ay(t),l=new xb(u,!1,s),c=this.runWebGLProgram(l,[o],n,[[r,a]]);return c.shape=t,i.texture=null,this.disposeIntermediateTensorInfo(o),c.dataId}write(e,t,n){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),"complex64"===n&&null!=e)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");const r={id:this.nextDataId()};return this.texData.set(r,{shape:t,dtype:n,values:e,usage:cy.UPLOAD,refCount:1}),r}refCount(e){if(this.texData.has(e)){return this.texData.get(e).refCount}return 0}incRef(e){this.texData.get(e).refCount++}decRef(e){if(this.texData.has(e)){this.texData.get(e).refCount--}}move(e,t,n,r,a){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),"complex64"===r)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:r,values:t,usage:cy.UPLOAD,refCount:a})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){const t=this.texData.get(e),{values:n,dtype:r,complexTensorInfos:a,slice:s,shape:o,isPacked:i}=t;if(null!=s){let t;t=i?new mk(o,fk):new lk(o,fk);const n=this.runWebGLProgram(t,[{dataId:e,shape:o,dtype:r}],r),a=this.readSync(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===r)return n;const u=null!=this.activeTimers;let l,c;if(u&&(l=Dr()),"complex64"===r){c=eh(this.readSync(a.real.dataId),this.readSync(a.imag.dataId))}else c=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=Dr()-l),this.convertAndCacheOnCPU(e,c)}read(t){return e(this,null,function*(){if(this.pendingRead.has(t)){const e=this.pendingRead.get(t);return new Promise(t=>e.push(t))}const e=this.texData.get(t),{values:n,shape:r,slice:a,dtype:s,complexTensorInfos:o,isPacked:i}=e;if(null!=a){let e;e=i?new mk(r,fk):new lk(r,fk);const n=this.runWebGLProgram(e,[{dataId:t,shape:r,dtype:s}],s),a=this.read(n.dataId);return this.disposeIntermediateTensorInfo(n),a}if(null!=n)return this.convertAndCacheOnCPU(t);if(W().getBool("DEBUG")&&!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===W().getNumber("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u,l,c=null;if("complex64"!==s&&W().get("WEBGL_BUFFER_SUPPORTED")){u=this.decode(t);const e=this.texData.get(u.dataId);c=this.gpgpu.createBufferFromTexture(e.texture.texture,...my(r))}if(this.pendingRead.set(t,[]),"complex64"!==s&&(yield this.gpgpu.createAndWaitForFence()),"complex64"===s){const e=yield Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]);l=eh(e[0],e[1])}else if(null==c)l=this.getValuesFromTexture(t);else{const e=d(r);l=this.gpgpu.downloadFloat32MatrixFromBuffer(c,e)}if(null!=u&&this.disposeIntermediateTensorInfo(u),null!=c){const e=this.gpgpu.gl;by(e,()=>e.deleteBuffer(c))}const p=this.convertAndCacheOnCPU(t,l),h=this.pendingRead.get(t);return this.pendingRead.delete(t),h.forEach(e=>e(p)),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)&&Ba().removeDataId(t,this),this.pendingDeletes--),p})}readToGPU(e,t={}){const n=this.texData.get(e),{values:r,shape:a,slice:s,dtype:o,isPacked:i,texture:u}=n;if("complex64"===o)throw new Error("Does not support reading texture for complex64 dtype.");if(null!=s){let n;n=i?new mk(a,fk):new lk(a,fk);const r=this.runWebGLProgram(n,[{dataId:e,shape:a,dtype:o}],o),s=this.readToGPU(r,t);return this.disposeIntermediateTensorInfo(r),s}if(null==u)throw null!=r?new Error("Data is not on GPU but on CPU."):new Error("There is no data on GPU or CPU.");const l=this.decode(e,t.customTexShape),c=Ba().makeTensorFromTensorInfo(l),d=this.texData.get(l.dataId);return Object.assign({tensorRef:c},d.texture)}bufferSync(e){const t=this.readSync(e.dataId);if("string"===e.dtype)try{const n=t.map(e=>Pr(e));return Ls(e.shape,e.dtype,n)}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return Ls(e.shape,e.dtype,t)}checkNumericalProblems(e){if(null!=e)for(let t=0;t<e.length;t++){const n=e[t];if(!xy(n)){if(W().getBool("WEBGL_RENDER_FLOAT32_CAPABLE"))throw Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);throw Error(`The value ${n} cannot be represented on this device.`)}}}getValuesFromTexture(e){const{shape:t,dtype:n,isPacked:r}=this.texData.get(e),a=d(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){const n=this.decode(e),r=this.texData.get(n.dataId),s=this.gpgpu.downloadMatrixFromPackedTexture(r.texture.texture,...my(t)).subarray(0,a);return this.disposeIntermediateTensorInfo(n),s}const s=W().getBool("WEBGL_PACK")&&!0===r,o=s?Ay(t):t,i=s?new yb(o):new gb(o),u=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),c=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture,l.texShape[0],l.texShape[1]).subarray(0,a);return this.disposeIntermediateTensorInfo(u),c}timerAvailable(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}time(t){const n=this.activeTimers,r=[];let a=!1;null==this.programTimersStack?(this.programTimersStack=r,a=!0):this.activeTimers.push(r),this.activeTimers=r,t();const s=Br(this.activeTimers.map(e=>e.query)).filter(e=>null!=e),o=Br(this.activeTimers.map(e=>e.name)).filter(e=>null!=e);this.activeTimers=n,a&&(this.programTimersStack=null);const i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(()=>e(this,null,function*(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){const e=yield Promise.all(s);i.kernelMs=function(e){let t=0;for(let n=0;n<e.length;n++)t+=e[n];return t}(e),i.getExtraProfileInfo=()=>e.map((e,t)=>({name:o[t],ms:e})).map(e=>`${e.name}: ${e.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}))()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:Dr(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=Dr(),e)}getQueryTime(t){return e(this,null,function*(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(t);const e=t;return e.endMs-e.startMs})}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);const{complexTensorInfos:n}=this.texData.get(e);return null!=n&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){const{texture:t,dtype:n,texShape:r,usage:a,isPacked:s,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,u=this.dataRefCount.get(i);u>1?this.dataRefCount.set(i,u-1):(this.dataRefCount.delete(i),null!=t&&(this.numBytesInGPU-=this.computeBytes(r,n),this.textureManager.releaseTexture(t,r,a,s)));const l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=xk){return W().getBool("WEBGL_CPU_FORWARD")&&e.every(e=>null==this.texData.get(e.dataId).texture&&d(e.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){ar("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");const t=e.dataSync();return yk(e.shape,t)}packedUnaryOp(e,t,n){const r=new mk(e.shape,t),a=this.compileAndRun(r,[e],n);return Ba().makeTensorFromTensorInfo(a)}abs(e){if(this.shouldExecuteOnCPU([e])&&"complex64"!==e.dtype){const t=Bw(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,t)}if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,dk,e.dtype);const t=new lk(e.shape,dk),n=this.compileAndRun(t,[e]);return Ba().makeTensorFromTensorInfo(n)}makeTensorInfo(e,t,n){let r;if("string"===t&&null!=n&&n.length>0&&N(n[0])){const a=n.map(e=>Mr(e));r=this.write(a,e,t)}else r=this.write(n,e,t);return this.texData.get(r).usage=null,{dataId:r,shape:e,dtype:t}}makeOutput(e,t,n){return Ba().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,n),this)}unpackTensor(e){const t=new gk(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){const t=new rk(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){const n=[Ry(e.shape),..._y(e.shape)],r={dtype:e.dtype,shape:n,dataId:e.dataId},a=[Ry(t),..._y(t)],s=new ak(a,n),o=[n],i=this.runWebGLProgram(s,[r],e.dtype,o,!0);return{dataId:i.dataId,shape:t,dtype:i.dtype}}decode(e,t){const n=this.texData.get(e),{isPacked:r,shape:a,dtype:s}=n;if(null!=t){u(d(a)<=t[0]*t[1]*4,()=>"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.")}const o=Ay(a);let i;i=r?new mb(o):new fb(o);const l=[null!=t?t:my(o)];return{dtype:s,shape:a,dataId:this.runWebGLProgram(i,[{shape:o,dtype:s,dataId:e}],s,l,!0,t).dataId}}runWebGLProgram(e,t,n,r,a=!1,s){const o=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(o.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===uy.DENSE){const t=null!=s?s:my(e.outputShape);i.texShape=t.map(e=>2*e)}if(null!=e.outTexUsage&&(i.usage=e.outTexUsage),0===d(o.shape))return i.values=v(o.dtype,0),o;const u=[],l=t.map(t=>{if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let n=this.texData.get(t.dataId);if(null==n.texture){if(!e.packedInputs&&d(t.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:n.values};e.packedInputs&&(n.isPacked=!0,n.shape=t.shape)}if(this.uploadToGPU(t.dataId),!!n.isPacked!==!!e.packedInputs)t=n.isPacked?this.unpackTensor(t):this.packTensor(t),u.push(t),n=this.texData.get(t.dataId);else if(n.isPacked&&!Fy(n.shape,t.shape)){const e=t,r=t.shape;t.shape=n.shape,t=this.packedReshape(t,r),u.push(t),n=this.texData.get(t.dataId),e.shape=r}return{shape:t.shape,texData:n,isUniform:!1}});this.uploadToGPU(o.dataId);const c={shape:o.shape,texData:i,isUniform:!1},h=function(e,t,n){let r="";t.concat(n).forEach(t=>{const a=null!=t.texData&&null!=t.texData.slice&&t.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!t.isUniform){const s=t.texData.texShape,{useSqueezeShape:o,uniformShape:i,keptDims:u}=ib(e.packedInputs,t.shape,s);let l="",c="",h="";if(1===i.length&&e.packedInputs){const e=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)];l=`${e[0]>1}_${e[1]>1}`}else if(2!==i.length||e.packedInputs){if(i.length>2&&!e.packedInputs){const e=$(i);h=`${e[0]===s[1]}_${e[e.length-1]===s[1]}`}}else c=`${i[0]>1}_${i[1]>1}`;const f=t.shape.length,m=2===i.length&&p(t.shape,s),g=1===d(t.shape),y=ui(t.shape,n.shape),b=!e.packedInputs&&f===n.shape.length&&p(s,n.texData.texShape),x=e.packedInputs||i.length>2?"":`${s[0]>1}_${s[1]>1}`;r+=`${f}_${b}_${o?u:""}_${i.length}_${g}_${y}_${m}_${l}_${c}_${h}_${x}_${a}`}else{const e=t.isUniform?"uniform":t.texData.texShape;r+=`${t.shape}_${e}_${a}`}});const a=e.userCode;let s=e.constructor.name;return s+="_"+r+"_"+a+`${W().getNumber("WEBGL_VERSION")}`,s}(e,l,c),f=this.getAndSaveBinary(h,()=>cb(this.gpgpu,e,l,c)),m=null!=this.activeTimers;let g;m&&(g=this.startTimer()),W().get("ENGINE_COMPILE_ONLY")||function(e,t,n,r,a){t.program.enableShapeUniforms||(pb(t.inShapeInfos,n),pb([t.outShapeInfo],[r]));const s=r.texData.texture,o=r.texData.texShape;r.texData.isPacked?e.setOutputPackedMatrixTexture(s.texture,o[0],o[1]):e.setOutputMatrixTexture(s.texture,o[0],o[1]),e.setProgram(t.webGLProgram),e.bindVertexArray(t.webGLProgram.vao),1===W().getNumber("WEBGL_VERSION")&&null!==t.infLoc&&e.gl.uniform1f(t.infLoc,1/0),null!==t.nanLoc&&e.gl.uniform1f(t.nanLoc,NaN);for(let u=0;u<n.length;++u){const r=n[u],{uniform:a,offset:s,shape:o,texShape:i}=t.variablesLocations[u];if(o){const{uniformShape:n}=ib(t.program.packedInputs,r.shape,r.texData.texShape);switch(n.length){case 1:e.gl.uniform1iv(o,new Int32Array(n));break;case 2:e.gl.uniform2iv(o,new Int32Array(n));break;case 3:e.gl.uniform3iv(o,new Int32Array(n));break;case 4:e.gl.uniform4iv(o,new Int32Array(n))}}if(i&&e.gl.uniform2i(i,r.texData.texShape[0],r.texData.texShape[1]),null!=a)if(r.isUniform)if(d(r.shape)<2)e.gl.uniform1f(a,r.uniformValues[0]);else{let t=r.uniformValues;t instanceof Float32Array||(t=new Float32Array(t)),e.gl.uniform1fv(a,t)}else null!=r.texData.slice&&null!=s&&e.gl.uniform1i(s,r.texData.slice.flatOffset),e.setInputMatrixTexture(r.texData.texture.texture,a,u)}const i=t.outShapeLocation;if(i)switch(r.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(r.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(r.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(r.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(r.shape))}if(t.outShapeStridesLocation){const n=$(r.shape);switch(r.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(n));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(n));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(n))}}if(t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,r.texData.texShape[0],r.texData.texShape[1]),t.program.customUniforms&&a)for(let u=0;u<t.program.customUniforms.length;++u){const n=t.program.customUniforms[u],r=t.customUniformLocations[u],s=a[u];if("float"===n.type)e.gl.uniform1fv(r,s);else if("vec2"===n.type)e.gl.uniform2fv(r,s);else if("vec3"===n.type)e.gl.uniform3fv(r,s);else if("vec4"===n.type)e.gl.uniform4fv(r,s);else if("int"===n.type)e.gl.uniform1iv(r,s);else if("ivec2"===n.type)e.gl.uniform2iv(r,s);else if("ivec3"===n.type)e.gl.uniform3iv(r,s);else{if("ivec4"!==n.type)throw Error(`uniform type ${n.type} is not supported yet.`);e.gl.uniform4iv(r,s)}}e.executeProgram()}(this.gpgpu,f,l,c,r),u.forEach(e=>this.disposeIntermediateTensorInfo(e)),m&&(g=this.endTimer(g),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(g)}));const y=W().getNumber("WEBGL_FLUSH_THRESHOLD");if(y>0){const e=Dr();e-this.lastGlFlushTime>y&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=e)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&!1===a){const e=this.unpackTensor(o);return this.disposeIntermediateTensorInfo(o),e}return o}compileAndRun(e,t,n,r,a=!1){n=n||t[0].dtype;return this.runWebGLProgram(e,t,n,r,a)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){if(!this.disposed){if(!W().getBool("IS_TEST")){Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]})}this.textureManager.dispose(),null!=this.canvas&&"undefined"!==typeof HTMLCanvasElement&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0}}floatPrecision(){return null==this.floatPrecisionValue&&(this.floatPrecisionValue=Va(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){const e=W().getBool("DEBUG");W().set("DEBUG",!1);const t=this.abs(_i(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return 32===this.floatPrecision()?1e-7:1e-4}uploadToGPU(e){const t=this.texData.get(e),{shape:n,dtype:r,values:a,texture:s,usage:i,isPacked:u}=t;if(null!=s)return;const l=null!=this.activeTimers;let c;l&&(c=Dr());let p=t.texShape;if(null==p&&(p=function(e,t=!1){let n=W().getNumber("WEBGL_MAX_TEXTURE_SIZE"),r=W().getNumber("WEBGL_MAX_SIZE_FOR_NARROW_TEXTURE");if(r===1/0&&W().getBool("WEBGL_AUTO_SQUARIFY_NARROW_TEXTURE_SHAPE")&&(r=n/2),t&&(n*=2,r*=2,1===(e=e.map((t,n)=>n>=e.length-2?o(e[n]):e[n])).length&&(e=[2,e[0]])),2!==e.length){const t=x(e);e=t.newShape}let a=d(e),s=null;e.length<=1&&a<=n?s=[1,a]:2===e.length&&e[0]<=n&&e[1]<=n?s=e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?s=[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?s=[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?s=[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n&&(s=[e[0],e[1]*e[2]*e[3]]);const i=null!=s&&Math.max(...s)>r&&Math.min(...s)<=(t?2:1)&&Math.min(...s)>0;if(null==s||i)if(t){const t=Ry(e);let n=2,r=2;e.length&&([n,r]=_y(e)),a=t*(n/2)*(r/2),s=f(a).map(e=>2*e)}else s=f(a);return s}(n,u),t.texShape=p),null!=a){const e=Ay(n);let s,o=p[1],i=p[0];const d=a instanceof Uint8Array||a instanceof Uint8ClampedArray;!u&&d||([o,i]=gy(p[0],p[1])),s=u?new vb(e,d):new xb(e,d);const h=d?[i,o]:p,f=this.makeTensorInfo(h,r),m=this.texData.get(f.dataId);m.usage=d?cy.PIXELS:cy.UPLOAD,m.texShape=h,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(f.dataId),o,i,a);const g=[[i,o]],y=!0,b=this.runWebGLProgram(s,[f],r,g,y),x=this.texData.get(b.dataId);t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,W().get("ENGINE_COMPILE_ONLY")?this.disposeData(b.dataId):(t.texture=x.texture,t.values=null,this.texData.delete(b.dataId)),this.disposeIntermediateTensorInfo(f),l&&(this.uploadWaitMs+=Dr()-c)}else{const e=this.acquireTexture(p,i,r,u);t.texture=e}}convertAndCacheOnCPU(e,t){const n=this.texData.get(e),{dtype:r}=n;return null!=t&&(n.values=function(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){const n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length);for(let t=0;t<n.length;++t)n[t]=Math.round(e[t]);return n}throw new Error(`Unknown dtype ${t}`)}(t,r)),n.values}acquireTexture(e,t,n,r){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>1024*this.numMBBeforeWarning*1024){const e=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${e} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,r)}computeBytes(e,t){return e[0]*e[1]*I(t)}checkCompileCompletion(){for(const[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}checkCompileCompletionAsync(){return e(this,null,function*(){const e=[];if(this.gpgpu.parallelCompilationExtension){for(const[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}for(const[,t]of Object.entries(this.binaryCache)){const n=new Promise(e=>{try{this.checkCompletion_(t),e(!0)}catch(n){throw n}});e.push(n)}return Promise.all(e)})}checkCompletionAsync_(t){return e(this,null,function*(){return this.gpgpu.gl.getProgramParameter(t.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(t):(yield Rp(),this.checkCompletionAsync_(t))})}checkCompletion_(e){if(!1===this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)){if(console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),!1===this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS))throw ky(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error("Failed to compile fragment shader.");throw new Error("Failed to link vertex and fragment shaders.")}return!0}getUniformLocations(){for(const e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);const{variablesLocations:t,customUniformLocations:n,infLoc:r,nanLoc:a,outShapeLocation:s,outShapeStridesLocation:o,outTexShapeLocation:i}=db(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=n,e.infLoc=r,e.nanLoc=a,e.outShapeLocation=s,e.outShapeStridesLocation=o,e.outTexShapeLocation=i}}createTensorFromGPUData(e,t,n){e.channels=e.channels||"RGBA";const{texture:r,height:a,width:s,channels:o}=e,i=Ba().backend;if(!i.gpgpu.gl.isTexture(r))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 u=i.writeTexture(r,t,n,a,s,o);return Ba().makeTensorFromDataId(u,t,n,i)}}vk.nextDataId=0,Sa()&&Ha("webgl",()=>new vk,2);const wk="\n  if (isnan(a)) return a;\n  if (isnan(b)) return b;\n";class kk{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=ci(t,n),this.enableShapeUniforms=hb(this.outputShape.length),this.userCode=`\n      float binaryOperation(float a, float b) {\n        ${e}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `}}const Ik="\n  result.r = isNaN.r ? NAN : result.r;\n  result.g = isNaN.g ? NAN : result.g;\n  result.b = isNaN.b ? NAN : result.b;\n  result.a = isNaN.a ? NAN : result.a;\n";class Nk{constructor(e,t,n,r=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=ci(t,n);const a=this.outputShape.length;this.enableShapeUniforms=hb(a);let s="";if(r)if(0===a||1===d(this.outputShape))s="\n          result.y = 0.;\n          result.z = 0.;\n          result.w = 0.;\n        ";else{if(s=`\n          ${ob(a)} coords = getOutputCoords();\n        `,1===a)this.enableShapeUniforms?s+="\n            result.y = (coords + 1) >= outShape ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          ":s+=`\n            result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n            result.z = 0.;\n            result.w = 0.;\n          `;else{const e=nk("coords",a);this.enableShapeUniforms?s+=`\n            bool nextRowOutOfBounds =\n              (${e[a-2]} + 1) >= outShape[${a} - 2];\n            bool nextColOutOfBounds =\n              (${e[a-1]} + 1) >= outShape[${a} - 1];\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `:s+=`\n            bool nextRowOutOfBounds =\n              (${e[a-2]} + 1) >= ${this.outputShape[a-2]};\n            bool nextColOutOfBounds =\n              (${e[a-1]} + 1) >= ${this.outputShape[a-1]};\n            result.y = nextColOutOfBounds ? 0. : result.y;\n            result.z = nextRowOutOfBounds ? 0. : result.z;\n            result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n          `}}this.userCode=`\n      vec4 binaryOperation(vec4 a, vec4 b) {\n        ${e}\n      }\n\n      void main() {\n        vec4 a = getAAtOutCoords();\n        vec4 b = getBAtOutCoords();\n\n        vec4 result = binaryOperation(a, b);\n        ${s}\n\n        setOutput(result);\n      }\n    `}}function Sk(e){const{inputs:t,backend:n}=e,{x:r}=t;return n.incRef(r.dataId),{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}const Tk={kernelName:it,backendName:"webgl",kernelFunc:Sk};function Ck(e){const{inputs:t,backend:n}=e,{real:r,imag:a}=t,s=n.makeTensorInfo(r.shape,"complex64"),o=n.texData.get(s.dataId),i=Sk({inputs:{x:r},backend:n}),u=Sk({inputs:{x:a},backend:n});return o.complexTensorInfos={real:i,imag:u},s}const $k={kernelName:be,backendName:"webgl",kernelFunc:Ck},Ek="return (a < 0.) ? b * a : a;",Rk="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";const _k={kernelName:ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{alpha:s}=r,o=n.makeTensorInfo([],"float32",Or(s,"float32")),i=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nk(Rk,a.shape,o.shape):new kk(Ek,a.shape,o.shape),u=n.runWebGLProgram(i,[a,o],"float32");return n.disposeIntermediateTensorInfo(o),u}},Ak="return (a < 0.) ? b * a : a;",Ok="\n  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n";const Fk={kernelName:Kt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r,alpha:a}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nk(Ok,r.shape,a.shape):new kk(Ak,r.shape,a.shape);return n.runWebGLProgram(s,[r,a],"float32")}},Dk="if (isnan(x)) return x;";function Mk({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:r}){return({inputs:a,backend:s})=>{const{x:o}=a,i=s,u=r||o.dtype;if(i.shouldExecuteOnCPU([o])&&null!=n){const e=i.texData.get(o.dataId),t=n(e.values,u);return i.makeTensorInfo(o.shape,u,t)}let l;return l=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&null!=t?new mk(o.shape,t):new lk(o.shape,e),i.runWebGLProgram(l,[o],u)}}function Pk({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:r=!1,cpuKernelImpl:a,dtype:s}){return({inputs:o,backend:i})=>{const{a:u,b:l}=o,c=i;if(r&&"complex64"===u.dtype){const t=c.texData.get(u.dataId),n=c.texData.get(l.dataId),[r,a]=[[t.complexTensorInfos.real,n.complexTensorInfos.real],[t.complexTensorInfos.imag,n.complexTensorInfos.imag]].map(t=>{const[n,r]=t,a={dataId:n.dataId,dtype:n.dtype,shape:u.shape},s={dataId:r.dataId,dtype:r.dtype,shape:l.shape},o=new kk(e,u.shape,l.shape);return c.runWebGLProgram(o,[a,s],da(n.dtype,r.dtype))}),s=Ck({inputs:{real:r,imag:a},backend:c});return c.disposeIntermediateTensorInfo(r),c.disposeIntermediateTensorInfo(a),s}const d=s||da(u.dtype,l.dtype);if(("string"===u.dtype||"string"===l.dtype||c.shouldExecuteOnCPU([u,l]))&&null!=a){const e=c.texData.get(u.dataId).values,t=c.texData.get(l.dataId).values,n="string"===u.dtype?Rh(e):e,r="string"===u.dtype?Rh(t):t,[s,o]=a(u.shape,l.shape,n,r,d),i=c.makeTensorInfo(o,d);return c.texData.get(i.dataId).values=s,i}let p;return p=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&null!=t?new Nk(t,u.shape,l.shape,n):new kk(e,u.shape,l.shape),c.runWebGLProgram(p,[u,l],d)}}function Lk(e,t=!1){if("linear"===e)return"return x;";if("relu"===e)return t?"\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n":pk;if("elu"===e)return t?"\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n":"return (x >= 0.0) ? x : (exp(x) - 1.0);";if("relu6"===e)return t?"\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n":hk;if("prelu"===e)return t?Ok:Ak;if("leakyrelu"===e)return t?Rk:Ek;if("sigmoid"===e)return"return 1.0 / (1.0 + exp(-1.0 * x));";throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}class Bk{constructor(e,t,n,r=!1,a=!1,s=!1,o=null,i=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=hb(this.outputShape.length);const l=r?e[1]:e[2],c=Math.ceil(l/2),d=r?"i * 2, rc.y":"rc.y, i * 2",p=a?"rc.z, i * 2":"i * 2, rc.z",h=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=a?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];let m="",g="";o&&(m=i?`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${o}\n        }`:u?`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${o}\n        }`:`vec4 activation(vec4 x) {\n          ${o}\n        }`,g="result = activation(result);");const y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let b="rc.x",x="rc.x";e[0]<t[0]?b=`imod(rc.x, ${e[0]})`:t[0]<e[0]&&(x=`imod(rc.x, ${t[0]})`),this.userCode=`\n      ${m}\n      // Don't use uniform for sharedDimensionPacked for performance.\n      const float sharedDimension = ${c}.0;\n\n      vec4 dot2x2ARowBCol(ivec3 rc) {\n        vec4 result = vec4(0);\n        int batchA = ${b};\n        int batchB = ${x};\n        for (int i = 0; i < ${c}; i++) {\n          vec4 a = getMatrixA(batchA, ${d});\n          vec4 b = getMatrixB(batchB, ${p});\n\n          // These swizzled products need to be separately added.\n          // See: https://github.com/tensorflow/tfjs/issues/1735\n          result += (${h[0]} * ${f[0]});\n          result += (${h[1]} * ${f[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n        vec4 result = dot2x2ARowBCol(rc);\n\n        ${y}\n\n        ${g}\n\n        setOutput(result);\n      }\n    `}}const Vk="return areal * breal - aimag * bimag;",Wk="return areal * bimag + aimag * breal;";class zk{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=ci(t,n),this.userCode=`\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${e}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `}}const Uk="return a * b;";function Gk(e){const{inputs:t,backend:n}=e,{a:r,b:a}=t,s=da(r.dtype,a.dtype);if("complex64"===r.dtype){const e=n.texData.get(r.dataId),t=n.texData.get(a.dataId),s=new zk(Vk,r.shape,a.shape),o=new zk(Wk,r.shape,a.shape),i=[{dataId:e.complexTensorInfos.real.dataId,dtype:e.complexTensorInfos.real.dtype,shape:r.shape},{dataId:e.complexTensorInfos.imag.dataId,dtype:e.complexTensorInfos.imag.dtype,shape:r.shape},{dataId:t.complexTensorInfos.real.dataId,dtype:t.complexTensorInfos.real.dtype,shape:a.shape},{dataId:t.complexTensorInfos.imag.dataId,dtype:t.complexTensorInfos.imag.dtype,shape:a.shape}],u=n.runWebGLProgram(s,i,"float32"),l=n.runWebGLProgram(o,i,"float32"),c=Ck({inputs:{real:u,imag:l},backend:n});return n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(l),c}if(n.shouldExecuteOnCPU([r,a])){const e=n.texData.get(r.dataId),t=n.texData.get(a.dataId),[o,i]=$w(r.shape,a.shape,e.values,t.values,s),u=n.makeTensorInfo(i,s);return n.texData.get(u.dataId).values=o,u}let o;return o=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nk(Uk,r.shape,a.shape):new kk(Uk,r.shape,a.shape),n.runWebGLProgram(o,[r,a],s)}const Hk={kernelName:Pt,backendName:"webgl",kernelFunc:Gk};function jk(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{shape:s}=r,o=n,i=d(a.shape),l=y(s,i),c=d(l);u(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${a.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);const p=o.texData.get(a.dataId);return!p.isPacked||Fy(a.shape,l)||null!==p.texture&&Fy(p.shape,l)?(o.incRef(a.dataId),{dataId:a.dataId,shape:l,dtype:a.dtype}):function(e,t,n){const r=[Ry(e.shape),..._y(e.shape)],a={dtype:e.dtype,shape:r,dataId:e.dataId},s=[Ry(t),..._y(t)],o=new ak(s,r),i=[r],u=n.runWebGLProgram(o,[a],e.dtype,i,!0);return{dataId:u.dataId,shape:t,dtype:u.dtype}}(a,l,o)}const qk={kernelName:rn,backendName:"webgl",kernelFunc:jk};class Kk{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];const o=4*Math.floor(n/4),i=n%4;let u="sumValue += dot(values, ones);";if(null!=t){const e=1/t;u=`sumValue += dot(values * ${h(e)?e.toPrecision(2):e}, ones);`}let l="";a%n>0&&(l=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return 0.0;\n        }\n      `),this.userCode=`\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${l}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${o}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${u}\n        }\n\n        int inIdx = inOffset + ${o};\n        if (${1===i}) {\n          vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n          ${u}\n        } else if (${2===i}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1), 0.0, 0.0);\n\n          ${u}\n        } else if (${3===i}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2), 0.0);\n\n          ${u}\n        }\n        setOutput(sumValue);\n      }\n    `}}class Xk{constructor(e,t){this.variableNames=["x"];const{windowSize:n,batchSize:r,inSize:a,outSize:s}=e;this.outputShape=[r,s];let o="0.0",i="";"prod"===t?o="1.0":"min"===t?(o="1.0 / 1e-20",i="min"):"max"===t&&(o="-1.0 / 1e-20",i="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");const l=4*Math.floor(n/4),c=n%4;let d=`\n      if (${"sum"===t}) {\n        sumValue += dot(values, ones);\n      } else if (${"prod"===t}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${i}(values, minMaxValue);\n        if (${"min"===t} || ${"max"===t}) {\n          minMaxValue = ${i}(values, minMaxValue);\n          bvec4 isNaN = isnan(values);\n          if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n            minMaxValue = vec4(NAN);\n          }\n        }\n      }\n    `,p="vec4";"all"===t?(o="1.0",d="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",p="bvec4"):"any"===t&&(o="0.0",d="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",p="bvec4");let h="";a%n>0&&(h=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return initializationValue;\n        }\n      `),this.userCode=`\n      const float initializationValue = ${o};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${h}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${n};\n\n        vec4 minMaxValue = vec4(${o});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${l}; i += 4) {\n          int inIdx = inOffset + i;\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${d}\n        }\n\n        int inIdx = inOffset + ${l};\n        if (${1===c}) {\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${d}\n        } else if (${2===c}) {\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${d}\n        } else if (${3===c}) {\n          ${p} values = ${p}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${d}\n        }\n        setOutput(${u});\n      }\n    `}}function Yk(e,t,n,r){const a=function(e){const t=[];for(;0===t.length||1!==t[t.length-1].outSize;){const n=t.length?t[t.length-1].outSize:e[1],r=Bp(n);t.push({inSize:n,windowSize:r,outSize:Math.ceil(n/r)})}return t}(e.shape);let s=e;for(let o=0;o<a.length;o++){const{inSize:i,windowSize:u,outSize:l}=a[o];let c,d;c="mean"===n?0===o?new Kk({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},i):new Kk({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l}):new Xk({windowSize:u,inSize:i,batchSize:e.shape[0],outSize:l},n),d=s,s=r.runWebGLProgram(c,[s],t),d.dataId!==e.dataId&&r.disposeIntermediateTensorInfo(d)}return s}class Qk{constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;const r=ob(this.rank),a=function(e){const t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t);for(let a=0;a<e.length;a++)r[e[a]]=n[a];return r.join()}(t);this.userCode=`\n    void main() {\n      ${r} resRC = getOutputCoords();\n      setOutput(getA(${a}));\n    }\n    `}}class Zk{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;const n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);const r=ob(this.rank),a=tk("rc",this.rank),s=new Array(this.rank);for(let l=0;l<t.length;l++)s[t[l]]=a[l];const o=`vec2(${s.slice(-2).join()})`,i=`++${a[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${s.join()}), ${o})`;this.userCode=`\n    void main() {\n      ${r} rc = getOutputCoords();\n      vec4 result = vec4(0.);\n      result[0] = ${u};\n      if(${i}) {\n        result[1] = ${u};\n      }\n      --${a[this.rank-1]};\n      if(++${a[this.rank-2]} < ${n[this.rank-2]}) {\n        result[2] = ${u};\n        if(${i}) {\n          result[3] = ${u};\n        }\n      }\n      setOutput(result);\n    }\n    `}}function Jk(e,t,n){const r=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Zk(e.shape,t):new Qk(e.shape,t);return n.runWebGLProgram(r,[e],e.dtype)}function eI(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:o}=r;return function(e,t,n,r){const a=t,s=e.shape.length,o=b(a,e.shape);let i=o;const u=Si(i,s),l=null!=u;let c=e;l&&(c=Jk(e,u,r),i=Ci(i.length,s)),Ni("sum",i,s);const[p,h]=ki(c.shape,i);let f=p;n&&(f=Ii(p,o));const m=d(h),g=jk({inputs:{x:c},attrs:{shape:[d(e.shape)/m,m]},backend:r}),y=Yk(g,pa(e.dtype),"sum",r),x=jk({inputs:{x:y},attrs:{shape:f},backend:r});return r.disposeIntermediateTensorInfo(g),r.disposeIntermediateTensorInfo(y),l&&r.disposeIntermediateTensorInfo(c),x}(a,s,o,n)}const tI={kernelName:Sn,backendName:"webgl",kernelFunc:eI};function nI(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{perm:s}=r,o=n,i=a.shape.length,u=new Array(i);for(let c=0;c<u.length;c++)u[c]=a.shape[s[c]];let l;if(o.shouldExecuteOnCPU([a])){const e=o.texData.get(a.dataId).values,t=Jw(e,a.shape,a.dtype,s,u);l=o.makeTensorInfo(u,a.dtype);o.texData.get(l.dataId).values=t}else l=Jk(a,s,o);return l}const rI={kernelName:qn,backendName:"webgl",kernelFunc:nI};function aI({a:e,b:t,transposeA:n,transposeB:r,backend:a,bias:s=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){const c=e.shape.length,p=t.shape.length,h=n?e.shape[c-2]:e.shape[c-1],f=r?t.shape[p-1]:t.shape[p-2],m=n?e.shape[c-1]:e.shape[c-2],g=r?t.shape[p-2]:t.shape[p-1],y=e.shape.slice(0,-2),b=t.shape.slice(0,-2),x=d(y),v=d(b),w=ci(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([m,g]);u(h===f,()=>`Error in matMul: inner shapes (${h}) and (${f}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${r} must match.`);const k=n?[x,h,m]:[x,m,h],I=r?[v,g,f]:[v,f,g],N=jk({inputs:{x:e},backend:a,attrs:{shape:k}}),S=jk({inputs:{x:t},backend:a,attrs:{shape:I}}),T=[N,S],C=Math.max(x,v),$=n?N.shape[1]:N.shape[2],E=null!=s,R=null!=o,_="leakyrelu"===l,A=null!=l?Lk(l,!0):null;let O;if((1===m||1===g)&&$>1e3&&!1===(E||R||_||null!=A)){let e=N,t=S;n&&(e=nI({inputs:{x:N},backend:a,attrs:{perm:[0,2,1]}}),T.push(e)),r&&(t=nI({inputs:{x:S},backend:a,attrs:{perm:[0,2,1]}}),T.push(t));const s=1===g;let o=e;1!==g&&(o=jk({inputs:{x:e},backend:a,attrs:{shape:[C,$,1]}}),T.push(o));const i=1===g?2:1;let u=t;s&&(u=jk({inputs:{x:t},backend:a,attrs:{shape:[C,1,$]}}),T.push(u));const l=Gk({inputs:{a:o,b:u},backend:a});O=eI({inputs:{x:l},backend:a,attrs:{axis:i,keepDims:!0}}),T.push(l)}else{const u=da(e.dtype,t.dtype),l=new Bk(k,I,[C,m,g],n,r,E,A,R,_),c=[N,S];if(null!=s&&c.push(s),R&&c.push(o),_){const e=a.makeTensorInfo([],"float32",Or(i,"float32"));c.push(e),T.push(e)}O=a.runWebGLProgram(l,c,u)}const F=jk({inputs:{x:O},backend:a,attrs:{shape:w}});T.push(O);for(const u of T)a.disposeIntermediateTensorInfo(u);return F}const sI={kernelName:tr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a:a,b:s,bias:o,preluActivationWeights:i}=t,{transposeA:u,transposeB:l,activation:c,leakyreluAlpha:d}=r;return aI({a:a,b:s,transposeA:u,transposeB:l,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:c})}},oI="return abs(x);";const iI={kernelName:j,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])&&"complex64"!==r.dtype){const e=n.texData.get(r.dataId),t=Bw(e.values);return n.makeTensorInfo(r.shape,r.dtype,t)}let a;return a=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new mk(r.shape,oI):new lk(r.shape,oI),n.runWebGLProgram(a,[r],r.dtype)}},uI=Mk({opSnippet:ck+"\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return acos(x);\n"}),lI={kernelName:q,backendName:"webgl",kernelFunc:uI},cI=Mk({opSnippet:ck+"\n  if (x < 1.0) return NAN;\nreturn log(x + sqrt(x * x - 1.0));"}),dI={kernelName:K,backendName:"webgl",kernelFunc:cI},pI="return a + b;",hI=Pk({opSnippet:pI,packedOpSnippet:pI,supportsComplex:!0,cpuKernelImpl:ow}),fI={kernelName:X,backendName:"webgl",kernelFunc:hI};class mI{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((e,t)=>`T${t}`);const n=[];this.variableNames.forEach(e=>{n.push(`float v${e} = get${e}AtOutCoords();`)});const r=this.variableNames.map(e=>`v${e}`).join(" + ");this.userCode=`\n      void main() {\n        ${n.join("\n        ")}\n\n        float result = ${r};\n        setOutput(result);\n      }\n    `}}class gI{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((e,t)=>`T${t}`);const n=[];this.variableNames.forEach(e=>{n.push(`vec4 v${e} = get${e}AtOutCoords();`)});const r=this.variableNames.map(e=>`v${e}`).join(" + ");this.userCode=`\n      void main() {\n        ${n.join("\n        ")}\n\n        vec4 result = ${r};\n        setOutput(result);\n      }\n    `}}const yI={kernelName:Y,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,a=n;if(1===a.length)return Sk({inputs:{x:a[0]},backend:r});if(a.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){const t=Math.floor(a.length/2),n=e({inputs:a.slice(0,t),backend:r}),s=e({inputs:a.slice(t),backend:r});return e({inputs:[n,s],backend:r})}const s=a.map(e=>e.dtype).reduce((e,t)=>da(e,t)),o=a.map(e=>e.shape),i=W().getBool("WEBGL_PACK")?new gI(a[0].shape,o):new mI(a[0].shape,o);return r.runWebGLProgram(i,a,s)}};const bI={kernelName:Q,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:o}=r,i=a.shape.length,u=b(s,a.shape);let l=u;const c=Si(l,i);let p=a;null!=c&&(p=nI({inputs:{x:a},backend:n,attrs:{perm:c}}),l=Ci(l.length,i)),Ni("all",l,i);const[h,f]=ki(p.shape,l),m=jk({inputs:{x:p},backend:n,attrs:{shape:[-1,d(f)]}}),g=Yk(m,m.dtype,"all",n);let y;if(o){y=jk({inputs:{x:g},backend:n,attrs:{shape:Ii(h,u)}})}else y=jk({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(p),y}};const xI={kernelName:Z,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:o}=r,i=a.shape.length,u=b(s,a.shape);let l=u;const c=Si(l,i);let p=a;null!=c&&(p=nI({inputs:{x:a},backend:n,attrs:{perm:c}}),l=Ci(l.length,i)),Ni("any",l,i);const[h,f]=ki(p.shape,l),m=jk({inputs:{x:p},backend:n,attrs:{shape:[-1,d(f)]}}),g=Yk(m,m.dtype,"any",n);let y;if(o){y=jk({inputs:{x:g},backend:n,attrs:{shape:Ii(h,u)}})}else y=jk({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(p),y}};class vI{constructor(e,t,n){this.variableNames=["A"];const{windowSize:r,batchSize:a,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[a,s];const o="max"===t?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${r};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${r}; i++) {\n          int inIdx = ${i};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${o} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `}}class wI{constructor(e,t,n,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,u(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);const a=e[e.length-1],s=Math.ceil(a/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),r||this.variableNames.push("bestIndicesA");const o=this.outputShape,i=o.length,l=ob(i),c=nk("coords",i);let d,p;if(1===s){p=i+1;const e=ob(p);d=`\n        ${e} sourceLocR = ${e}(${c.join()}, 0);\n        ++${c[i-1]};\n        ${e} sourceLocG = ${e}(${c.join()}, 0);\n        ++${c[i-2]};\n        ${e} sourceLocA = ${e}(${c.join()}, 0);\n        --${c[i-1]};\n        ${e} sourceLocB = ${e}(${c.join()}, 0);\n        --${c[i-2]};`}else p=i,d=`\n        ${l} sourceLocR = coords;\n        ++${c[i-1]};\n        ${l} sourceLocG = coords;\n        ++${c[i-2]};\n        ${l} sourceLocA = coords;\n        --${c[i-1]};\n        ${l} sourceLocB = coords;\n        --${c[i-2]};`;const h=["x","y","z","w","u","v"].slice(0,p),f="."+h[p-1],m=h.map(e=>"int "+e),g=nk("sourceLocR",p-1).concat("inIdx.r"),y=nk("sourceLocG",p-1).concat("inIdx.g"),b=nk("sourceLocB",p-1).concat("inIdx.b"),x=nk("sourceLocA",p-1).concat("inIdx.a"),v="max"===n?"greaterThan":"lessThan",w=r?"":`\n          inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),\n                             getBestIndicesAChannel(${y.join()}),\n                             getBestIndicesAChannel(${b.join()}),\n                             getBestIndicesAChannel(${x.join()})));`,k=`vec4(\n            getAChannel(${g.join()}),\n            hasNextCol ? getAChannel(${y.join()}) : 0.,\n            hasNextRow ? getAChannel(${b.join()}) : 0.,\n            hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,I=r?"":`\n      float getBestIndicesAChannel(${m.join()}) {\n        return getChannel(getBestIndicesA(${h.join()}),\n                                          vec2(${h.slice(-2).join()}));\n      }`;this.userCode=`\n      float getAChannel(${m.join()}) {\n        return getChannel(getA(${h.join()}),\n                               vec2(${h.slice(-2).join()}));\n      }\n      ${I}\n      void main() {\n        ${l} coords = getOutputCoords();\n        bool hasNextCol = ${c[i-1]} < ${o[i-1]-1};\n        bool hasNextRow = ${c[i-2]} < ${o[i-2]-1};\n        ${d}\n        ivec4 srcIdx = ivec4(sourceLocR${f}, sourceLocG${f},\n          sourceLocB${f}, sourceLocA${f}) * ${t};\n        ivec4 inIdx = srcIdx;\n        vec4 bestIndex = vec4(inIdx);\n        vec4 bestValue = ${k};\n\n        for (int i = 0; i < ${t}; i++) {\n          inIdx = srcIdx;\n          ${w}\n          vec4 candidate = ${k};\n          bvec4 nan = isnan(candidate);\n          bvec4 replace = bvec4(\n            vec4(${v}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));\n\n          bestValue = vec4(replace.x  ? candidate.x : bestValue.x,\n                           replace.y  ? candidate.y : bestValue.y,\n                           replace.z  ? candidate.z : bestValue.z,\n                           replace.w  ? candidate.w : bestValue.w);\n          bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));\n          srcIdx++;\n        }\n        setOutput(bestIndex);\n      }\n    `}}function kI(e,t,n,r=null){let a=t.shape[0],s=t.shape[1];null!=r&&(a=r.shape[0],s=r.shape[1]);const o=Bp(s),i={windowSize:o,inSize:s,batchSize:a,outSize:Math.ceil(s/o)},u=new vI(i,n,null==r),l=[t];null!=r&&l.push(r);const c=e.runWebGLProgram(u,l,"int32");if(1===c.shape[1])return c;const d=kI(e,t,n,c);return e.disposeIntermediateTensorInfo(c),d}function II(e,t,n,r=null){const a=null!=r?r.shape:t.shape,s=Bp(a[a.length-1]),o=new wI(a,s,n,null==r),i=null==r?[t]:[t,r],u=e.runWebGLProgram(o,i,"int32");if(u.shape.length===t.shape.length){const r=II(e,t,n,u);return e.disposeIntermediateTensorInfo(u),r}return u}function NI(e,t,n,r){const a=[n];if(Ni("arg"+r.charAt(0).toUpperCase()+r.slice(1),a,t.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){const n=[],s=e.texData.get(t.dataId);let o=t;null!==s&&s.isPacked&&(o=e.unpackTensor(t),n.push(o));const[i,u]=ki(o.shape,a),l=d(u),c=jk({inputs:{x:o},backend:e,attrs:{shape:[-1,l]}});n.push(c);const p=kI(e,c,r);n.push(p);const h=jk({inputs:{x:p},backend:e,attrs:{shape:i}});return n.forEach(t=>e.disposeIntermediateTensorInfo(t)),h}return II(e,t,r)}const SI={kernelName:J,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;let o=b(s,a.shape);const i=Si(o,a.shape.length);let u=a;const l=[];null!=i&&(u=nI({inputs:{x:a},backend:n,attrs:{perm:i}}),l.push(u),o=Ci(o.length,u.shape.length)),Ni("argMax",[o[0]],u.shape.length);const c=NI(n,u,o[0],"max");return l.forEach(e=>n.disposeIntermediateTensorInfo(e)),c}};const TI={kernelName:ee,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s}=r;let o=b(s,a.shape);const i=Si(o,a.shape.length);let u=a;const l=[];null!=i&&(u=nI({inputs:{x:a},backend:n,attrs:{perm:i}}),l.push(u),o=Ci(o.length,u.shape.length)),Ni("argMin",[o[0]],u.shape.length);const c=NI(n,u,o[0],"min");return l.forEach(e=>n.disposeIntermediateTensorInfo(e)),c}},CI=Mk({opSnippet:ck+"\n  if (abs(x) > 1.) {\n    return NAN;\n  }\n  return asin(x);\n"}),$I={kernelName:te,backendName:"webgl",kernelFunc:CI},EI=Mk({opSnippet:ck+"return log(x + sqrt(x * x + 1.0));"}),RI={kernelName:ne,backendName:"webgl",kernelFunc:EI},_I=Mk({opSnippet:ck+"\n  return atan(x);\n"}),AI={kernelName:re,backendName:"webgl",kernelFunc:_I},OI=Pk({opSnippet:wk+"\n  return atan(a, b);\n",packedOpSnippet:"\n  vec4 result = atan(a, b);\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  "+Ik+"\n  return result;\n"}),FI={kernelName:se,backendName:"webgl",kernelFunc:OI},DI=Mk({opSnippet:ck+"\n  if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;"}),MI={kernelName:ae,backendName:"webgl",kernelFunc:DI};class PI{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const s=e.filterWidth,o=e.strideHeight,i=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;const f="avg"===t,m=`((batch  * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`;let y="0.0";if(f||(y="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n        const ivec2 strides = ivec2(${o}, ${i});\n        const ivec2 pads = ivec2(${p}, ${h});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${c};\n              wR += ${u}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${d};\n                wC += ${l}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${t} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = ${r?a?m:g:`wR * ${d} + wC`};\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `)}let b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(b="avgValue / max(count, 1.0)");const x=4*Math.floor(s/4),v=s%4,w=`\n      if (${f}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec2 strides = ivec2(${o}, ${i});\n      const ivec2 pads = ivec2(${p}, ${h});\n      const float initializationValue = ${y};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${y});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${c};\n            wR += ${u}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${x}; wC += 4) {\n            int xC = xCCorner + wC * ${l};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              getValue(batch, xR, xC + 3 * ${l}, d)\n            );\n\n            ${w}\n          }\n\n          int xC = xCCorner + ${x};\n          if (${1===v}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${w}\n          } else if (${2===v}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${w}\n          } else if (${3===v}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${l}, d),\n              getValue(batch, xR, xC + 2 * ${l}, d),\n              initializationValue\n            );\n\n            ${w}\n          }\n        }\n        setOutput(${b});\n      }\n    `}}class LI{constructor(e,t,n,r=!1,a=!1){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");const s=e.filterWidth,o=e.strideDepth,i=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,c=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;const b="avg"===t;let x="0.0";if(b||(x="-1.0 / 1e-20"),n){const t=">=";return void(this.userCode=`\n        const ivec3 strides =\n            ivec3(${o}, ${i}, ${u});\n        const ivec3 pads = ivec3(${m}, ${g}, ${y});\n\n        void main() {\n          ivec5 coords = getOutputCoords();\n          int batch = coords.x;\n          int ch = coords.u;\n\n          ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n          int xDCorner = xCorner.x;\n          int xRCorner = xCorner.y;\n          int xCCorner = xCorner.z;\n\n          // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n\n          for (int wD = 0; wD < ${p};\n              wD += ${l}) {\n            int xD = xDCorner + wD;\n\n            if (xD < 0 || xD >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int wR = 0; wR < ${h};\n                wR += ${c}) {\n              int xR = xRCorner + wR;\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int wC = 0; wC < ${f};\n                  wC += ${d}) {\n                int xC = xCCorner + wC;\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float value = getX(batch, xD, xR, xC, ch);\n\n                // If a min / max value has already been found, use it. If not,\n                // use the current value.\n                float currMinMaxValue = mix(\n                    value, minMaxValue, minMaxValueFound);\n                if (value ${t} currMinMaxValue) {\n                  minMaxValue = value;\n                  minMaxValueFound = 1.0;\n                  minMaxPosition = ${r?a?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +\n                      wR * ${f} + wC`};\n                }\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `)}let v=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;"avg"===t&&(v="avgValue / max(count, 1.0)");const w=4*Math.floor(s/4),k=s%4,I=`\n      if (${b}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    `;this.userCode=`\n      const ivec3 strides =\n        ivec3(${o}, ${i}, ${u});\n      const ivec3 pads = ivec3(${m}, ${g}, ${y});\n      const float initializationValue = ${x};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xD, int xR, int xC, int ch) {\n        if (xC < 0 || xC >= ${e.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xD, xR, xC, ch);\n      }\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xDCorner = xCorner.x;\n        int xRCorner = xCorner.y;\n        int xCCorner = xCorner.z;\n\n        // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${x});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wD = 0; wD < ${p};\n            wD += ${l}) {\n          int xD = xDCorner + wD;\n\n          if (xD < 0 || xD >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${h};\n            wR += ${c}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${w}; wC += 4) {\n              int xC = xCCorner + wC * ${d};\n\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${d}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${d}, ch),\n                getValue(batch, xD, xR, xC + 3 * ${d}, ch)\n              );\n\n              ${I}\n            }\n\n            int xC = xCCorner + ${w};\n            if (${1===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                initializationValue,\n                initializationValue,\n                initializationValue\n              );\n\n              ${I}\n            } else if (${2===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${d}, ch),\n                initializationValue,\n                initializationValue\n              );\n\n              ${I}\n            } else if (${3===k}) {\n              vec4 values = vec4(\n                getValue(batch, xD, xR, xC, ch),\n                getValue(batch, xD, xR, xC + ${d}, ch),\n                getValue(batch, xD, xR, xC + 2 * ${d}, ch),\n                initializationValue\n              );\n\n              ${I}\n            }\n          }\n        }\n        setOutput(${v});\n      }\n    `}}const BI={kernelName:oe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Wy(a,"avgPool");const{filterSize:s,strides:o,pad:i,dimRoundingMode:l}=r;u(yo(o,1),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '1'`);const c=oo(a.shape,s,o,1,i,l);if(1===c.filterWidth&&1===c.filterHeight&&p(c.inShape,c.outShape))return Sk({inputs:{x:a},backend:n});const d=new PI(c,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}};const VI={kernelName:ue,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:o,pad:i,dimRoundingMode:u,dataFormat:l}=r,c=io(a.shape,s,o,[1,1,1],i,u,l),d=new LI(c,"avg",!1);return n.runWebGLProgram(d,[a],"float32")}};class WI{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=i-1-e.padInfo.top,c=u-1-e.padInfo.left,d=1/(t*n);this.userCode=`\n      const ivec2 pads = ivec2(${l}, ${c});\n      const float avgMultiplier = float(${d});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${i};\n            wR += ${s}) {\n          float dyR = float(dyRCorner + wR) / ${r}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${u};\n            wC+= ${o}) {\n            float dyC = float(dyCCorner + wC) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class zI{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=c-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*r);this.userCode=`\n      const ivec3 pads = ivec3(${h}, ${f}, ${m});\n      const float avgMultiplier = float(${g});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${c};\n            wD += ${i}) {\n          float dyD = float(dyDCorner + wD) / ${a}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${d};\n              wR += ${u}) {\n            float dyR = float(dyRCorner + wR) / ${s}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${p};\n                wC += ${l}) {\n              float dyC = float(dyCCorner + wC) / ${o}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n              dotProd += dyValue * avgMultiplier;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const UI={kernelName:le,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,o=s,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=r,d=io(o.shape,i,u,[1,1,1],l,c),p=new zI(d);return n.runWebGLProgram(p,[a],o.dtype)}};const GI={kernelName:ie,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,o=s;Wy([a,s],"avgPoolGrad");const{filterSize:i,strides:u,pad:l}=r,c=oo(o.shape,i,u,1,l),d=new WI(c);return n.runWebGLProgram(d,[a],o.dtype)}};const HI={kernelName:ce,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{a:a,b:s}=t,{transposeA:o,transposeB:i}=r;return aI({a:a,b:s,transposeA:o,transposeB:i,backend:n})}};class jI{constructor(e,t,n,r,a,s){this.outputShape=[],this.variableNames=["x","mean","variance"],ci(e,t),ci(e,n);let o="0.0";null!=r&&(ci(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";null!=a&&(ci(e,a),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${o};\n        float scale = ${i};\n        float inv = scale * inversesqrt(variance + float(${s}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `}}class qI{constructor(e,t,n,r,a,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],ci(e,t),ci(e,n);let o="vec4(0.0)";null!=r&&(ci(e,r),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";null!=a&&(ci(e,a),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`\n      void main() {\n        vec4 offset = ${o};\n        vec4 scale = ${i};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${s}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `}}const KI={kernelName:nt,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,mean:a,variance:s,offset:o,scale:i}=e;u(a.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),u(null==o||a.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),u(null==i||a.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;null==l&&(l=.001);const c=[r,a,s];let d=null;null!=o&&(d=o.shape,c.push(o));let p=null;null!=i&&(p=i.shape,c.push(i));const h=W().getBool("WEBGL_PACK_NORMALIZATION")?new qI(r.shape,a.shape,s.shape,d,p,l):new jI(r.shape,a.shape,s.shape,d,p,l);return t.runWebGLProgram(h,c,c[0].dtype)}};class XI{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;const t=ob(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const n=function(e){if(1===e)return"sourceLoc";if(e<=6)return YI.slice(0,e).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}(this.rank);let r;r=`\n        ${t} sourceLoc;\n        ${t} coords = getOutputCoords();\n        ${e.map((e,t)=>`sourceLoc.${YI[t]} = start[${t}] + coords.${YI[t]};`).join("\n")}\n      `,this.userCode=`\n      void main() {\n        ${r}\n        setOutput(getSource(${n}));\n      }\n    `}}const YI=["x","y","z","w","u","v"];class QI{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];const t=ob(this.rank),n=nk("coords",this.rank),r=nk("sourceLoc",this.rank),a=1===this.rank?"sourceLoc":`vec2(${r.slice(-2).join()})`,s=`getChannel(getSource(${r.join()}), ${a})`,o=`\n      result.x = ${s};\n      if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n        ++${r[this.rank-1]};\n        result.y = ${s};\n        --${r[this.rank-1]};\n      }\n    `,i=1===this.rank?"":`\n      --${n[this.rank-1]};\n      if (++${n[this.rank-2]} < ${e[this.rank-2]}) {\n        ++${r[this.rank-2]};\n        result.z = ${s};\n        if (++${n[this.rank-1]} < ${e[this.rank-1]}) {\n          ++${r[this.rank-1]};\n          result.w = ${s};\n        }\n      }\n    `,u=this.rank<=4?`sourceLoc = coords +\n            ${t}(${e.map((e,t)=>`start[${t}]`).join()});`:e.map((e,t)=>`${r[t]} = ${n[t]} + start[${t}];`).join("\n");this.userCode=`\n      void main() {\n        ${t} coords = getOutputCoords();\n        ${t} sourceLoc;\n        ${u}\n        vec4 result = vec4(0.);\n        ${o}\n        ${i}\n        setOutput(result);\n      }\n    `}}function ZI(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,size:o}=r,[i,u]=Tp(a,s,o);if(kp(a,i,u),0===d(u))return n.makeTensorInfo(u,a.dtype,[]);if(n.shouldExecuteOnCPU([a])||"string"===a.dtype){const e=n.texData.get(a.dataId),t=Vw(e.values,i,u,a.shape,a.dtype);return n.makeTensorInfo(u,a.dtype,t)}const{isPacked:l}=n.texData.get(a.dataId),c=Np(a.shape,i,u);if(l||!c){const e=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QI(u):new XI(u),t=[i];return n.runWebGLProgram(e,[a],a.dtype,t)}return n.uploadToGPU(a.dataId),function(e,t,n,r){const a=r.texData.get(e.dataId),s=r.makeTensorInfo(n,e.dtype),o=r.texData.get(s.dataId);Object.assign(o,a),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Sp(t,$(e.shape));a.slice&&(i+=a.slice.flatOffset),o.slice={flatOffset:i,origDataId:a.slice&&a.slice.origDataId||e.dataId};const u=r.dataRefCount.get(o.slice.origDataId)||1;return r.dataRefCount.set(o.slice.origDataId,u+1),s}(a,i,u,n)}const JI={kernelName:bn,backendName:"webgl",kernelFunc:ZI},eN={kernelName:de,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,crops:o}=r;u(a.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");const i=s.reduce((e,t)=>e*t),l=Wp(a.shape,s,i),c=zp(l.length,s.length),d=Up(a.shape,s,i),p=Gp(o,s.length),h=Hp(d,o,s.length),f=[],m=jk({inputs:{x:a},backend:n,attrs:{shape:l}}),g=nI({inputs:{x:m},backend:n,attrs:{perm:c}}),y=jk({inputs:{x:g},backend:n,attrs:{shape:d}}),b=ZI({inputs:{x:y},backend:n,attrs:{begin:p,size:h}});return f.push(m),f.push(g),f.push(y),f.forEach(e=>n.disposeIntermediateTensorInfo(e)),b}};const tN={kernelName:pe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:o}=r,i=n.readSync(a.dataId),u=n.readSync(s.dataId),l=iw(i,u,s.dtype,s.shape,o);return n.makeTensorInfo([o],s.dtype,l)}};const nN={kernelName:he,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{a:r,b:a}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS"),o=W().getNumber("WEBGL_VERSION");if(n.shouldExecuteOnCPU([r,a])||1===o){const e=n.texData.get(r.dataId).values,t=n.texData.get(a.dataId).values,[s,o]=lw(r.shape,a.shape,e,t,r.dtype),i=n.makeTensorInfo(o,r.dtype);return n.texData.get(i.dataId).values=s,i}let i;return i=s?new Nk("\n  int r = int(a.r) & int(b.r);\n  int g = int(a.g) & int(b.g);\n  int rb = int(a.b) & int(b.b);\n  int ra = int(a.a) & int(b.a);\n  return vec4(r, g, rb, ra);\n",r.shape,a.shape,!1):new kk("\n  return float(int(a.r) & int(b.r));\n",r.shape,a.shape),n.runWebGLProgram(i,[r,a],r.dtype)}};const rN={kernelName:fe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:a}=t,s=n.readSync(r.dataId),o=n.readSync(a.dataId),i=ci(Array.from(s),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}},aN=Pk({opSnippet:"return float(a != b);",cpuKernelImpl:Rw,dtype:"bool"}),sN={kernelName:Bt,backendName:"webgl",kernelFunc:aN};function oN(e){const{inputs:t,backend:n}=e,{input:r}=t;return Sk({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.real},backend:n})}const iN={kernelName:en,backendName:"webgl",kernelFunc:oN};const uN={kernelName:me,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r,attrs:a}=t,{x:s}=n,{dtype:o}=a;if("complex64"===o){if("complex64"===s.dtype)return Sk({inputs:{x:s},backend:r});const t=Nu(s.shape),n=e({inputs:{x:s},backend:r,attrs:{dtype:"float32"}}),a=Ck({inputs:{real:n,imag:t},backend:r});return t.dispose(),r.disposeIntermediateTensorInfo(n),a}if("complex64"===s.dtype){const t=oN({inputs:{input:s},backend:r}),n=e({inputs:{x:t},backend:r,attrs:{dtype:o}});return r.disposeIntermediateTensorInfo(t),n}if(!k(s.dtype,o)){const e=Sk({inputs:{x:s},backend:r});return{dataId:e.dataId,shape:e.shape,dtype:o}}if(r.shouldExecuteOnCPU([s])){const e=r.texData.get(s.dataId).values,[t,n,a]=cw(e,s.shape,s.dtype,o);return r.makeTensorInfo(t,n,a)}if("int32"===o)return function(e,t){const n=new lk(e.shape,"return float(int(x));"),r=t.runWebGLProgram(n,[e],"int32");return{dataId:r.dataId,shape:r.shape,dtype:r.dtype}}(s,r);if("bool"===o){const e=r.makeTensorInfo([],"bool",v("bool",1)),t=aN({inputs:{a:s,b:e},backend:r});return r.disposeIntermediateTensorInfo(e),t}throw new Error(`Error in Cast: failed to cast ${s.dtype} to ${o}`)}},lN="return ceil(x);",cN=Mk({opSnippet:lN,packedOpSnippet:lN,cpuKernelImpl:dw}),dN={kernelName:ge,backendName:"webgl",kernelFunc:cN};class pN{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n\n      void main() {\n        float value = getAAtOutCoords();\n        if (isnan(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, minVal, maxVal));\n      }\n    "}}class hN{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (any(isnan(value))) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n      }\n    "}}const fN={kernelName:ye,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{clipValueMin:s,clipValueMax:o}=r;let i;i=W().getBool("WEBGL_PACK_CLIP")?new hN(a.shape):new pN(a.shape);const u=[[s],[o]];return n.runWebGLProgram(i,[a],a.dtype,u)}};class mN{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "}}function gN(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}const yN={kernelName:xe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=n.texData.get(r.dataId),s=new mN(r.shape),o=[gN(r,a.complexTensorInfos.real),gN(r,a.complexTensorInfos.imag)];return n.runWebGLProgram(s,o,o[0].dtype)}};class bN{constructor(e){this.outputShape=[],this.outputShape=Ap(e,1),this.variableNames=e.map((e,t)=>`T${t}`);const t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];const n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){const e=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${e}));`)}const r=t.length,a=t[t.length-1];n.push(`else setOutput(getT${r}(yR, yC-${a}));`),this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        ${n.join("\n        ")}\n      }\n    `}}class xN{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=Ap(e,t);const n=this.outputShape,r=n.length,a=ob(r),s=nk("coords",r),o=["x","y","z","w","u","v"].slice(0,r);this.variableNames=e.map((e,t)=>`T${t}`);const i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];const u=o[t],l=o.slice(-2),c=o.join();let d=`if (${u} < ${i[0]}) {\n        return getChannel(\n            getT0(${c}), vec2(${l.join()}));\n        }`;for(let f=1;f<i.length;f++){const e=i[f-1];d+=`\n        if (${u} < ${i[f]}  && ${u} >= ${i[f-1]}) {\n          return getChannel(\n            getT${f}(${vN(o,u,e)}),\n            vec2(${vN(l,u,e)}));\n        }`}const p=i.length,h=i[i.length-1];d+=`\n        return getChannel(\n          getT${p}(${vN(o,u,h)}),\n          vec2(${vN(l,u,h)}));`,this.userCode=`\n      float getValue(${o.map(e=>"int "+e)}) {\n        ${d}\n      }\n\n      void main() {\n        ${a} coords = getOutputCoords();\n        vec4 result = vec4(getValue(${s}), 0., 0., 0.);\n\n        ${s[r-1]} = ${s[r-1]} + 1;\n        if (${s[r-1]} < ${n[r-1]}) {\n          result.g = getValue(${s});\n        }\n\n        ${s[r-2]} = ${s[r-2]} + 1;\n        if (${s[r-2]} < ${n[r-2]}) {\n          result.a = getValue(${s});\n        }\n\n        ${s[r-1]} = ${s[r-1]} - 1;\n        if (${s[r-2]} < ${n[r-2]} &&\n            ${s[r-1]} < ${n[r-1]}) {\n          result.b = getValue(${s});\n        }\n        setOutput(result);\n      }\n    `}}function vN(e,t,n){const r=e.indexOf(t);return e.map((e,t)=>t===r?`${e} - ${n}`:e).join()}function wN(e){const{inputs:t,backend:n}=e,{input:r}=t;return Sk({inputs:{x:n.texData.get(r.dataId).complexTensorInfos.imag},backend:n})}const kN={kernelName:lt,backendName:"webgl",kernelFunc:wN};function IN(e,t,n){const r=e[0].dtype;if("complex64"===r){const r=e.map(e=>oN({inputs:{input:e},backend:n})),a=e.map(e=>wN({inputs:{input:e},backend:n})),s=IN(r,t,n),o=IN(a,t,n),i=Ck({inputs:{real:s,imag:o},backend:n});return r.forEach(e=>n.disposeIntermediateTensorInfo(e)),a.forEach(e=>n.disposeIntermediateTensorInfo(e)),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(o),i}let a=n.shouldExecuteOnCPU(e);if("string"===r&&(a=!0),a){const a=e.map(e=>{const r=d(e.shape.slice(t));return jk({inputs:{x:e},backend:n,attrs:{shape:[-1,r]}})}),s=a.map(e=>({vals:n.readSync(e.dataId),shape:e.shape})),o=Ap(a.map(e=>e.shape),1),i=1===a[0].shape[0],u=pw(s,o,r,i),l=Ap(e.map(e=>e.shape),t),c=n.makeTensorInfo(l,r,u);return a.forEach(e=>n.disposeIntermediateTensorInfo(e)),c}const s=e.filter(e=>d(e.shape)>0),o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&s[0].shape.length>1;if(1===s.length){const t=o?new lk(e[0].shape,fk):new mk(e[0].shape,fk);return n.runWebGLProgram(t,e,r)}const i=W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");if(s.length>i){const e=[];for(let a=0;a<s.length;a+=i){const r=s.slice(a,a+i);e.push(IN(r,t,n))}const r=IN(e,t,n);for(const t of e)n.disposeIntermediateTensorInfo(t);return r}if(o){const e=new xN(s.map(e=>e.shape),t);return n.runWebGLProgram(e,s,r)}const{tensors2D:u,outShape:l}=function(e,t,n){const r=Ap(e.map(e=>e.shape),t),a=e.map(e=>jk({inputs:{x:e},attrs:{shape:[-1,d(e.shape.slice(t))]},backend:n}));return{tensors2D:a,outShape:r}}(s,t,n),c=new bN(u.map(e=>e.shape)),p=n.runWebGLProgram(c,u,r);u.forEach(e=>n.disposeIntermediateTensorInfo(e));const h=jk({inputs:{x:p},attrs:{shape:l},backend:n});return n.disposeIntermediateTensorInfo(p),h}function NN(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=b(a,t[0].shape)[0],o=t.map(e=>e.shape);_p(o,s);const i=Ap(t.map(e=>e.shape),s);if(0===d(i))return n.makeTensorInfo(i,t[0].dtype,[]);const u=t.filter(e=>d(e.shape)>0);return 1===u.length?Sk({inputs:{x:u[0]},backend:n}):IN(u,s,n)}const SN={kernelName:ve,backendName:"webgl",kernelFunc:NN};class TN{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;const s=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4,m="channelsLast"===e.dataFormat,g=m?1:2,y=m?2:3,b=m?3:1;let x="",v="";n&&(x=r?`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,v="result = activation(result);");const w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${x}\n\n      const ivec2 strides = ivec2(${i}, ${u});\n      const ivec2 pads = ivec2(${s}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[${b}];\n\n        ivec2 xRCCorner =\n            ivec2(coords[${g}], coords[${y}]) * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${d}; wR++) {\n          int xR = xRCorner + wR * ${l};\n\n          if (xR < 0 || xR >= ${e.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${p}; wC++) {\n            int xC = xCCorner + wC * ${c};\n\n            if (xC < 0 || xC >= ${e.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${h}; d1 += 4) {\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              if (${m}) {\n                vec4 xValues = vec4(\n                  getX(batch, xR, xC, d1),\n                  getX(batch, xR, xC, d1 + 1),\n                  getX(batch, xR, xC, d1 + 2),\n                  getX(batch, xR, xC, d1 + 3)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec4 xValues = vec4(\n                  getX(batch, d1, xR, xC),\n                  getX(batch, d1 + 1, xR, xC),\n                  getX(batch, d1 + 2, xR, xC),\n                  getX(batch, d1 + 3, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n\n            if (${1===f}) {\n\n              if (${m}) {\n                dotProd +=\n                    getX(batch, xR, xC, ${h}) *\n                    getW(wR, wC, ${h}, d2);\n              } else {\n                dotProd +=\n                    getX(batch, ${h}, xR, xC) *\n                    getW(wR, wC, ${h}, d2);\n              }\n\n            } else if (${2===f}) {\n              vec2 wValues = vec2(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2)\n              );\n\n              if (${m}) {\n                vec2 xValues = vec2(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec2 xValues = vec2(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            } else if (${3===f}) {\n              vec3 wValues = vec3(\n                getW(wR, wC, ${h}, d2),\n                getW(wR, wC, ${h} + 1, d2),\n                getW(wR, wC, ${h} + 2, d2)\n              );\n\n              if (${m}) {\n                vec3 xValues = vec3(\n                  getX(batch, xR, xC, ${h}),\n                  getX(batch, xR, xC, ${h} + 1),\n                  getX(batch, xR, xC, ${h} + 2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else {\n                vec3 xValues = vec3(\n                  getX(batch, ${h}, xR, xC),\n                  getX(batch, ${h} + 1, xR, xC),\n                  getX(batch, ${h} + 2, xR, xC)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n\n            }\n          }\n        }\n\n        float result = dotProd;\n        ${w}\n        ${v}\n        setOutput(result);\n      }\n    `}}class CN{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,a=e.strideDepth,s=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=`\n      const ivec3 strides = ivec3(${a}, ${s}, ${o});\n      const ivec3 pads = ivec3(${t}, ${n}, ${r});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${c}; wF++) {\n          int xF = xFCorner + wF * ${i};\n\n          if (xF < 0 || xF >= ${e.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${d}; wR++) {\n            int xR = xRCorner + wR * ${u};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${p}; wC++) {\n              int xC = xCCorner + wC * ${l};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${h}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${1===f}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${h}) *\n                  getW(wF, wR, wC, ${h}, d2);\n              } else if (${2===f}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${3===f}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${h}),\n                  getX(batch, xF, xR, xC, ${h} + 1),\n                  getX(batch, xF, xR, xC, ${h} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${h}, d2),\n                  getW(wF, wR, wC, ${h} + 1, d2),\n                  getW(wF, wR, wC, ${h} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class $N{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=hb(this.outputShape.length);const s=e.padInfo.left,i=e.strideWidth,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,d=c;let p="\n       int xR; int xC; int xCOffset;\n       vec4 wTexel; vec4 previous; vec4 final;";for(let o=0;o<c;o++)p+=`\n           vec4 xTexelC${2*o};\n           int xTexelC${2*o}Ready;\n           vec4 xTexelC${2*o+1};\n           int xTexelC${2*o+1}Ready;\n           vec4 xC${o};`;p+=`\n     for (int r = 0; r < ${l}; r++) {\n      for (int d1 = 0; d1 < ${e.inChannels}; d1 += 2) {\n       `;for(let o=0;o<c;o++)p+=`\n           xTexelC${2*o} = vec4(0.0);\n           xTexelC${2*o}Ready = 0;\n           xTexelC${2*o+1} = vec4(0.0);\n           xTexelC${2*o+1}Ready = 0;\n           xC${o} = vec4(0.0);`;p+="\n         xR = xRCorner + r * dilations[0];\n         if (xR >=0 && xR < inDims[0]) {\n       ";for(let g=0;g<(d+1)/2;g++){const t=2*g;if(p+=`\n           xC = xCCorner + ${t*u};\n           `,1===i){if(t<c&&(s%2===1?(p+=`\n                 xCOffset = xC + 1;\n                 if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n                   xTexelC${t} = getX(batch, xR, xCOffset, d1);\n\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${t}.zw = vec2(0.0);\n                   }\n                   xTexelC${t}Ready = 1;\n                 }\n               `,p+=1===u&&t>0?`\n                 xC${t} = vec4(xTexelC${t-2}.zw, xTexelC${t}.xy);\n                 `:`\n                   xCOffset = xC + 1 - 2;\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       previous.zw = vec2(0.0);\n                     }\n\n                     xC${t} = vec4(previous.zw, xTexelC${t}.xy);\n                   } else {\n                     xC${t} = vec4(0.0, 0.0, xTexelC${t}.xy);\n                   }\n                   `):p+=`\n                 if (xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n                   xTexelC${t} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${t}.zw = vec2(0.0);\n                   }\n                   xTexelC${t}Ready = 1;\n                 }\n\n                 xC${t} = xTexelC${t};\n                 `,t+1<c)){const e=s%2===0?o(u):u;u%2===0&&s%2===1||u%2!==0&&s%2!==1?(p+=`\n                   xCOffset = xC + imod(pads[1], 2) + ${e};\n\n                   if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                     xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n\n                     // Need to manually clear unused channels in case\n                     // we're reading from recycled texture.\n                     if (xCOffset + 1 >= inDims[1]) {\n                       xTexelC${t+1}.zw = vec2(0.0);\n                     }\n                     xTexelC${t+1}Ready = 1;\n                   }\n                   `,p+=u>1?`\n                     xCOffset -= 2;\n                     if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                      previous = getX(batch, xR, xCOffset, d1);\n                      xC${t+1} = vec4(previous.zw, xTexelC${t+1}.xy);\n                     } else {\n                      xC${t+1} = vec4(0.0, 0.0, xTexelC${t+1}.xy);\n                     }\n                     `:`\n                     xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.xy);\n                     `):p+=1===e?`\n                     xC${t+1} = xTexelC${t};\n                     `:`\n                     xCOffset = xC + ${e};\n\n                     if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                       xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n                       if (xCOffset + 1 >= inDims[1]) {\n                         xTexelC${t+1}.zw = vec2(0.0);\n                       }\n                       xTexelC${t+1}Ready = 1;\n                     }\n\n                     xC${t+1} = xTexelC${t+1};\n                     `}}else t<c&&(s%2===1?(p+=`\n                 xCOffset = xC + 1 - strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t}Ready == 0) {\n                   xTexelC${t} = getX(batch, xR, xCOffset, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${t}.zw = vec2(0.0);\n                   }\n                   xTexelC${t}Ready = 1;\n                 }\n\n                 if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${t+1}Ready == 0) {\n                   xTexelC${t+1} = getX(batch, xR, xC + 1, d1);\n                   // Need to manually clear unused channels in case\n                   // we're reading from recycled texture.\n                   if (xC + 2 >= inDims[1]) {\n                     xTexelC${t+1}.zw = vec2(0.0);\n                   }\n                   xTexelC${t+1}Ready = 1;\n                 }\n\n                 xC${t} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n               `,t+1<c&&(p+=`\n                   final = vec4(0.0);\n                   xCOffset = xC + 1 + strides[1];\n                   if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                     final = getX(batch, xR, xCOffset, d1);\n                   }\n                   xC${t+1} = vec4(xTexelC${t+1}.xy, final.xy);\n                 `)):(p+=`\n                 if(xC >= 0 && xC < inDims[1] && xTexelC${t}Ready == 0) {\n                   xTexelC${t} = getX(batch, xR, xC, d1);\n                   if (xC + 1 >= inDims[1]) {\n                     xTexelC${t}.zw = vec2(0.0);\n                   }\n                   xTexelC${t}Ready = 1;\n                 }\n\n                 xCOffset = xC + strides[1];\n                 if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${t+1}Ready == 0) {\n                   xTexelC${t+1} = getX(batch, xR, xCOffset, d1);\n                   if (xCOffset + 1 >= inDims[1]) {\n                     xTexelC${t+1}.zw = vec2(0.);\n                   }\n                   xTexelC${t+1}Ready = 1;\n                 }\n\n                 xC${t} = vec4(\n                   xTexelC${t}.xy, xTexelC${t+1}.xy);\n               `,t+1<c&&(p+=`\n                   xC${t+1} = vec4(xTexelC${t}.zw, xTexelC${t+1}.zw);\n                 `)));t<c&&(p+=`\n             wTexel = getW(r, ${t}, d1, d2);\n             dotProd += xC${t}.xxzz * vec4(wTexel.xy, wTexel.xy);\n             if(d1 + 1 < ${e.inChannels}) {\n               dotProd += xC${t}.yyww * vec4(wTexel.zw, wTexel.zw);\n             }\n           `,t+1<c&&(p+=`\n               wTexel = getW(r, ${t+1}, d1, d2);\n               dotProd += xC${t+1}.xxzz * vec4(wTexel.xy, wTexel.xy);\n               if(d1 + 1 < ${e.inChannels}) {\n                 dotProd += xC${t+1}.yyww * vec4(wTexel.zw, wTexel.zw);\n               }\n             `))}p+="\n     }\n   ",p+="\n     }\n   ",p+="\n     }\n   ";let h="",f="";n&&(h=r?`vec4 activation(vec4 a) {\n           vec4 b = getPreluActivationWeightsAtOutCoords();\n           ${n}\n         }`:a?`vec4 activation(vec4 a) {\n           vec4 b = getLeakyreluAlphaAtOutCoords();\n           ${n}\n         }`:`vec4 activation(vec4 x) {\n           ${n}\n         }`,f="result = activation(result);");const m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n       ${h}\n\n       void main() {\n         ivec4 coords = getOutputCoords();\n         int batch = coords.x;\n         ivec2 xRCCorner = coords.yz * strides - pads;\n         int d2 = coords.w;\n         int xRCorner = xRCCorner.x;\n         int xCCorner = xRCCorner.y;\n\n         //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n         vec4 dotProd = vec4(0.000000000000001);\n\n         ${p}\n\n         vec4 result = dotProd - vec4(0.000000000000001);\n         ${m}\n         ${f}\n         setOutput(result);\n       }\n     `}}class EN{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,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=e,this.enableShapeUniforms=hb(this.outputShape.length);const{dataFormat:n}=t,r=Uy(),a="channelsLast"===n,s=a?1:2,o=a?2:3,i=this.enableShapeUniforms?"if(blockIndex < outShape[2] && pos < outShape[1]) {":`if(blockIndex < ${e[2]} && pos < ${e[1]}) {`;let u="";for(let l=0;l<=1;l++)for(let e=0;e<=1;e++)u+=`\n          blockIndex = rc.z + ${e};\n          pos = rc.y + ${l};\n\n          ${i}\n            offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];\n            d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);\n\n            if(d0 < inputShape[${s}] && d0 >= 0) {\n              // Use custom imod instead mod. On Intel GPU, mod may generate\n              // unexpected value.\n              // https://github.com/tensorflow/tfjs/issues/5447\n              offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n              d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n                  inChannels);\n\n              if(d1 < inputShape[${o}] && d1 >= 0) {\n\n                ch = imod(pos, inChannels);\n\n                if (${a}) {\n                  innerDims = vec2(d1, ch);\n                  result[${2*l+e}] = getChannel(\n                    getA(rc.x, d0, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                } else {\n                  innerDims = vec2(d0, d1);\n                  result[${2*l+e}] = getChannel(\n                    getA(rc.x, ch, int(innerDims.x),\n                    int(innerDims.y)), innerDims);\n                }\n              }\n            }\n          }\n        `;this.userCode=`\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n        vec2 innerDims;\n\n        ${u}\n\n        ${r.output} = result;\n      }\n    `}}function RN(e,t){const n=e.length;return n>=3?t?[...e.slice(0,-3),e[n-3]*e[n-2],e[n-1]]:[...e.slice(0,-3),e[n-3],e[n-2]*e[n-1]]:!t&&1===n&&e[0]>1?[e[0],1]:null}function _N({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:o=0,activation:i=null}){const l=e.shape,c=r.texData.get(e.dataId),d=n.inChannels,h=l[0]*l[1]*l[2],f=n.outChannels,m="channelsLast"===n.dataFormat,g=!1;let y;const b=[];if(null!=s){const e=RN(s.shape,m);null!=e&&(s=jk({inputs:{x:s},backend:r,attrs:{shape:e}}),b.push(s))}if(null!=a){const e=RN(a.shape,m);null!=e&&(a=jk({inputs:{x:a},backend:r,attrs:{shape:e}}),b.push(a))}if(!((1===h||1===f)&&d>1e3)&&c.isPacked&&m&&null!=c.texture&&l[2]%2!==0&&p(c.shape.slice(-3),l.slice(-3))){const d=l[0]*l[1]*(l[2]+1),p={dataId:e.dataId,shape:[1,d,n.inChannels],dtype:e.dtype},h=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,u(Fy(c.shape,p.shape),()=>`packed reshape ${c.shape} to ${p.shape} isn't free`);const f=jk({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}});b.push(f);const m=aI({a:p,b:f,backend:r,transposeA:false,transposeB:g,bias:a,activation:i,preluActivationWeights:s,leakyreluAlpha:o}),x=r.texData.get(m.dataId);u(x.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=h,x.shape=n.outShape,y=Sk({inputs:{x:m},backend:r}),y.shape=n.outShape,b.push(m)}else{const u=n.outHeight*n.outWidth,l=jk({inputs:{x:e},backend:r,attrs:{shape:m?[n.batchSize,u,n.inChannels]:[n.batchSize,n.inChannels,u]}}),c=jk({inputs:{x:t},backend:r,attrs:{shape:[1,n.inChannels,n.outChannels]}}),d=aI({a:m?l:c,b:m?c:l,transposeA:!m,transposeB:g,backend:r,bias:a,activation:i,preluActivationWeights:s,leakyreluAlpha:o});y=jk({inputs:{x:d},backend:r,attrs:{shape:n.outShape}}),b.push(l),b.push(c),b.push(d)}for(const u of b)r.disposeIntermediateTensorInfo(u);return y}function AN({x:e,filter:t,convInfo:n,backend:r,bias:a=null,preluActivationWeights:s=null,leakyreluAlpha:o=0,activation:i=null}){const{filterWidth:u,filterHeight:l,inChannels:c,outWidth:p,outHeight:h,dataFormat:f}=n,m="channelsLast"===f,g=u*l*c,y=h*p,b=[n.batchSize,g,y],x=[];if(null!=s){const e=RN(s.shape,m);null!=e&&(s=jk({inputs:{x:s},backend:r,attrs:{shape:e}}),x.push(s))}if(null!=a){const e=RN(a.shape,m);null!=e&&(a=jk({inputs:{x:a},backend:r,attrs:{shape:e}}),x.push(a))}const v=jk({inputs:{x:t},backend:r,attrs:{shape:[1,g,d(t.shape)/g]}});x.push(v);const w=new EN(b,n),k=[e.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],I=r.runWebGLProgram(w,[e],"float32",k),N=jk({inputs:{x:I},backend:r,attrs:{shape:b}});x.push(I),x.push(N);const S=null!=a,T=null!=s,C="leakyrelu"===i,$=i?Lk(i,!0):null,E=new Bk(m?N.shape:v.shape,m?v.shape:N.shape,m?[n.batchSize,y,n.outChannels]:[n.batchSize,n.outChannels,y],!0,!1,S,$,T,C),R=m?[N,v]:[v,N];if(a&&R.push(a),T&&R.push(s),C){const e=r.makeTensorInfo([],"float32",Or(o,"float32"));R.push(e),x.push(e)}const _=r.runWebGLProgram(E,R,"float32"),A=jk({inputs:{x:_},backend:r,attrs:{shape:n.outShape}});x.push(_);for(const d of x)r.disposeIntermediateTensorInfo(d);return A}const ON={kernelName:we,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=r,d=xo(u),p=uo(a.shape,s.shape,o,l,i,c,!1,d);let h;if(1!==p.filterHeight||1!==p.filterWidth||1!==p.dilationHeight||1!==p.dilationWidth||1!==p.strideHeight||1!==p.strideWidth||"SAME"!==p.padInfo.type&&"VALID"!==p.padInfo.type)if(p.strideWidth<=2&&"channelsLast"===d&&W().getBool("WEBGL_EXP_CONV")){const e=new $N(p),t=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];h=n.runWebGLProgram(e,[a,s],"float32",t)}else if(W().getBool("WEBGL_CONV_IM2COL"))h=AN({x:a,filter:s,convInfo:p,backend:n});else{const e=new TN(p);h=n.runWebGLProgram(e,[a,s],"float32")}else h=_N({x:a,filter:s,convInfo:p,backend:n});const f=jk({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}};class FN{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s="channelsLast"===e.dataFormat;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${r};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${a};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              ${s?"float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);":"float dyValue = getDy(b, d2, yR, yC);\n              float xValue = getX(b, d1, xR, xC);\n              dotProd += (xValue * dyValue);"}\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class DN{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s="channelsLast"===e.dataFormat,o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,u=s?1:2,l=s?2:3,c=s?3:1;this.userCode=`\n      const ivec2 pads = ivec2(${o}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[${c}];\n\n        ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${r}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n              if (${s}) {\n                float xValue = getDy(batch, idyR, idyC, d2);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              } else {\n                float xValue = getDy(batch, d2, idyR, idyC);\n                float wValue = getW(wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class MN{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.padInfo.front,s=e.padInfo.top,o=e.padInfo.left;this.userCode=`\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yF = 0; yF < ${e.outDepth}; yF++) {\n            int xF = wF + yF * ${t} - ${a};\n\n            if (xF < 0 || xF >= ${e.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${e.outHeight}; yR++) {\n              int xR = wR + yR * ${n} - ${s};\n\n              if (xR < 0 || xR >= ${e.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${e.outWidth}; yC++) {\n                int xC = wC + yC * ${r} - ${o};\n\n                if (xC < 0 || xC >= ${e.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class PN{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,a=e.strideDepth,s=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=`\n      const ivec3 pads = ivec3(${i}, ${u}, ${l});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${t}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${a}.0;\n\n          if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${t} - 1 - wF;\n\n          for (int wR = 0; wR < ${n}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${s}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${n} - 1 - wR;\n\n            for (int wC = 0; wC < ${r}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${o}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${r} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const LN={kernelName:ke,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=r,d=xo(u),p=uo(a.shape,c,o,1,i,l,!1,d),h=new FN(p);return n.runWebGLProgram(h,[a,s],"float32")}};class BN{constructor(e){this.variableNames=["dy","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"strides",type:"vec2"}],this.outputShape=e.inShape,this.enableShapeUniforms=hb(this.outputShape.length);const t=e.filterHeight,n=e.filterWidth,r=t-1-e.padInfo.top,a=n-1-e.padInfo.left;this.userCode=`\n      const ivec2 pads = ivec2(${r}, ${a});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        vec4 result = vec4(0.);\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / strides[0];\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            int wCPerm = ${n} - 1 - wC;\n\n            float dyC = float(dyCCorner + wC) / strides[1];\n            bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n              && (fract(dyC) == 0.0);\n            int idyC = int(dyC);\n\n            float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n            bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n              && (fract(dyC2) == 0.0);\n            int idyC2 = int(dyC2);\n\n            if (idyCVal && idyCVal2) {\n              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n                vec4 dySample = getDy(batch, idyR, idyC, d2);\n                vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n                  dySample : getDy(batch, idyR, idyC2, d2);\n\n                vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n                  dySample.xy : dySample.zw;\n                result.xy += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n\n                dyValue = mod(float(idyC2), 2.) == 0. ?\n                  dySample2.xy : dySample2.zw;\n                result.zw += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n              }\n            } else if (idyCVal) {\n              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n                vec4 dySample = getDy(batch, idyR, idyC, d2);\n                vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n                  dySample.xy : dySample.zw;\n                result.xy += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n              }\n            } else if (idyCVal2) {\n              for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n                vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n                vec4 dySample = getDy(batch, idyR, idyC2, d2);\n                vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n                  dySample.xy : dySample.zw;\n                result.zw += vec2(dot(dyValue, wValue.xy),\n                  dot(dyValue, wValue.zw));\n              }\n            }\n          }\n        }\n        setOutput(result);\n      }\n    `}}const VN={kernelName:Ie,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=r,d=xo(l),p=uo(o,s.shape,i,1,u,c,!1,d);if(W().getBool("WEBGL_PACK_CONV2DTRANSPOSE")&&"channelsLast"===d){const e=[[p.strideHeight,p.strideWidth]],t=new BN(p);return n.runWebGLProgram(t,[a,s],"float32",e)}{const e=new DN(p);return n.runWebGLProgram(e,[a,s],"float32")}}};const WN={kernelName:Ne,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:o,pad:i,dilations:u}=r,l=lo(a.shape,s.shape,o,u,i),c=new CN(l);return n.runWebGLProgram(c,[a,s],"float32")}};const zN={kernelName:Se,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:o,pad:i,filterShape:u}=r,l=lo(a.shape,u,o,1,i),c=new MN(l);return n.runWebGLProgram(c,[a,s],"float32")}};const UN={kernelName:Te,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:o,strides:i,inputShape:u}=r,l=lo(u,s.shape,i,1,o),c=new PN(l);return n.runWebGLProgram(c,[a,s],"float32")}},GN=Mk({opSnippet:Dk+"\n  return cos(x);\n",packedOpSnippet:`\n  vec4 result = cos(x);\n  bvec4 isNaN = isnan(x);\n  ${Ik}\n  return result;\n`}),HN={kernelName:Ce,backendName:"webgl",kernelFunc:GN},jN=Mk({opSnippet:"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n"}),qN={kernelName:$e,backendName:"webgl",kernelFunc:jN};class KN{constructor(e,t,n,r,a){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];const[s,o,i,u]=e,[l]=t,[c,d]=n;this.outputShape=[l,c,d,u];const p="bilinear"===r?1:0,[h,f]=[o-1+".0",i-1+".0"],[m,g,y]=c>1?[""+(o-1)/(c-1),"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[b,x,v]=d>1?[""+(i-1)/(d-1),"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`\n      const float height_ratio = float(${m});\n      const float width_ratio = float(${b});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${s}) {\n          return;\n        }\n\n        float height_scale = ${g};\n        float width_scale = ${x};\n\n        float in_y = ${y};\n        if( in_y < 0.0 || in_y > ${h} ) {\n          setOutput(float(${a}));\n          return;\n        }\n        float in_x = ${v};\n        if( in_x < 0.0 || in_x > ${f} ) {\n          setOutput(float(${a}));\n          return;\n        }\n\n        vec2 sourceFracIndexCR = vec2(in_x,in_y);\n        if(${p} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n          ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n          float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n          float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n          float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n          float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n          vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n          float top = topLeft + (topRight - topLeft) * fracCR.x;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n          float newValue = top + (bottom - top) * fracCR.y;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestCR = ivec2(floor(\n            sourceFracIndexCR + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n          setOutput(newValue);\n        }\n      }\n    `}}const XN={kernelName:_e,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,c=new KN(a.shape,s.shape,i,u,l);return n.runWebGLProgram(c,[a,s,o],"float32")}};var YN,QN;(QN=YN||(YN={})).Prod="*",QN.Sum="+";class ZN{constructor(e,t,n,r){this.op=e,this.outputShape=t,this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}];const a=this.outputShape.length,s=this.op===YN.Prod?"1.0":"0.0",o=n?s:`getX(${JN(a,"coords",this.op)})`,i=this.outputShape[this.outputShape.length-1];let u="",l="";n?(u=r?"end != "+(i-1):"end != 0",l=r?"end + 1":"end - 1"):(u=r?`end + pow2 < ${i}`:"end >= pow2",l=r?"end + pow2":"end - pow2"),this.userCode=`\n      void main() {\n        ${ob(a)} coords = getOutputCoords();\n        int end = ${eS(a,"coords",this.op)};\n        float val = ${o};\n        int pow2 = int(pow(2.0, index));\n        if (${u}) {\n          int idx = ${l};\n          ${eS(a,"coords",this.op)} = idx;\n          val ${this.op}= getX(${JN(a,"coords",this.op)});\n        }\n        setOutput(val);\n      }\n    `}}function JN(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.x, ${t}.y`;if(3===e)return`${t}.x, ${t}.y, ${t}.z`;if(4===e)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function eS(e,t,n){if(1===e)return`${t}`;if(2===e)return`${t}.y`;if(3===e)return`${t}.z`;if(4===e)return`${t}.w`;throw new Error(`Cumulative ${n} for rank ${e} is not yet supported`)}function tS(e,t,n,r,a,s){const o=t.shape.length,i=Si([r],o);let u=t;null!=i&&(u=nI({inputs:{x:t},backend:n,attrs:{perm:i}}));const l=Ci(1,o)[0];if(l!==o-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${t.shape.length-1} but got axis=${r}`);const c=u.shape[l];let d=Sk({inputs:{x:u},backend:n});for(let p=0;p<=Math.ceil(Math.log2(c))-1;p++){const t=new ZN(e,u.shape,!1,s),r=[[p]],a=d;d=n.runWebGLProgram(t,[d],d.dtype,r),n.disposeIntermediateTensorInfo(a)}if(a){const t=new ZN(e,u.shape,a,s),r=d;d=n.runWebGLProgram(t,[d],d.dtype),n.disposeIntermediateTensorInfo(r)}if(null!=i){const e=nI({inputs:{x:d},backend:n,attrs:{perm:Ti(i)}});return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(u),e}return d}const nS={kernelName:Ee,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:o,reverse:i}=r;return tS(YN.Prod,a,n,s,o,i)}};const rS={kernelName:Re,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:o,reverse:i}=r;return tS(YN.Sum,a,n,s,o,i)}};const aS={kernelName:Ae,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:o,binaryOutput:i}=r;if(1===a.shape.length){const e=n.readSync(a.dataId),t=n.readSync(s.dataId),r=iw(e,t,s.dtype,s.shape,o);return n.makeTensorInfo([o],s.dtype,r)}if(2===a.shape.length){const e=n.bufferSync(a),t=n.bufferSync(s),r=uw(e,t,o,i);return n.makeTensorInfo(r.shape,s.dtype,r.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}};class sS{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${t};\n      int offset_h = imod(h, ${t});\n      int in_w = w / ${t};\n      int offset_w = imod(w, ${t});\n      int offset_d = (offset_h * ${t} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `}getHeightCoordString(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"}getWidthCoordString(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"}getDepthCoordString(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"}getOutputDepthSize(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}}const oS={kernelName:Oe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:o}=r,i=a.shape[0],u=("NHWC"===o?a.shape[1]:a.shape[2])*s,l=("NHWC"===o?a.shape[2]:a.shape[3])*s,c=("NHWC"===o?a.shape[3]:a.shape[1])/(s*s),d=new sS("NHWC"===o?[i,u,l,c]:[i,c,u,l],s,o);return n.runWebGLProgram(d,[a],a.dtype)}};class iS{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=hb(this.outputShape.length);const s=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels;let u="",l="";n&&(u=r?`float activation(float a) {\n          float b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`float activation(float a) {\n          float b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`\n          float activation(float x) {\n            ${n}\n          }\n        `,l="result = activation(result);");const c=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${u}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${i};\n        int q = d2 - d1 * ${i};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${s}; wR++) {\n          int xR = xRCorner + wR * dilations[0];\n\n          if (xR < 0 || xR >= inDims[0]) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${o}; wC++) {\n            int xC = xCCorner + wC * dilations[1];\n\n            if (xC < 0 || xC >= inDims[1]) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n\n        float result = dotProd;\n        ${c}\n        ${l}\n        setOutput(result);\n      }\n    `}}class uS{constructor(e,t=!1,n=null,r=!1,a=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=hb(this.outputShape.length);const s=e.outChannels/e.inChannels,i=e.padInfo.left,u=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,d=e.filterWidth,p=d;let h="\n      int xR; int xC; int xCOffset;\n      vec4 wTexel; vec4 previous; vec4 final;";for(let o=0;o<d;o++)h+=`\n          vec4 xTexelC${2*o};\n          int xTexelC${2*o}Ready;\n          vec4 xTexelC${2*o+1};\n          int xTexelC${2*o+1}Ready;\n          vec4 xC${o};`;h+=`\n    for (int r = 0; r < ${c}; r++) {\n      `;for(let o=0;o<d;o++)h+=`\n          xTexelC${2*o} = vec4(0.0);\n          xTexelC${2*o}Ready = 0;\n          xTexelC${2*o+1} = vec4(0.0);\n          xTexelC${2*o+1}Ready = 0;\n          xC${o} = vec4(0.0);`;h+="\n        xR = xRCorner + r * dilations[0];\n        if (xR >=0 && xR < inDims[0]) {\n      ";for(let y=0;y<(p+1)/2;y++){const e=2*y;if(h+=`\n          xC = xCCorner + ${e*l};\n          `,1===u){if(e<d&&(i%2===1?(h+=`\n                xCOffset = xC + 1;\n                if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e}Ready == 0) {\n                  xTexelC${e} = getX(batch, xR, xCOffset, d1);\n\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${e}.zw = vec2(0.0);\n                  }\n                  xTexelC${e}Ready = 1;\n                }\n              `,h+=1===l&&e>0?`\n                xC${e} = vec4(xTexelC${e-2}.zw, xTexelC${e}.xy);\n                `:`\n                  xCOffset = xC + 1 - 2;\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                    previous = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      previous.zw = vec2(0.0);\n                    }\n\n                    xC${e} = vec4(previous.zw, xTexelC${e}.xy);\n                  } else {\n                    xC${e} = vec4(0.0, 0.0, xTexelC${e}.xy);\n                  }\n                  `):h+=`\n                if (xC >= 0 && xC < inDims[1] && xTexelC${e}Ready == 0) {\n                  xTexelC${e} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${e}.zw = vec2(0.0);\n                  }\n                  xTexelC${e}Ready = 1;\n                }\n\n                xC${e} = xTexelC${e};\n                `,e+1<d)){const t=i%2===0?o(l):l;l%2===0&&i%2===1||l%2!==0&&i%2!==1?(h+=`\n                  xCOffset = xC + imod(pads[1], 2) + ${t};\n\n                  if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e+1}Ready == 0) {\n                    xTexelC${e+1} = getX(batch, xR, xCOffset, d1);\n\n                    // Need to manually clear unused channels in case\n                    // we're reading from recycled texture.\n                    if (xCOffset + 1 >= inDims[1]) {\n                      xTexelC${e+1}.zw = vec2(0.0);\n                    }\n                    xTexelC${e+1}Ready = 1;\n                  }\n                  `,h+=l>1?`\n                    xCOffset -= 2;\n                    if (xCOffset >= 0 && xCOffset < inDims[1]) {\n                     previous = getX(batch, xR, xCOffset, d1);\n                     xC${e+1} = vec4(previous.zw, xTexelC${e+1}.xy);\n                    } else {\n                     xC${e+1} = vec4(0.0, 0.0, xTexelC${e+1}.xy);\n                    }\n                    `:`\n                    xC${e+1} = vec4(xTexelC${e}.zw, xTexelC${e+1}.xy);\n                    `):h+=1===t?`\n                    xC${e+1} = xTexelC${e};\n                    `:`\n                    xCOffset = xC + ${t};\n\n                    if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e+1}Ready == 0) {\n                      xTexelC${e+1} = getX(batch, xR, xCOffset, d1);\n                      if (xCOffset + 1 >= inDims[1]) {\n                        xTexelC${e+1}.zw = vec2(0.0);\n                      }\n                      xTexelC${e+1}Ready = 1;\n                    }\n\n                    xC${e+1} = xTexelC${e+1};\n                    `}}else e<d&&(i%2===1?(h+=`\n                xCOffset = xC + 1 - strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e}Ready == 0) {\n                  xTexelC${e} = getX(batch, xR, xCOffset, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${e}.zw = vec2(0.0);\n                  }\n                  xTexelC${e}Ready = 1;\n                }\n\n                if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${e+1}Ready == 0) {\n                  xTexelC${e+1} = getX(batch, xR, xC + 1, d1);\n                  // Need to manually clear unused channels in case\n                  // we're reading from recycled texture.\n                  if (xC + 2 >= inDims[1]) {\n                    xTexelC${e+1}.zw = vec2(0.0);\n                  }\n                  xTexelC${e+1}Ready = 1;\n                }\n\n                xC${e} = vec4(xTexelC${e}.zw, xTexelC${e+1}.zw);\n              `,e+1<d&&(h+=`\n                  final = vec4(0.0);\n                  xCOffset = xC + 1 + strides[1];\n                  if(xCOffset >= 0 && xCOffset < inDims[1]) {\n                    final = getX(batch, xR, xCOffset, d1);\n                  }\n                  xC${e+1} = vec4(xTexelC${e+1}.xy, final.xy);\n                `)):(h+=`\n                if(xC >= 0 && xC < inDims[1] && xTexelC${e}Ready == 0) {\n                  xTexelC${e} = getX(batch, xR, xC, d1);\n                  if (xC + 1 >= inDims[1]) {\n                    xTexelC${e}.zw = vec2(0.0);\n                  }\n                  xTexelC${e}Ready = 1;\n                }\n\n                xCOffset = xC + strides[1];\n                if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${e+1}Ready == 0) {\n                  xTexelC${e+1} = getX(batch, xR, xCOffset, d1);\n                  if (xCOffset + 1 >= inDims[1]) {\n                    xTexelC${e+1}.zw = vec2(0.);\n                  }\n                  xTexelC${e+1}Ready = 1;\n                }\n\n                xC${e} = vec4(\n                  xTexelC${e}.xy, xTexelC${e+1}.xy);\n              `,e+1<d&&(h+=`\n                  xC${e+1} = vec4(xTexelC${e}.zw, xTexelC${e+1}.zw);\n                `)));e<d&&(h+=`\n            wTexel = getW(r, ${e}, d1, q);\n            dotProd += xC${e} * vec4(wTexel.xz, wTexel.xz);\n          `,e+1<d&&(h+=`\n              wTexel = getW(r, ${e+1}, d1, q);\n              dotProd += xC${e+1} * vec4(wTexel.xz, wTexel.xz);\n            `))}h+="\n    }\n  ",h+="\n      }\n    ";let f="",m="";n&&(f=r?`vec4 activation(vec4 a) {\n          vec4 b = getPreluActivationWeightsAtOutCoords();\n          ${n}\n        }`:a?`vec4 activation(vec4 a) {\n          vec4 b = getLeakyreluAlphaAtOutCoords();\n          ${n}\n        }`:`vec4 activation(vec4 x) {\n          ${n}\n        }`,m="result = activation(result);");const g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),r&&this.variableNames.push("preluActivationWeights"),a&&this.variableNames.push("leakyreluAlpha"),this.userCode=`\n      ${f}\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${s};\n        int q = d2 - d1 * ${s};\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        //intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.\n        vec4 dotProd = vec4(0.000000000000001);\n\n        ${h}\n\n        vec4 result = dotProd - vec4(0.000000000000001);\n        ${g}\n        ${m}\n        setOutput(result);\n      }\n    `}}const lS={kernelName:Fe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=r;let d=l;null==d&&(d=[1,1]),u(yo(o,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${d}'`);const p=uo(a.shape,s.shape,o,d,i,c,!0);let h;h=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels===1?new uS(p):new iS(p);const f=[[p.padInfo.top,p.padInfo.left],[p.strideHeight,p.strideWidth],[p.dilationHeight,p.dilationWidth],[p.inHeight,p.inWidth]];return n.runWebGLProgram(h,[a,s],"float32",f)}};class cS{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;const t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,a=e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${s} + dm;\n\n        float dotProd = 0.0;\n\n        // TO DO: Vec4 over the batch size\n        for (int b = 0; b < ${e.batchSize}; b++) {\n          for (int yR = 0; yR < ${e.outHeight}; yR++) {\n            int xR = wR + yR * ${t} - ${r};\n\n            if (xR < 0 || xR >= ${e.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${e.outWidth}; yC++) {\n              int xC = wC + yC * ${n} - ${a};\n\n              if (xC < 0 || xC >= ${e.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class dS{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;const t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,a=e.strideWidth,s=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`\n      const ivec2 pads = ivec2(${s}, ${o});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${t}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${r}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${t} - 1 - wR;\n\n          for (int wC = 0; wC < ${n}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${a}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${n} - 1 - wC;\n\n            // TO DO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${i}; dm++) {\n              int d2 = d1 * ${i} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const pS={kernelName:De,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=r,d=uo(a.shape,c,o,i,u,l,!0),p=new cS(d);return n.runWebGLProgram(p,[a,s],"float32")}};const hS={kernelName:Me,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=r,d=uo(c,s.shape,o,i,u,l,!0),p=new dS(d);return n.runWebGLProgram(p,[a,s],"float32")}};class fS{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode="\n      void main() {\n          ivec2 coords = getOutputCoords();\n          float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n          setOutput(val);\n      }\n    "}}const mS={kernelName:Pe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=[...r.shape,...r.shape],s=d(r.shape),o=jk({inputs:{x:r},backend:n,attrs:{shape:[s]}}),i=new fS(s),u=n.runWebGLProgram(i,[o],o.dtype),l=jk({inputs:{x:u},backend:n,attrs:{shape:a}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(u),l}};class gS{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;const{inHeight:t,inWidth:n,padInfo:r,strideHeight:a,strideWidth:s,filterHeight:o,filterWidth:i,dilationHeight:u,dilationWidth:l}=e,{top:c,left:d}=r;this.userCode=`\n      const ivec2 strides = ivec2(${a}, ${s});\n      const ivec2 pads = ivec2(${c}, ${d});\n      const float neg_infinity = -3.4e38;\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.w;\n        ivec2 outTopLeftCorner =\n            coords.yz * strides - pads;\n        int hBeg = outTopLeftCorner.x;\n        int wBeg = outTopLeftCorner.y;\n\n        float curVal = neg_infinity;\n        for (int h = 0; h < ${o}; h++) {\n          int hIn = hBeg + h * ${u};\n\n          if (hIn >= 0 && hIn < ${t}) {\n            for (int w = 0; w < ${i}; w++) {\n              int wIn = wBeg + w * ${l};\n\n              if (wIn >= 0 && wIn < ${n}) {\n                float xVal = getX(batch, hIn, wIn, d1);\n                float wVal = getW(h, w, d1);\n\n                float val = xVal + wVal;\n                if (val > curVal) {\n                  curVal = val;\n                }\n              }\n            }\n          }\n        }\n\n        float result = curVal;\n        setOutput(result);\n      }\n    `}}const yS={kernelName:Le,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:o,pad:i,dilations:u}=r,l=so(a.shape,s.shape,o,i,"NHWC",u);let c;const d=new gS(l);c=n.runWebGLProgram(d,[a,s],"float32");const p=jk({inputs:{x:c},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(c),p}};const bS={kernelName:Ue,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{equation:a}=r,s=t,{allDims:o,summedDims:i,idDims:u}=ch(a,s.length);ph(o.length,u,s);const{path:l,steps:c}=hh(i,u),d=c.length;let h=null,f=o.length;const m=[];for(let g=0;g<d;++g){for(const e of c[g]){const{permutationIndices:t,expandDims:r}=dh(f,u[e]);let a;fh(t)?a=s[e]:(a=nI({inputs:{x:s[e]},backend:n,attrs:{perm:t}}),m.push(a));const o=a.shape.slice();for(let e=0;e<r.length;++e)o.splice(r[e],0,1);p(a.shape,o)||(a=jk({inputs:{x:a},backend:n,attrs:{shape:o}}),m.push(a)),null===h?h=a:(h=Gk({inputs:{a:a,b:h},backend:n}),m.push(h))}g<d-1&&(l[g]>=0&&(h=eI({inputs:{x:h},backend:n,attrs:{axis:l[g]-(o.length-f),keepDims:!1}}),m.push(h)),f--)}for(const p of m)p!==h&&n.disposeIntermediateTensorInfo(p);return h}},xS=Mk({opSnippet:"return (x >= 0.0) ? x : (exp(x) - 1.0);",packedOpSnippet:"\n  vec4 result;\n\n  result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n  result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n  result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n  result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n  return result;\n"}),vS={kernelName:Ge,backendName:"webgl",kernelFunc:xS},wS={kernelName:He,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n}=e,{dy:r,y:a}=t,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Nk("\n  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n",r.shape,a.shape):new kk("return (b >= 0.0) ? a : a * (b + 1.0);",r.shape,a.shape);return n.runWebGLProgram(s,[r,a],r.dtype)}},kS=Pk({opSnippet:"return float(a == b);",packedOpSnippet:"\n  return vec4(equal(a, b));\n",dtype:"bool",cpuKernelImpl:hw}),IS={kernelName:qe,backendName:"webgl",kernelFunc:kS},NS=Mk({opSnippet:`\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n  float p = ${Kp};\n  float a1 = ${Xp};\n  float a2 = ${Yp};\n  float a3 = ${Qp};\n  float a4 = ${Zp};\n  float a5 = ${Jp};\n\n  float sign = sign(x);\n  x = abs(x);\n  float t = 1.0 / (1.0 + p * x);\n  return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`}),SS={kernelName:je,backendName:"webgl",kernelFunc:NS},TS=Mk({opSnippet:Dk+"\n  return exp(x);\n",packedOpSnippet:"\n  vec4 result = exp(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",cpuKernelImpl:fw,dtype:"float32"}),CS={kernelName:Ke,backendName:"webgl",kernelFunc:TS};function $S(e){const{inputs:t,attrs:n,backend:r}=e,{dim:a}=n,{input:s}=t,o=s.shape.length,i=s.shape.slice();let l=a;return a<0&&(u(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),jk({inputs:{x:s},backend:r,attrs:{shape:i}})}const ES={kernelName:Xe,backendName:"webgl",kernelFunc:$S},RS="return exp(x) - 1.0;",_S=Mk({opSnippet:RS,packedOpSnippet:RS,cpuKernelImpl:mw}),AS={kernelName:Ye,backendName:"webgl",kernelFunc:_S};class OS{constructor(e,t,n){this.variableNames=["real","imag"];const r=t[1];this.outputShape=t;const a=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${r}.0`:"1.0";let o;if("real"===e)o="return real * expR - imag * expI;";else{if("imag"!==e)throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);o="return real * expI + imag * expR;"}this.userCode=`\n      const float exponentMultiplier = ${a};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${o}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${r});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${r}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${s};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `}}function FS(e,t,n){const r=n.texData.get(e.dataId),a=d(e.shape),s=e.shape[e.shape.length-1],o=jk({inputs:{x:e},backend:n,attrs:{shape:[a/s,s]}}),i=o.shape,u=new OS("real",i,t),l=new OS("imag",i,t),c=[{dataId:r.complexTensorInfos.real.dataId,dtype:r.complexTensorInfos.real.dtype,shape:i},{dataId:r.complexTensorInfos.imag.dataId,dtype:r.complexTensorInfos.imag.dtype,shape:i}],p=n.runWebGLProgram(u,c,"float32"),h=n.runWebGLProgram(l,c,"float32"),f=Ck({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);const m=jk({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(f),m}const DS={kernelName:Qe,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return FS(r,!1,n)}};class MS{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode="\n      void main() {\n        // Input can be obtained from uniform value.\n        setOutput(value);\n      }\n    "}}function PS(e){const{backend:t,attrs:n}=e,{shape:r,value:a}=n;let{dtype:s}=n;if(s=s||S(a),"string"===s){const e=w(s,d(r));return e.fill(a),t.makeTensorInfo(r,s,e)}{const e=new MS(r,a),n=[[a]];return t.runWebGLProgram(e,[],s,n)}}const LS={kernelName:Ze,backendName:"webgl",kernelFunc:PS};class BS{constructor(e){this.variableNames=["Image"],this.outputShape=[];const t=e[2];this.outputShape=e,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n\n          int coordX = ${t} - x - 1;\n          float outputValue;\n          if(coordX >= 0 && coordX < ${t}) {\n            outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n          } else {\n            outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}}const VS={kernelName:Je,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{const{image:n}=e,r=t,a=new BS(n.shape);return r.runWebGLProgram(a,[n],n.dtype)}},WS="return floor(x);",zS=Mk({opSnippet:WS,packedOpSnippet:WS,cpuKernelImpl:gw}),US={kernelName:et,backendName:"webgl",kernelFunc:zS},GS=Pk({opSnippet:"\n  float s = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  if (ib != 0) {\n    // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n    return float(idiv(ia, ib, s));\n  } else {\n    return NAN;\n  }\n",packedOpSnippet:"\n  ivec4 ia = round(a);\n  ivec4 ib = round(b);\n  bvec4 cond = notEqual(ib, ivec4(0));\n  ivec4 result = ivec4(0);\n  vec4 s = sign(a) * sign(b);\n\n  // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n  if (cond[0]) {\n    result[0] = idiv(ia[0], ib[0], s[0]);\n  }\n  if (cond[1]) {\n    result[1] = idiv(ia[1], ib[1], s[1]);\n  }\n  if (cond[2]) {\n    result[2] = idiv(ia[2], ib[2], s[2]);\n  }\n  if (cond[3]) {\n    result[3] = idiv(ia[3], ib[3], s[3]);\n  }\n  return vec4(result);\n",dtype:"int32"}),HS={kernelName:tt,backendName:"webgl",kernelFunc:GS};class jS{constructor(e){this.variableNames=["A"];const t=Uy(),[n,r]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${r}.0, ${n}.0);\n\n        vec4 values = ${t.texture2D}(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `}}class qS{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;const t=Uy(),[n,r]=e;this.outputShape=e,this.userCode=`\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n\n        vec4 result = vec4(0.);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            texC = coords[1] + row;\n            depth = coords[2] + col;\n\n            vec2 uv = (vec2(texC, texR) + halfCR) /\n                       vec2(${r}.0, ${n}.0);\n            vec4 values = ${t.texture2D}(A, uv);\n            float value;\n            if (depth == 0) {\n              value = values.r;\n            } else if (depth == 1) {\n              value = values.g;\n            } else if (depth == 2) {\n              value = values.b;\n            } else if (depth == 3) {\n              value = values.a;\n            }\n\n            result[row * 2 + col] = floor(value * 255.0 + 0.5);\n          }\n        }\n\n        ${t.output} = result;\n      }\n    `}}const KS={kernelName:Jn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e;let{pixels:a}=t;const{numChannels:s}=r,o="undefined"!==typeof HTMLVideoElement&&a instanceof HTMLVideoElement,i="undefined"!==typeof HTMLImageElement&&a instanceof HTMLImageElement,[u,l]=o?[a.videoWidth,a.videoHeight]:[a.width,a.height],c=[l,u],d=[l,u,s];if(i||o){const e=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");null!=XS&&e===YS||(YS=e,XS=document.createElement("canvas").getContext("2d",{willReadFrequently:YS})),XS.canvas.width=u,XS.canvas.height=l,XS.drawImage(a,0,0,u,l),a=XS.canvas}const p=n.makeTensorInfo(c,"int32");n.texData.get(p.dataId).usage=cy.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),a);const h=W().getBool("WEBGL_PACK")?new qS(d):new jS(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}};let XS,YS=W().getBool("CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU");const QS={kernelName:nr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:o,preluActivationWeights:i}=t,{strides:u,pad:l,dataFormat:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=xo(c),g=uo(a.shape,s.shape,u,d,l,p,!1,m);let y;const b=[],x=null!=o,v=null!=i,w="leakyrelu"===h,k=()=>{const e=[a,s],t=(e,t)=>{if("NCHW"===t&&1===e.shape.length&&1!==e.shape[0]){const t=jk({inputs:{x:e},backend:n,attrs:{shape:[e.shape[0],1,1]}});return b.push(t),t}return e};if(x&&e.push(t(o,c)),v&&e.push(t(i,c)),w){const t=n.makeTensorInfo([],"float32",Or(f,"float32"));e.push(t),b.push(t)}return e};if(1!==g.filterHeight||1!==g.filterWidth||1!==g.dilationHeight||1!==g.dilationWidth||1!==g.strideHeight||1!==g.strideWidth||"SAME"!==g.padInfo.type&&"VALID"!==g.padInfo.type)if(g.strideWidth<=2&&"channelsLast"===m&&W().getBool("WEBGL_EXP_CONV")){const e=h?Lk(h,!0):null,t=new $N(g,x,e,v,w),r=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],a=k();y=n.runWebGLProgram(t,a,"float32",r)}else if(W().getBool("WEBGL_CONV_IM2COL"))y=AN({x:a,filter:s,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{const e=h?Lk(h,!1):null,t=new TN(g,x,e,v,w),r=k();y=n.runWebGLProgram(t,r,"float32")}else y=_N({x:a,filter:s,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});const I=jk({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return b.push(y),b.forEach(e=>n.disposeIntermediateTensorInfo(e)),I}};const ZS={kernelName:rr,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=r,m=[];let g=d;null==g&&(g=[1,1]),u(yo(l,g),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${g}'`);const y=uo(a.shape,s.shape,l,g,c,p,!0),b=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&y.strideWidth<=2&&y.outChannels/y.inChannels===1,x=h?Lk(h,b):null,v=[a,s],w=null!=o,k=null!=i,I="leakyrelu"===h;if(w&&v.push(o),k&&v.push(i),I){const e=n.makeTensorInfo([],"float32",Or(f,"float32"));v.push(e),m.push(e)}let N;N=b?new uS(y,w,x,k,I):new iS(y,w,x,k,I);const S=[[y.padInfo.top,y.padInfo.left],[y.strideHeight,y.strideWidth],[y.dilationHeight,y.dilationWidth],[y.inHeight,y.inWidth]],T=n.runWebGLProgram(N,v,"float32",S);return m.forEach(e=>n.disposeIntermediateTensorInfo(e)),T}};class JS{constructor(e,t,n,r){this.sliceDim=e,this.strides=t,this.paramsShape=r,this.variableNames=["x","indices"],this.outputShape=n;const a=ob(n.length);let s="\n    int index;";for(let o=0;o<this.sliceDim;o++)s+=`\n          index = round(getIndices(coords[0], ${o}));\n          out_of_bounds = out_of_bounds || index < 0;\n          out_of_bounds = out_of_bounds || index >= ${this.paramsShape[o]};\n          flattenIndex += index * ${this.strides[o]};`;this.userCode=`\n         void main() {\n          ${a} coords = getOutputCoords();\n          int flattenIndex = 0;\n          bool out_of_bounds = false;\n\n          ${s}\n\n          setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n        }\n      `}}const eT={kernelName:at,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{params:r,indices:a}=t,s=a.shape,o=s[s.length-1],i=d(r.shape),[u,l,c,p]=wp(r,a),h=jk({inputs:{x:a},backend:n,attrs:{shape:[l,o]}}),f=jk({inputs:{x:r},backend:n,attrs:{shape:[d(r.shape)/c,c]}});if(n.shouldExecuteOnCPU([r,a])||"string"===r.dtype){const e=n.readSync(a.dataId),t=n.bufferSync(r),s=yw(e,t,r.dtype,l,o,c,p,r.shape,i);return n.makeTensorInfo(u,r.dtype,s.values)}const m=new JS(o,p,[l,c],r.shape),g=n.runWebGLProgram(m,[f,h],f.dtype),y=jk({inputs:{x:g},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(g),y}};class tT{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;const n=ob(this.rank),r=function(e){const t=["resRC.x","resRC.y","resRC.z","resRC.w"],n=[];for(let r=0;r<e.length;r++)2===r?n.push("index"):n.push(`${t[r]}`);return n.join()}(e);this.userCode=`\n      void main() {\n        ${n} resRC = getOutputCoords();\n        int index = int(getIndices(resRC.x, resRC.z));\n        float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n        setOutput(inBounds * getA(${r}));\n      }\n    `}}function nT(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,indices:s}=t,{axis:o,batchDims:i}=r,l=b(o,a.shape)[0];if(W().get("DEBUG")){const e=n.readSync(s.dataId),t=a.shape[l];for(let n=0;n<e.length;++n){const r=e[n];u(r<=t-1&&r>=0,()=>`GatherV2: the index value ${r} is not in [0, ${t-1}]`)}}const c=Eh(a,s,l,i),p=d(s.shape),h=[],f=jk({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),m=jk({inputs:{x:s},backend:n,attrs:{shape:[c.batchSize,p/c.batchSize]}});h.push(f),h.push(m);const g=[c.batchSize,c.outerSize,p/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([a,s])||"string"===a.dtype){const e=n.bufferSync(m),t=n.bufferSync(f),r=bw(t,e,g);return h.forEach(e=>n.disposeIntermediateTensorInfo(e)),n.makeTensorInfo(c.outputShape,r.dtype,r.values)}const y=new tT(f.shape,g),x=n.runWebGLProgram(y,[f,m],f.dtype);h.push(x);const v=jk({inputs:{x:x},backend:n,attrs:{shape:c.outputShape}});return h.forEach(e=>n.disposeIntermediateTensorInfo(e)),v}const rT={kernelName:rt,backendName:"webgl",kernelFunc:nT},aT=Pk({opSnippet:"return float(a > b);",packedOpSnippet:"\n  return vec4(greaterThan(a, b));\n",cpuKernelImpl:xw,dtype:"bool"}),sT={kernelName:st,backendName:"webgl",kernelFunc:aT},oT=Pk({opSnippet:"return float(a >= b);",packedOpSnippet:"\n  return vec4(greaterThanEqual(a, b));\n",dtype:"bool",cpuKernelImpl:vw}),iT={kernelName:ot,backendName:"webgl",kernelFunc:oT};const uT={kernelName:ut,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{input:r}=t;return FS(r,!0,n)}},lT=Mk({opSnippet:"return float(!isnan(x) && !isinf(x));",dtype:"bool"}),cT={kernelName:ct,backendName:"webgl",kernelFunc:lT},dT=Mk({opSnippet:"return float(isinf(x));",dtype:"bool"}),pT={kernelName:dt,backendName:"webgl",kernelFunc:dT},hT=Mk({opSnippet:"return float(isnan(x));",dtype:"bool"}),fT={kernelName:pt,backendName:"webgl",kernelFunc:hT},mT=Pk({opSnippet:"return float(a < b);",packedOpSnippet:"\n  return vec4(lessThan(a, b));\n",cpuKernelImpl:ww,dtype:"bool"}),gT={kernelName:ft,backendName:"webgl",kernelFunc:mT},yT=Pk({opSnippet:"return float(a <= b);",packedOpSnippet:"\n  return vec4(lessThanEqual(a, b));\n",cpuKernelImpl:kw,dtype:"bool"}),bT={kernelName:mt,backendName:"webgl",kernelFunc:yT};const xT={kernelName:gt,backendName:"webgl",kernelFunc:function(e){const{backend:t,attrs:n}=e,{start:r,stop:a,num:s}=n,o=Iw(r,a,s);return t.makeTensorInfo([o.length],"float32",o)}},vT=Mk({opSnippet:Dk+"\n  return x < 0.0 ? 0./0. : log(x);\n",packedOpSnippet:"\n  vec4 result = log(x);\n  bvec4 isNaN = isnan(x);\n  result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n  result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n  result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n  result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n  return result;\n",cpuKernelImpl:Nw}),wT={kernelName:yt,backendName:"webgl",kernelFunc:vT},kT=Mk({opSnippet:Dk+"\n  return log(1.0 + x);\n"}),IT={kernelName:bt,backendName:"webgl",kernelFunc:kT},NT=Pk({opSnippet:"return float(a >= 1.0 && b >= 1.0);",packedOpSnippet:"\n  return vec4(\n    vec4(greaterThanEqual(a, vec4(1.0))) *\n    vec4(greaterThanEqual(b, vec4(1.0))));\n",dtype:"bool"}),ST={kernelName:xt,backendName:"webgl",kernelFunc:NT},TT=Mk({opSnippet:"return float(!(x >= 1.0));"}),CT={kernelName:vt,backendName:"webgl",kernelFunc:TT},$T=Pk({opSnippet:"return float(a >= 1.0 || b >= 1.0);",packedOpSnippet:"\n  return min(\n    vec4(greaterThanEqual(a, vec4(1.0))) +\n    vec4(greaterThanEqual(b, vec4(1.0))),\n    vec4(1.0));\n",dtype:"bool"}),ET={kernelName:wt,backendName:"webgl",kernelFunc:$T};class RT{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[];const s=t,o=e[3]-1;let i;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;i=.5===a?`inversesqrt(${u})`:1===a?`1.0/(${u})`:`exp(log(${u}) * float(-${a}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${s}; j <= ${s}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${o}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${i};\n        setOutput(val);\n      }\n    `}}class _T{constructor(e,t,n,r,a){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;const s=t,o=e[3]-1;let i;this.outputShape=e;const u=`float(${n}) + float(${r}) * sum`;i=.5===a?`inversesqrt(${u})`:1===a?`1.0/(${u})`:`exp(log(${u}) * float(-${a}));`,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords.x;\n        int r = coords.y;\n        int c = coords.z;\n        int d = coords.w;\n\n        bool hasNextCol = d < ${this.outputShape[3]};\n        bool hasNextRow = c < ${this.outputShape[2]};\n\n        vec4 sum = vec4(0.);\n        vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n        vec4 xAtOutputCoords = vec4(\n          getChannel(xFragAtOutputCoords, vec2(c, d)),\n          hasNextCol ?\n            getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n          hasNextRow ?\n            getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n        );\n\n        int firstChannel = d - ${s};\n        vec2 cache = vec2(0.);\n        if(firstChannel >= 0){\n          vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n          cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n            if(hasNextRow){\n              cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n            }\n        }\n\n        ivec2 depth = ivec2(d, d + 1);\n        for (int j = - ${s}; j <= ${s}; j++) {\n          ivec2 idx = depth + j;\n          bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n          bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));\n\n          bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n          bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n          if(depthInRange || depthPlusOneInRange){\n            vec4 z = vec4(0.);\n            vec4 xFragAtCurrentDepth;\n            z.xz = cache.xy;\n            if(depthPlusOneInRange && hasNextCol){\n              xFragAtCurrentDepth = idx.y != d ?\n                getX(b, r, c, idx.y) : xFragAtOutputCoords;\n              z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n              if(hasNextRow){\n                z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n              }\n            }\n            cache.xy = z.yw;\n            sum += z * z;\n          }\n        }\n        vec4 result = xAtOutputCoords * ${i};\n        setOutput(result);\n      }\n    `}}const AT={kernelName:kt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{depthRadius:s,bias:o,alpha:i,beta:u}=r,l=W().getBool("WEBGL_PACK_NORMALIZATION")?new _T(a.shape,s,o,i,u):new RT(a.shape,s,o,i,u);return n.runWebGLProgram(l,[a],a.dtype)}};class OT{constructor(e,t,n,r,a){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=a,this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${t})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${t} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${r}) * norm + float(${n});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${r})\n                * float(${a})\n                * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${a});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `}}const FT={kernelName:It,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a,y:s,dy:o}=t,{depthRadius:i,bias:u,alpha:l,beta:c}=r,d=new OT(a.shape,i,u,l,c);return n.runWebGLProgram(d,[a,s,o],a.dtype)}};function DT(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reductionIndices:s,keepDims:o}=r,i=a.shape.length,u=b(s,a.shape);let l=u;const c=Si(l,i),p=null!=c,h=n.shouldExecuteOnCPU([a]);let f=a;if(p){if(h){const e=n.texData.get(f.dataId).values,t=new Array(i);for(let n=0;n<t.length;n++)t[n]=a.shape[c[n]];const r=Jw(e,a.shape,a.dtype,c,t);f=n.makeTensorInfo(t,a.dtype);n.texData.get(f.dataId).values=r}else f=Jk(a,c,n);l=Ci(l.length,i)}Ni("max",l,i);const[m,g]=ki(f.shape,l);let y,x=m;if(o&&(x=Ii(m,u)),h){const e=n.texData.get(f.dataId).values,t=Sw(e,d(g),x,a.dtype);y=n.makeTensorInfo(x,a.dtype);n.texData.get(y.dataId).values=t}else y=function(e,t,n,r){const a=d(t),s=jk({inputs:{x:e},attrs:{shape:[d(e.shape)/a,a]},backend:r}),o=Yk(s,e.dtype,"max",r),i=jk({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(o),i}(f,g,x,n);return p&&n.disposeIntermediateTensorInfo(f),y}const MT={kernelName:Nt,backendName:"webgl",kernelFunc:DT},PT=Pk({opSnippet:wk+"\n  return max(a, b);\n",packedOpSnippet:"\n  vec4 result = vec4(max(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  "+Ik+"\n  return result;\n",cpuKernelImpl:Tw}),LT={kernelName:St,backendName:"webgl",kernelFunc:PT};const BT={kernelName:Tt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;Wy(a,"maxPool");const{filterSize:s,strides:o,pad:i,dimRoundingMode:l}=r;u(yo(o,1),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '1'`);const c=oo(a.shape,s,o,1,i,l);if(1===c.filterWidth&&1===c.filterHeight&&p(c.inShape,c.outShape))return Sk({inputs:{x:a},backend:n});const d=new PI(c,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}};const VT={kernelName:$t,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{filterSize:s,strides:o,pad:i,dataFormat:u,dimRoundingMode:l}=r,c=io(a.shape,s,o,[1,1,1],i,l,u),d=new LI(c,"max",!1);return n.runWebGLProgram(d,[a],a.dtype)}};class WT{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,a=e.effectiveFilterHeight,s=e.effectiveFilterWidth,o=a-1-e.padInfo.top,i=s-1-e.padInfo.left,u=a*s-1;this.userCode=`\n      const ivec2 pads = ivec2(${o}, ${i});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${a};\n          wR += ${r}) {\n          float dyR = float(dyRCorner + wR) / ${t}.0;\n\n          if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${s}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${n}.0;\n\n            if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${s} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}class zT{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;const t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,a=e.dilationDepth,s=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.front,d=u-1-e.padInfo.top,p=l-1-e.padInfo.left,h=i*u*l-1;this.userCode=`\n      const ivec3 pads = ivec3(${c}, ${d}, ${p});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int ch = coords.u;\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyDCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n        // dx(xD, xR, xC, ch).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int wD = 0; wD < ${i};\n           wD += ${a}) {\n          float dyD = float(dyDCorner + wD) / ${t}.0;\n\n          if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n            continue;\n          }\n          int idyD = int(dyD);\n\n          for (int wR = 0; wR < ${u};\n              wR += ${s}) {\n            float dyR = float(dyRCorner + wR) / ${n}.0;\n\n            if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n                fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            for (int wC = 0; wC < ${l};\n                wC += ${o}) {\n              float dyC = float(dyCCorner + wC) / ${r}.0;\n\n              if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n              int maxPosValue = ${h} -\n                  int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n              // Get the current value, check it against the value from the\n              // position matrix.\n              int curPosValue =\n                  wD * ${u} * ${l} +\n                  wR * ${l} + wC;\n              float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n              dotProd += dyValue * mask;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `}}const UT={kernelName:Et,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,o=s,{filterSize:i,strides:u,pad:l,dimRoundingMode:c}=r,d=io(o.shape,i,u,[1,1,1],l,c),p=new LI(d,"max",!0),h=n.runWebGLProgram(p,[o],o.dtype),f=new zT(d),m=n.runWebGLProgram(f,[a,h],o.dtype);return n.disposeIntermediateTensorInfo(h),m}};const GT={kernelName:Ct,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s,output:o}=t,i=s;Wy([s,o],"maxPoolGrad");const{filterSize:u,strides:l,pad:c,dimRoundingMode:d}=r,p=oo(i.shape,u,l,1,c,d),h=new PI(p,"max",!0),f=n.runWebGLProgram(h,[i],i.dtype),m=new WT(p),g=n.runWebGLProgram(m,[a,f],i.dtype);return n.disposeIntermediateTensorInfo(f),g}};const HT={kernelName:Rt,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{filterSize:a,strides:s,pad:o,includeBatchInIndex:i}=t,l=n;u(4===r.shape.length,()=>`Error in maxPool: input must be rank 4 but got rank ${r.shape.length}.`);const c=[1,1];u(yo(s,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${c}'`);const d=oo(r.shape,a,s,c,o),[p,h]=function(e,t,n,r){let a=new PI(n,"max",!1);const s=r.runWebGLProgram(a,[e],"float32");return a=new PI(n,"max",!0,!0,t),[s,r.runWebGLProgram(a,[e],"float32")]}(r,i,d,l);return[p,h]}};const jT={kernelName:_t,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{x:r}=e,{keepDims:a,axis:s}=t,o=n,i=r.shape.length,u=b(s,r.shape);let l=u;const c=Si(l,i),p=null!=c,h=o.shouldExecuteOnCPU([r]),f=[];let m=r;if(p){if(h){const e=o.texData.get(m.dataId).values,t=new Array(i);for(let a=0;a<t.length;a++)t[a]=r.shape[c[a]];const n=Jw(e,r.shape,r.dtype,c,t);m=o.makeTensorInfo(t,r.dtype);o.texData.get(m.dataId).values=n}else m=Jk(r,c,o);f.push(m),l=Ci(l.length,i)}Ni("sum",l,i);const[g,y]=ki(m.shape,l);let x=g;a&&(x=Ii(g,u));const v=function(e,t,n,r){const a=d(t),s=jk({inputs:{x:e},attrs:{shape:[d(e.shape)/a,a]},backend:r}),o=Yk(s,"float32","mean",r),i=jk({inputs:{x:o},attrs:{shape:n},backend:r});return r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(o),i}(m,y,x,o);for(const d of f)o.disposeIntermediateTensorInfo(d);return v}};const qT={kernelName:At,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:o}=r,i=a.shape.length,u=b(s,a.shape);let l=u;const c=Si(l,i);let p=a;null!=c&&(p=nI({inputs:{x:a},backend:n,attrs:{perm:c}}),l=Ci(l.length,a.shape.length)),Ni("min",l,i);const[h,f]=ki(p.shape,l),m=jk({inputs:{x:p},backend:n,attrs:{shape:[-1,d(f)]}}),g=Yk(m,m.dtype,"min",n);let y;if(o){y=jk({inputs:{x:g},backend:n,attrs:{shape:Ii(h,u)}})}else y=jk({inputs:{x:g},backend:n,attrs:{shape:h}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),null!=c&&n.disposeIntermediateTensorInfo(p),y}},KT=Pk({opSnippet:wk+"\n  return min(a, b);\n",packedOpSnippet:"\n  vec4 result = vec4(min(a, b));\n  bvec4 isNaNA = isnan(a);\n  bvec4 isNaNB = isnan(b);\n  bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n  "+Ik+"\n  return result;\n",cpuKernelImpl:Cw}),XT={kernelName:Ot,backendName:"webgl",kernelFunc:KT};class YT{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((t,n)=>t[0]+e[n]+t[1]);const r=e.length,a=ob(r),s=t.map(e=>e[0]).join(","),o=t.map((t,n)=>t[0]+e[n]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r),u="reflect"===n?0:1;this.userCode=1!==r?`\n      ${a} start = ${a}(${s});\n      ${a} end = ${a}(${o});\n\n      void main() {\n        ${a} outC = getOutputCoords();\n        for (int i = 0; i < ${r}; i++) {\n          if (outC[i] < start[i]) {\n            outC[i] = start[i] * 2 - outC[i] - ${u};\n          } else if(outC[i] >= end[i]) {\n            outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};\n          }\n        }\n        ${a} coords = outC - start;\n        setOutput(getX(${i}));\n      }\n    `:`\n        int start = ${s};\n        int end = ${o};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start) {\n            outC = start * 2 - outC - ${u};\n          } else if(outC >= end) {\n            outC = (end - 1) * 2 - outC + ${u};\n          }\n          setOutput(getX(outC - start));\n        }\n      `}}class QT{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((t,n)=>t[0]+e[n]+t[1]);const r=e.length,a=ob(r),s=t.map(e=>e[0]).join(","),o=t.map((t,n)=>t[0]+e[n]).join(","),i=nk("rc",r),u=nk("source",r),l=`${i[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,d="reflect"===n?0:1;let p="";if(1===r){const e=`\n        ${a} source = rc;\n        if (source < start) {\n          source = start * 2 - source - ${d};\n        } else if (source >= end) {\n          source = (end - 1) * 2 - source + ${d};\n        }\n        source -= start;\n      `;p=`\n        ${a} rc = outputLoc;\n        ${e}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${i[r-1]} += 1;\n        if(${l}) {\n          ${e}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n      `}else{const e=`\n        ${a} source = rc;\n        ${a} lt = ${a}(lessThan(source, start));\n        ${a} gte = ${a}(greaterThanEqual(source, end));\n        ${a} orig = 1 - (lt + gte);\n        source = orig * source +\n                lt * (start * 2 - source - ${d}) +\n                gte * ((end - 1) * 2 - source + ${d});\n        source -= start;\n      `;p=`\n        ${a} rc = outputLoc;\n        ${e}\n        result[0] = getChannel(getX(${u.join()}), ${c});\n        ${i[r-1]} += 1;\n        if(${l}) {\n          ${e}\n          result[1] = getChannel(getX(${u.join()}), ${c});\n        }\n        rc = outputLoc;\n        ${i[r-2]} += 1;\n        if(${i[r-2]} < ${this.outputShape[r-2]}) {\n          ${e}\n          result[2] = getChannel(getX(${u.join()}), ${c});\n          ${i[r-1]} += 1;\n          if(${l}) {\n            ${e}\n            result[3] = getChannel(getX(${u.join()}), ${c});\n          }\n        }\n      `}this.userCode=`\n      const ${a} start = ${a}(${s});\n      const ${a} end = ${a}(${o});\n\n      void main() {\n        ${a} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${p}\n        setOutput(result);\n      }\n    `}}const ZT={kernelName:Ft,backendName:"webgl",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r}=e,{paddings:a,mode:s}=n,o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new QT(r.shape,a,s):new YT(r.shape,a,s);return t.runWebGLProgram(o,[r],r.dtype)}},JT=Pk({opSnippet:"if (b == 0.0) return NAN;\n  return mod(a, b);",packedOpSnippet:"\n  vec4 result = mod(a, b);\n  bvec4 isNaN = equal(b, vec4(0.0));\n  "+Ik+"\n  return result;\n"}),eC={kernelName:Dt,backendName:"webgl",kernelFunc:JT};class tC{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${t-1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${t-1}));\n      }\n    `}}const nC=Pk({opSnippet:"\nif (a == b) {\n  return 1.0;\n};\nreturn a / b;",packedOpSnippet:"\n  // vec4 one = vec4(equal(a, b));\n  // return one + (vec4(1.0) - one) * a / b;\n  vec4 result = a / b;\n  if(a.x == b.x) {\n    result.x = 1.;\n  }\n  if(a.y == b.y) {\n    result.y = 1.;\n  }\n  if(a.z == b.z) {\n    result.z = 1.;\n  }\n  if(a.w == b.w) {\n    result.w = 1.;\n  }\n\n  return result;\n",checkOutOfBounds:!0}),rC={kernelName:ze,backendName:"webgl",kernelFunc:nC},aC="return a - b;",sC=Pk({opSnippet:aC,packedOpSnippet:aC,supportsComplex:!0,cpuKernelImpl:Yw}),oC={kernelName:Wn,backendName:"webgl",kernelFunc:sC};function iC(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{dim:s}=r,o=b([s],a.shape),i=DT({inputs:{x:a},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),u=Ii(i.shape,o),l=jk({inputs:{x:i},backend:n,attrs:{shape:u}}),c=sC({inputs:{a:a,b:l},backend:n}),d=TS({inputs:{x:c},backend:n}),p=eI({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=jk({inputs:{x:p},backend:n,attrs:{shape:u}}),f=nC({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}const uC={kernelName:$n,backendName:"webgl",kernelFunc:iC};const lC={kernelName:Mt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{logits:a}=t,{numSamples:s,seed:o,normalized:i}=r,u=i?a:iC({inputs:{logits:a},backend:n,attrs:{dim:a.shape.length-1}}),l=u.shape[0],c=u.shape[1],d=new tC(l,c,s),p=[[o]],h=n.runWebGLProgram(d,[u],"int32",p);return i||n.disposeIntermediateTensorInfo(u),h}},cC=ck+"\n  return -x;\n";const dC={kernelName:Lt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t;if(n.shouldExecuteOnCPU([r])){const e=n.texData.get(r.dataId),[t,a]=Ew(e.values,r.shape,r.dtype);return n.makeTensorInfo(a,r.dtype,t)}let a;return a=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?new mk(r.shape,"\n  vec4 result = -x;\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"):new lk(r.shape,cC),n.runWebGLProgram(a,[r],r.dtype)}},pC=ad;const hC={kernelName:Vt,backendName:"webgl",kernelFunc:function(e){ar("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u}=r,l=n.readSync(a.dataId),c=n.readSync(s.dataId),{selectedIndices:d}=pC(l,c,o,i,u);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}},fC=sd;const mC={kernelName:Wt,backendName:"webgl",kernelFunc:function(e){ar("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,padToMaxOutputSize:l}=r,c=n.readSync(a.dataId),d=n.readSync(s.dataId),{selectedIndices:p,validOutputs:h}=fC(c,d,o,i,u,l);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}},gC=od;const yC={kernelName:zt,backendName:"webgl",kernelFunc:function(e){ar("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");const{inputs:t,backend:n,attrs:r}=e,{boxes:a,scores:s}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:u,softNmsSigma:l}=r,c=n.readSync(a.dataId),d=n.readSync(s.dataId),p=o,h=i,f=u,m=l,{selectedIndices:g,selectedScores:y}=gC(c,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}};class bC{constructor(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${r}), float(${n}),\n                      float(index == coords.y)));\n      }\n    `}}const xC={kernelName:Gt,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{indices:a}=t,{dtype:s,depth:o,onValue:i,offValue:u}=r,l=d(a.shape),c=new bC(l,o,i,u),p=jk({inputs:{x:a},backend:n,attrs:{shape:[l]}}),h=n.runWebGLProgram(c,[p],s);n.disposeIntermediateTensorInfo(p);const f=jk({inputs:{x:h},backend:n,attrs:{shape:[...a.shape,o]}});return n.disposeIntermediateTensorInfo(h),f}};function vC(e){const{inputs:t,backend:n}=e,{x:r}=t;if("complex64"===r.dtype){const e=oN({inputs:{input:r},backend:n}),t=vC({inputs:{x:e},backend:n}),a=wN({inputs:{input:r},backend:n}),s=vC({inputs:{x:a},backend:n}),o=Ck({inputs:{real:t,imag:s},backend:n});return n.disposeIntermediateTensorInfo(e),n.disposeIntermediateTensorInfo(t),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(s),o}return PS({attrs:{shape:r.shape,dtype:r.dtype,value:"string"===r.dtype?"":0},backend:n})}const wC={kernelName:Qn,backendName:"webgl",kernelFunc:vC};const kC={kernelName:Ut,backendName:"webgl",kernelFunc:function e(t){const{inputs:n,backend:r}=t,{x:a}=n;if("string"===a.dtype)throw new Error("onesLike is not supported under string dtype");if("complex64"===a.dtype){const t=oN({inputs:{input:a},backend:r}),n=e({inputs:{x:t},backend:r}),s=wN({inputs:{input:a},backend:r}),o=vC({inputs:{x:s},backend:r}),i=Ck({inputs:{real:n,imag:o},backend:r});return r.disposeIntermediateTensorInfo(t),r.disposeIntermediateTensorInfo(n),r.disposeIntermediateTensorInfo(s),r.disposeIntermediateTensorInfo(o),i}return PS({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:r})}};const IC={kernelName:Ht,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r;if(1===t.length)return $S({inputs:{input:t[0]},backend:n,attrs:{dim:a}});const s=t[0].shape,o=t[0].dtype;t.forEach(e=>{l(s,e.shape,"All tensors passed to stack must have matching shapes"),u(o===e.dtype,()=>"All tensors passed to stack must have matching dtypes")});const i=[],c=t.map(e=>{const t=$S({inputs:{input:e},backend:n,attrs:{dim:a}});return i.push(t),t}),d=NN({inputs:c,backend:n,attrs:{axis:a}});return i.forEach(e=>n.disposeIntermediateTensorInfo(e)),d}};class NC{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((t,n)=>t[0]+e[n]+t[1]);const r=e.length,a=ob(r),s=t.map(e=>e[0]).join(","),o=t.map((t,n)=>t[0]+e[n]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?`\n      ${a} start = ${a}(${s});\n      ${a} end = ${a}(${o});\n\n      void main() {\n        ${a} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(value);\n        } else {\n          ${a} coords = outC - start;\n          setOutput(getX(${i}));\n        }\n      }\n    `:`\n        int start = ${s};\n        int end = ${o};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(value);\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `}}class SC{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((t,n)=>t[0]+e[n]+t[1]);const r=e.length,a=ob(r),s=t.map(e=>e[0]).join(","),o=t.map((t,n)=>t[0]+e[n]).join(","),i=nk("rc",r),u=nk("source",r),l=`${i[r-1]} < ${this.outputShape[r-1]}`,c=1===r?"source":`vec2(${u.slice(-2).join()})`,d=[`${a} rc = outputLoc;`,`${i[r-1]} += 1;\n       if(${l}) {\n      `,1===r?"":`}\n       rc = outputLoc;\n       ${i[r-2]} += 1;\n       if(${i[r-2]} < ${this.outputShape[r-2]}) {`,1===r?"":`  ${i[r-1]} += 1;\n         if(${l}) {`],p=1===r?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";let h="";for(let f=0,m=1===r?2:4;f<m;f++)h+=`\n        ${d[f]}\n        if (${p}) {\n          result[${f}] = float(value);\n        } else {\n          ${a} source = rc - start;\n          result[${f}] = getChannel(getX(${u.join()}), ${c});\n        }\n      `;h+=1===r?"} ":"}}",this.userCode=`\n      const ${a} start = ${a}(${s});\n      const ${a} end = ${a}(${o});\n\n      void main() {\n        ${a} outputLoc = getOutputCoords();\n        vec4 result = vec4(0.);\n        ${h}\n        setOutput(result);\n      }\n    `}}const TC=e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{paddings:s,constantValue:o}=r;if(0===d(a.shape)){return PS({backend:n,attrs:{shape:s.map((e,t)=>e[0]+a.shape[t]+e[1]),value:o,dtype:a.dtype}})}const i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SC(a.shape,s,o):new NC(a.shape,s,o),u=[[o]];return n.runWebGLProgram(i,[a],a.dtype,u)},CC={kernelName:jt,backendName:"webgl",kernelFunc:TC},$C=Pk({opSnippet:"\n  if(a < 0.0 && floor(b) < b){\n    return NAN;\n  }\n  if (b == 0.0) {\n    return 1.0;\n  }\n  return (round(mod(b, 2.0)) != 1) ?\n      pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",packedOpSnippet:"\n  // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n  vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n  vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n  vec4 result = multiplier * pow(abs(a), b);\n\n  // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n  bvec4 isExpZero = equal(b, vec4(0.0));\n  result.r = isExpZero.r ? 1.0 : result.r;\n  result.g = isExpZero.g ? 1.0 : result.g;\n  result.b = isExpZero.b ? 1.0 : result.b;\n  result.a = isExpZero.a ? 1.0 : result.a;\n\n  bvec4 isNaN1 = lessThan(a, vec4(0.0));\n  bvec4 isNaN2 = lessThan(floor(b), b);\n  bvec4 isNaN = bvec4(isNaN1.x && isNaN2.x, isNaN1.y && isNaN2.y, isNaN1.z && isNaN2.z, isNaN1.w && isNaN2.w);\n  "+Ik+"\n  return result;\n"}),EC={kernelName:qt,backendName:"webgl",kernelFunc:$C};const RC={kernelName:Xt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,keepDims:o}=r,i=a.shape.length,u=[],l=b(s,a.shape);let c=l;const p=Si(c,i);let h,f=a;if(null!=p&&(f=nI({inputs:{x:a},backend:n,attrs:{perm:p}}),c=Ci(c.length,i),u.push(f)),Ni("prod",c,i),n.shouldExecuteOnCPU([f])){const e=n.texData.get(f.dataId).values,{outVals:t,outShape:r,outDtype:a}=_w(f.shape,f.dtype,e,c);h=n.makeTensorInfo(r,a,t)}else{const[e,t]=ki(f.shape,c),r=d(t),s=jk({inputs:{x:f},backend:n,attrs:{shape:[-1,r]}}),o=Yk(s,pa(a.dtype),"prod",n);h=jk({inputs:{x:o},backend:n,attrs:{shape:e}}),u.push(s),u.push(o)}if(o){u.push(h);const e=Ii(h.shape,l);h=jk({inputs:{x:h},backend:n,attrs:{shape:e}})}return u.forEach(e=>n.disposeIntermediateTensorInfo(e)),h}};const _C={kernelName:Yt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{paramsNestedSplits:a,paramsDenseValues:s,indices:o}=t,{outputRaggedRank:i}=r,u=a.map(e=>n.readSync(e.dataId)),l=a.map(e=>e.shape),c=n.readSync(s.dataId),d=n.readSync(o.dataId),[p,h,f]=Aw(u,l,c,s.shape,s.dtype,d,o.shape,i),m=p.map(e=>n.makeTensorInfo([e.length],"int32",e)),g=n.makeTensorInfo(f,s.dtype,h);return m.concat([g])}};const AC={kernelName:Qt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{starts:r,limits:a,deltas:s}=t,o=n.readSync(r.dataId),i=n.readSync(a.dataId),u=n.readSync(s.dataId),[l,c]=Ow(o,r.shape,r.dtype,i,a.shape,u,s.shape);return[n.makeTensorInfo([l.length],"int32",l),n.makeTensorInfo([c.length],r.dtype,c)]}};const OC={kernelName:Zt,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{shape:a,values:s,defaultValue:o,rowPartitionTensors:i}=t,{rowPartitionTypes:u}=r,l=n.readSync(a.dataId),c=n.readSync(s.dataId),d=n.readSync(o.dataId),p=i.map(e=>n.readSync(e.dataId)),h=i.map(e=>e.shape),[f,m]=Fw(l,a.shape,c,s.shape,s.dtype,d,o.shape,p,h,u);return n.makeTensorInfo(f,s.dtype,m)}},FC=e=>{const{backend:t,attrs:n}=e,{start:r,stop:a,step:s,dtype:o}=n,i=Dw(r,a,s,o);return t.makeTensorInfo([i.length],o,i)},DC={kernelName:Jt,backendName:"webgl",kernelFunc:FC},MC=Mk({opSnippet:"return 1.0 / x;"}),PC={kernelName:tn,backendName:"webgl",kernelFunc:MC},LC=Mk({opSnippet:ck+"\n  return (x < 0.0) ? 0.0 : x;\n",packedOpSnippet:"\n  vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"}),BC={kernelName:nn,backendName:"webgl",kernelFunc:LC},VC=Mk({opSnippet:ck+"\n  return (x < 0.0) ? 0.0 : min(6.0, x);\n",packedOpSnippet:"\n  vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n"}),WC={kernelName:ln,backendName:"webgl",kernelFunc:VC};class zC{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[s,o,i,u]=e;this.outputShape=[s,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let d;d=a?"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${d};\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `}}class UC{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[s,o,i,u]=e;this.outputShape=[s,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n];let d;d=a?"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,\n                                     ${i}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${d};\n\n        // Compute the four integer indices.\n        ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n        ivec3 sourceCeilRC = ivec3(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        // In parallel, construct four corners for all four components in\n        // packed 2x2 cell.\n        vec4 topLeft = vec4(\n          getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 bottomLeft = vec4(\n          getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n        vec4 topRight = vec4(\n          getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec4 bottomRight = vec4(\n          getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n          hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n        vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n        vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n        vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n        vec4 newValue = mix(top, bottom, fracRC.x);\n\n        setOutput(newValue);\n      }\n    `}}const GC={kernelName:on,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:o,size:i}=r,[u,l]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new UC(a.shape,u,l,s,o):new zC(a.shape,u,l,s,o);return n.runWebGLProgram(c,[a],"float32")}};class HC{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,s,o]=e,i=[n&&s>1?r-1:r,n&&o>1?a-1:a],u=[n&&s>1?s-1:s,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],d=1/l,p=1/c,h=2*Math.ceil(d)+2,f=2*Math.ceil(p)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${d});\n        const float invWidthScale = float(${p});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${s}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${o}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${r-1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${a-1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}}const jC={kernelName:un,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:o}=r,i=new HC(s.shape,a.shape,o);return n.runWebGLProgram(i,[s],s.dtype)}};class qC{constructor(e,t,n,r,a){this.variableNames=["A"],this.outputShape=[];const[s,o,i,u]=e;this.outputShape=[s,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0";let p;p=a?"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":"vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]});\n      const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = ${p};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `}}class KC{constructor(e,t,n,r,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];const[s,o,i,u]=e;this.outputShape=[s,t,n,u];const l=[r&&t>1?o-1:o,r&&n>1?i-1:i],c=[r&&t>1?t-1:t,r&&n>1?n-1:n],d=r?"0.5":"0.0";let p;p=a?"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":"vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`\n      const vec3 effectiveInputOverOutputRatioRC = vec3(\n          ${l[0]/c[0]},\n          ${l[1]/c[1]},\n          ${l[1]/c[1]});\n      const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,\n                                     ${i}.0);\n\n      float getAValue(int b, int r, int c, int d) {\n        return getChannel(getA(b, r, c, d), vec2(c, d));\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        // Calculate values for next column in yRC.z.\n        ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n        // Fractional source index.\n        vec3 sourceFracIndexRC = ${p};\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec3 sourceNearestRC = ivec3(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));\n\n        // Should we calculate next column and row elements in 2x2 packed cell.\n        bool hasNextCol = d < ${u-1};\n        bool hasNextRow = coords.z < ${n-1};\n\n        vec4 newValue = vec4(\n          getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n          hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n                     : 0.0,\n          hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n                     : 0.0,\n          (hasNextRow && hasNextCol) ?\n            getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n        setOutput(newValue);\n      }\n    `}}const XC={kernelName:an,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a}=t,{alignCorners:s,halfPixelCenters:o,size:i}=r,[u,l]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new KC(a.shape,u,l,s,o):new qC(a.shape,u,l,s,o);return n.runWebGLProgram(c,[a],a.dtype)}};class YC{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;const[,r,a]=t,[,s,o]=e,i=[n&&s>1?r-1:r,n&&o>1?a-1:a],u=[n&&s>1?s-1:s,n&&o>1?o-1:o],l=i[0]/u[0],c=i[1]/u[1],d=1/l,p=1/c,h=2*Math.ceil(d)+2,f=2*Math.ceil(p)+2;this.userCode=`\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${l});\n        const float widthScale = float(${c});\n\n        const float invHeightScale = float(${d});\n        const float invWidthScale = float(${p});\n\n        const int winHeight = int(${h});\n        const int winWidth = int(${f});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${s}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${o}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${i[0]}) *\n                (float(dyR) / float(${u[0]}));\n\n            float sourceFracCol =\n                float(${i[1]}) *\n                  (float(dyC) / float(${u[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${r}) - 1),\n                ${n} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${a}) - 1),\n                ${n} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `}}const QC={kernelName:sn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{images:a,dy:s}=t,{alignCorners:o}=r,i=new YC(s.shape,a.shape,o);return n.runWebGLProgram(i,[s],s.dtype)}};class ZC{constructor(e,t){this.variableNames=["x"];const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,1===n)return void(this.userCode=`\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${e[0]} - coord - 1));\n        }\n      `);const r=e.map((n,r)=>(n=>-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - coords[${n}] - 1`:`coords[${n}]`)(r)).join(","),a=ob(n);this.userCode=`\n      void main() {\n        ${a} coords = getOutputCoords();\n        setOutput(getX(${r}));\n      }\n    `}}class JC{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;const n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;const r=nk("rc",n),a=`${r[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${r[n-2]} + 1 < ${this.outputShape[n-2]}`,o=ob(n);var i;function u(n){const r=e.map((r,a)=>function(n,r){return-1!==t.indexOf(n)&&1!==e[n]?`${e[n]} - ${r[n]} - 1`:`${r[n]}`}(a,n));return`getChannel(getX(${r.join(",")}), vec2(${r.slice(-2).join(",")}))`}this.userCode=1===n?`\n        void main(){\n          int rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = getChannel(getX(${e[0]} - rc - 1),\n            ${e[0]} - rc - 1);\n          if(${a}){\n              result.g = getChannel(getX(${e[0]} - (rc  + 1) - 1),\n                ${e[0]} - (rc  + 1) - 1);\n          }\n          setOutput(result);\n        }\n      `:`\n        void main() {\n          ${o} rc = getOutputCoords();\n          vec4 result = vec4(0.);\n          result.r = ${i=r.slice(),u(i)};\n          if(${a}){\n            result.g = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",u(e)}(r.slice())};\n          }\n          if(${s}) {\n            result.b = ${function(e){return e[n-2]="("+e[n-2]+" + 1)",u(e)}(r.slice())};\n            if(${a}) {\n              result.a = ${function(e){return e[n-1]="("+e[n-1]+" + 1)",e[n-2]="("+e[n-2]+" + 1)",u(e)}(r.slice())};\n            }\n          }\n          setOutput(result);\n        }\n    `}}const e$={kernelName:cn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{dims:s}=r,o=a.shape.length,i=b(s,a.shape);if(0===o)return Sk({inputs:{x:a},backend:n});const u=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new JC(a.shape,i):new ZC(a.shape,i);return n.runWebGLProgram(u,[a],a.dtype)}};class t${constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];const n=e[1],r=e[2];this.outputShape=e;let a="";a="number"===typeof t?`float outputValue = ${t.toFixed(2)};`:`\n        vec3 fill = vec3(${t.join(",")});\n        float outputValue = fill[coords[3]];`,this.userCode=`\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int x = coords[2];\n          int y = coords[1];\n          float coordXFloat = (float(x) - params[0]) * params[3] -\n            (float(y) - params[1]) * params[2];\n          float coordYFloat = (float(x) - params[0]) * params[2] +\n            (float(y) - params[1]) * params[3];\n          int coordX = int(round(coordXFloat + params[0]));\n          int coordY = int(round(coordYFloat + params[1]));\n          ${a}\n          if(coordX >= 0 && coordX < ${r} && coordY >= 0 && coordY < ${n}) {\n            outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n          }\n          setOutput(outputValue);\n        }\n    `}}const n$={kernelName:er,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{const{image:r}=e,{radians:a,fillValue:s,center:o}=t,i=n,u=new t$(r.shape,s),[l,c]=Vp(o,r.shape[1],r.shape[2]),d=[[l,c,Math.sin(a),Math.cos(a)]];return i.runWebGLProgram(u,[r],r.dtype,d)}},r$=Mk({opSnippet:"\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n"}),a$={kernelName:dn,backendName:"webgl",kernelFunc:r$},s$=Mk({opSnippet:"return inversesqrt(x);",cpuKernelImpl:Mw}),o$={kernelName:pn,backendName:"webgl",kernelFunc:s$};class i${constructor(e,t,n,r,a,s,o=!0,i=!1){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;const u=ob(a.length),l=ob(s.length);let c="";1===n?c="i":2===n&&(c="i, j");const d=`getIndices(${c})`;let p="";1===r?p="i":2===r&&(p="i, coords[1]");const h=`getUpdates(${p})`;let f="";i&&(f="coords[0], coords[1]");const m=`getDefaultValue(${f})`,g=t>1?"strides[j]":"strides";this.userCode=`\n        ${u} strides = ${u}(${a});\n\n        void main() {\n          ${l} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${e}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${t}; j++) {\n              int index = round(${d});\n              flattenedIndex += index * ${g};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${h};\n              found = true;\n            }\n          }\n          setOutput(mix(${m}, sum, float(found)));\n        }\n      `}}class u${constructor(e,t,n,r,a,s,o=!0,i=!1){this.variableNames=["updates","indices","defaultValue"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=s;const u=ob(a.length),l=ob(s.length);let c="";1===n?c="i":2===n&&(c="i, j");const d=`getIndices(${c})`;let p="";1===r?p="i":2===r&&(p="i, coords[1]");const h=`getUpdates(${p})`;let f="";i&&(f="coords[0], coords[1]");const m=`getDefaultValue(${f})`,g=t>1?"strides[j]":"strides",y=t>1?"strides[j + 1]":"strides";this.userCode=`\n        ${u} strides = ${u}(${a});\n\n        void main() {\n          ${l} coords = getOutputCoords();\n          vec4 sum = vec4(0.);\n          vec4 found = vec4(0.);\n          for (int i = 0; i < ${e}; i+=2) {\n            ivec2 flattenedIndex = ivec2(0);\n            for (int j = 0; j < ${t}; j+=2) {\n              ivec4 index = round(${d});\n              flattenedIndex += index.xz * ${g};\n              if (j + 1 < ${t}) {\n                flattenedIndex += index.yw * ${y};\n              }\n            }\n            if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n                flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n              vec4 updVals = ${h};\n              if (flattenedIndex[0] == coords[0]) {\n                sum.xy += updVals.xy;\n                found.xy = vec2(1.);\n              } else if (flattenedIndex[0] == coords[0] + 1) {\n                sum.zw += updVals.xy;\n                found.zw = vec2(1.);\n              }\n              if (flattenedIndex[1] == coords[0]) {\n                sum.xy += updVals.zw;\n                found.xy = vec2(1.);\n              } else if (flattenedIndex[1] == coords[0] + 1) {\n                sum.zw += updVals.zw;\n                found.zw = vec2(1.);\n              }\n            }\n          }\n          setOutput(mix(${m}, sum, found));\n        }\n      `}}const l$={kernelName:hn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{indices:a,updates:s}=t,{shape:o}=r,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:d}=hc(0,a,o),p=[d/l,l];if(0===d)return n.makeTensorInfo(o,a.dtype);const h=jk({inputs:{x:a},backend:n,attrs:{shape:[u,i]}}),f=jk({inputs:{x:s},backend:n,attrs:{shape:[u,l]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0]));let g;g=W().getBool("WEBGL_PACK")?new u$(u,i,h.shape.length,f.shape.length,c,p):new i$(u,i,h.shape.length,f.shape.length,c,p);const y=n.runWebGLProgram(g,[f,h,m],f.dtype),b=jk({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),b}};class c${constructor(e,t,n,r){this.variableNames=["sortedSequence","values"],this.customUniforms=[{name:"numInputs",type:"int"}],this.outputShape=[e,n];const a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,s=2===W().getNumber("WEBGL_VERSION")?"while (left < right) {":a,o="left"===r?"<":"<=";this.userCode=`\n       int findBound(int batch, float value) {\n         int left = 0;\n         int right = numInputs;\n         int mid;\n         ${s}\n           mid = (left + right) / 2;\n           if (getSortedSequence(batch, mid) ${o} value) {\n             left = mid + 1;\n           } else {\n             right = mid;\n           }\n         }\n         return right;\n       }\n\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int valueIndex = coords[1];\n\n         float value = getValues(batch, valueIndex);\n\n         setOutput(float(findBound(batch, value)));\n       }\n     `}}const d$={kernelName:mn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sortedSequence:a,values:s}=t,{side:o}=r,i=new c$(a.shape[0],a.shape[1],s.shape[1],o),u=[[a.shape[1]]];return n.runWebGLProgram(i,[a,s],"int32",u)}};class p${constructor(e,t,n){let r,a;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error(`Where for rank ${n} is not yet supported`);if(1===n)a="resRC",r="resRC";else{const n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[],o=[];for(let r=0;r<t.length;r++)o.push(`${n[r]}`),r<e&&s.push(`${n[r]}`);r=s.join(),a=o.join()}const s=ob(n);this.userCode=`\n      void main() {\n        ${s} resRC = getOutputCoords();\n        float cVal = getC(${r});\n        if (cVal >= 1.0) {\n          setOutput(getA(${a}));\n        } else {\n          setOutput(getB(${a}));\n        }\n      }\n    `}}const h$={kernelName:gn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{condition:r,t:a,e:s}=t,o=new p$(r.shape.length,a.shape,a.shape.length);return n.runWebGLProgram(o,[r,a,s],da(a.dtype,s.dtype))}},f$=Mk({opSnippet:`\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = 1.7580993408473768;\n  float scale = ${qp};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`}),m$={kernelName:yn,backendName:"webgl",kernelFunc:f$},g$=Mk({opSnippet:Dk+"\n  return 1.0 / (1.0 + exp(-1.0 * x));\n",packedOpSnippet:"\n  vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n  bvec4 isNaN = isnan(x);\n\n  result.r = isNaN.r ? x.r : result.r;\n  result.g = isNaN.g ? x.g : result.g;\n  result.b = isNaN.b ? x.b : result.b;\n  result.a = isNaN.a ? x.a : result.a;\n\n  return result;\n",cpuKernelImpl:Lw}),y$={kernelName:kn,backendName:"webgl",kernelFunc:g$},b$=Mk({opSnippet:"\n  if (isnan(x)) { return 0.0; }\n  return sign(x);\n"}),x$={kernelName:wn,backendName:"webgl",kernelFunc:b$},v$=Mk({opSnippet:Dk+"\n  return sin(x);\n",packedOpSnippet:`\n  vec4 result = sin(x);\n  bvec4 isNaN = isnan(x);\n  ${Ik}\n  return result;\n`}),w$={kernelName:xn,backendName:"webgl",kernelFunc:v$},k$=Mk({opSnippet:"\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n"}),I$={kernelName:vn,backendName:"webgl",kernelFunc:k$},N$=Mk({opSnippet:"\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n"}),S$={kernelName:In,backendName:"webgl",kernelFunc:N$},T$={kernelName:Tn,backendName:"webgl",kernelFunc:e=>{const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockShape:s,paddings:o}=r;u(a.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");const i=s.reduce((e,t)=>e*t),l=[[0,0]];l.push(...o);for(let u=1+s.length;u<a.shape.length;++u)l.push([0,0]);const c=[],d=TC({inputs:{x:a},backend:n,attrs:{paddings:l,constantValue:0}}),p=Wp(d.shape,s,i,!1),h=zp(p.length,s.length,!1),f=Up(d.shape,s,i,!1),m=jk({inputs:{x:d},backend:n,attrs:{shape:p}}),g=nI({inputs:{x:m},backend:n,attrs:{perm:h}}),y=jk({inputs:{x:g},backend:n,attrs:{shape:f}});return c.push(d),c.push(m),c.push(g),c.forEach(e=>n.disposeIntermediateTensorInfo(e)),y}};const C$={kernelName:En,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{indices:r,values:a,denseShape:s,defaultValue:o}=t;if(1!==s.shape.length)throw new Error(`Dense shape must be a vector, saw:\n         ${s.shape}`);if(2!==r.shape.length)throw new Error(`Indices must be a matrix, saw:\n         ${r.shape}`);if(1!==a.shape.length)throw new Error(`Values must be a vector, saw:\n         ${a.shape}`);if(0!==o.shape.length)throw new Error(`Default value must be a scalar, saw:\n        ${o.shape}`);const i=n.readSync(r.dataId),u=n.readSync(a.dataId),l=n.readSync(s.dataId),c=n.readSync(o.dataId)[0],[d,p,h,f,m]=Ww(i,r.shape,r.dtype,u,a.dtype,l,c);return[n.makeTensorInfo(p,r.dtype,d),n.makeTensorInfo([p[0]],a.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(e=>Number(e)))),n.makeTensorInfo([m.length],r.dtype,new Int32Array(m))]}};const $$={kernelName:Rn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{inputIndices:r,inputShape:a,newShape:s}=t;if(2!==r.shape.length)throw new Error(`Input indices should be a matrix but received shape ${r.shape}`);if(1!==a.shape.length)throw new Error(`Input shape should be a vector but received shape ${a.shape}`);if(1!==s.shape.length)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);const o=Array.from(n.readSync(a.dataId)),i=n.readSync(r.dataId),u=Array.from(n.readSync(s.dataId)),[l,c,d]=zw(i,r.shape,r.dtype,o,u);return[n.makeTensorInfo(c,r.dtype,l),n.makeTensorInfo([d.length],s.dtype,new Int32Array(d))]}};const E$={kernelName:_n,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:s}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.shape.length)throw new Error(`Indices should be a vector but received shape\n              ${a.shape}`);if(1!==s.shape.length)throw new Error(`Segment ids should be a vector but received shape\n              ${s.shape}`);const o=n.readSync(r.dataId),i=n.readSync(a.dataId),u=n.readSync(s.dataId),[l,c]=Uw(o,r.shape,r.dtype,i,u,!0);return n.makeTensorInfo(c,r.dtype,l)}};const R$={kernelName:An,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n}=e,{data:r,indices:a,segmentIds:s}=t;if(r.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(1!==a.shape.length)throw new Error(`Indices should be a vector but received shape\n             ${a.shape}`);if(1!==s.shape.length)throw new Error(`Segment ids should be a vector but received shape\n             ${s.shape}`);const o=n.readSync(r.dataId),i=n.readSync(a.dataId),u=n.readSync(s.dataId),[l,c]=Uw(o,r.shape,r.dtype,i,u);return n.makeTensorInfo(c,r.dtype,l)}};const _$={kernelName:On,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{sparseIndices:a,sparseValues:s,defaultValue:o}=t,{outputShape:i}=r,{sliceRank:u,numUpdates:l,sliceSize:c,strides:d,outputSize:p}=hc(0,a,i),h=!1;if("string"===s.dtype){const e=n.bufferSync(a),t=n.bufferSync(s),r=Pr(n.readSync(o.dataId)[0]),f=Pw(e,t,i,p,c,l,u,d,r,h);return n.makeTensorInfo(i,f.dtype,f.values)}const f=new i$(l,u,a.shape.length,s.shape.length,d,[p,1],h),m=n.runWebGLProgram(f,[s,a,o],s.dtype),g=jk({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(m),g}};const A$={kernelName:Cn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{numOrSizeSplits:s,axis:o}=r,i=b(o,a.shape)[0],u=gh(a,s,i),l=a.shape.length,c=new Array(l).fill(0),d=a.shape.slice();return u.map(e=>{const t=[...d];t[i]=e;const r=ZI({inputs:{x:a},backend:n,attrs:{begin:c,size:t}});return c[i]+=e,r})}},O$="return sqrt(x);",F$=Mk({opSnippet:O$,packedOpSnippet:O$,cpuKernelImpl:Gw}),D$={kernelName:Nn,backendName:"webgl",kernelFunc:F$},M$={kernelName:Dn,backendName:"webgl",kernelFunc:Mk({opSnippet:"return x * x;"})},P$="return (a - b) * (a - b);",L$=Pk({opSnippet:P$,packedOpSnippet:P$}),B$={kernelName:Fn,backendName:"webgl",kernelFunc:L$};const V$={kernelName:Mn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t;if("string"!==a.dtype)throw new Error("Input must be of datatype string");const s=Rh(n.readSync(a.dataId)),o=Hw(s,"string",r);return n.makeTensorInfo(a.shape,"string",o)}};const W$={kernelName:Zn,backendName:"webgl",kernelFunc:function({inputs:e,attrs:t,backend:n}){const{x:r}=e,a=ck+`\n    return x > 0.0 ? 1.0 : float(${t.alpha});\n  `,s=new lk(r.shape,a);return n.runWebGLProgram(s,[r],r.dtype)}};class z${constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;const r=n.length,a=ob(n.length),s=ob(n.length);let o="";if(1===r)o="coords * strides + begin";else{let e=0;o=n.map((t,r)=>(e++,1===n.length?`coords * strides[${r}] + begin[${r}]`:`coords[${e-1}] * strides[${r}] + begin[${r}]`)).join(",")}this.userCode=`\n      ${a} begin = ${a}(${e});\n      ${a} strides = ${a}(${t});\n\n      void main() {\n        ${s} coords = getOutputCoords();\n        setOutput(getX(${o}));\n      }\n    `}}const U$={kernelName:Pn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{begin:s,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:h}=r,{finalShapeSparse:f,finalShape:m,isIdentity:g,sliceDim0:y,isSimpleSlice:b,begin:x,end:v,strides:w}=Cp(a.shape,s,o,i,l,c,d,p,h);let k;if(g)k=jk({inputs:{x:a},backend:n,attrs:{shape:m}});else if(y||b){u(a.shape.length>=1,()=>`Input must have rank at least 1, got: ${a.shape.length}`);const e=Ip(x,v,w),t=ZI({inputs:{x:a},backend:n,attrs:{begin:x,size:e}});k=jk({inputs:{x:t},backend:n,attrs:{shape:m}}),n.disposeIntermediateTensorInfo(t)}else{if(n.shouldExecuteOnCPU([a])){const e=n.readSync(a.dataId),t=Ls(a.shape,a.dtype,e),r=jw(f,t,w,x);k=n.makeTensorInfo(m,a.dtype,r.values)}else{const e=new z$(x,w,f);k=n.runWebGLProgram(e,[a],a.dtype)}}const I=jk({inputs:{x:k},backend:n,attrs:{shape:m}});return n.disposeIntermediateTensorInfo(k),I}};const G$={kernelName:Ln,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{separator:a,nGramWidths:s,leftPad:o,rightPad:i,padWidth:u,preserveShortSequences:l}=r,{data:c,dataSplits:d}=t,p=n.readSync(c.dataId),h=n.readSync(d.dataId),[f,m]=qw(p,h,a,s,o,i,u,l);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}};const H$={kernelName:Bn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{skipEmpty:a}=r,{input:s,delimiter:o}=t;if("string"!==s.dtype)throw new Error("Input must be of datatype string");if(1!==s.shape.length)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(0!==o.shape.length)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);const i=n.readSync(s.dataId),u=n.readSync(o.dataId)[0],[l,c,d]=Kw(i,u,a),p=c.length;return[n.makeTensorInfo([p,2],"int32",l),n.makeTensorInfo([p],"string",c),n.makeTensorInfo([2],"int32",new Int32Array(d))]}};const j$={kernelName:Vn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{numBuckets:a}=r,{input:s}=t;if("string"!==s.dtype)throw new Error("Input must be of datatype string");if(a<=0)throw new Error("Number of buckets must be at least 1");const o=n.readSync(s.dataId),i=Xw(o,a);return n.makeTensorInfo(s.shape,"int32",i)}},q$=Mk({opSnippet:"return tan(x);"}),K$={kernelName:zn,backendName:"webgl",kernelFunc:q$},X$=Mk({opSnippet:"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n"}),Y$={kernelName:Un,backendName:"webgl",kernelFunc:X$};const Q$={kernelName:fn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{tensor:a,indices:s,updates:o}=t,{sliceRank:i,numUpdates:u,sliceSize:l,strides:c,outputSize:d}=hc(0,s,a.shape),p=[d/l,l];if(0===d)return n.makeTensorInfo(a.shape,s.dtype);const h=jk({inputs:{x:s},backend:n,attrs:{shape:[u,i]}}),f=jk({inputs:{x:o},backend:n,attrs:{shape:[u,l]}}),m=jk({inputs:{x:a},backend:n,attrs:{shape:p}}),g=new i$(u,i,h.shape.length,f.shape.length,c,p,!1,!0),y=n.runWebGLProgram(g,[f,h,m],m.dtype),b=jk({inputs:{x:y},backend:n,attrs:{shape:a.shape}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),b}};class Z${constructor(e,t){this.variableNames=["A"];const n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;const r=ob(this.rank),a=function(e){const t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(1===t)return`imod(resRC, ${e[0]})`;const n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[];for(let a=0;a<e.length;a++)r.push(`imod(${n[a]}, ${e[a]})`);return r.join()}(e);this.userCode=`\n      void main() {\n        ${r} resRC = getOutputCoords();\n        setOutput(getA(${a}));\n      }\n    `}}function J$(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{reps:s}=r;if("string"===a.dtype||a.shape.length>5){const e=n.readSync(a.dataId),t="string"===a.dtype?e.map(e=>Pr(e)):e,r=Ls(a.shape,a.dtype,t),o=Qw(r,s);return n.makeTensorInfo(o.shape,o.dtype,o.values)}const o=new Z$(a.shape,s);return n.runWebGLProgram(o,[a],a.dtype)}const eE={kernelName:Gn,backendName:"webgl",kernelFunc:J$};class tE{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode="\n       void main() {\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // We compare elements pair-wise within a group of size 2 * inc.\n         // The comparing rule for each group alternates between ascending\n         // and descending. Within each group, we compare each pair at\n         // positions i and i+inc. To decide whether an element at position i\n         // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n         // inc, it is in the first half of the group, we denote it as x0,\n         // otherwise we denote it as x1.\n         // For example, as shown in the Bitonic top K paper referenced above,\n         // Figure5(a) shows that element[1] is in the\n         // second half of the group when group size is 2, but it is in the\n         // first half of the group when group size is 4.\n\n         bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n         int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n         float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n         float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n         // Denotes which direction indices are in (ascending or descending).\n         bool reverse = imod(elemIdx, 2 * dir) >= dir;\n         bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n         if (reverse == isGreater) { // Elements in opposite order of direction\n           int iTemp = i0;\n           i0 = i1;\n           i1 = iTemp;\n         }\n         if (isFirstInPair) {\n            setOutput(float(i0));\n         } else {\n            setOutput(float(i1));\n         }\n       }\n     "}}class nE{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode="\n    void main() {\n         // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n         ivec2 coords = getOutputCoords();\n         int batch = coords[0];\n         int elemIdx = coords[1];\n\n         // The output size is half of the previous size.\n         // If the previous sequence is | | | | _ _ _ _  | | | |  _ _ _ _ (k=4),\n         // we only need to output the indices at positions |, the indices at\n         // positions _ can be thrown away, see Figure5(b) After Phase 2\n         // (Merge phase) in the Bitonic Top K paper referenced above.\n         // For example, the paper shows we only need to output the orange bars.\n         // The output sequence should look like this | | | | | | | |.\n         // Because the sequence is halved, to map the output index back\n         // to the previous sequence to find the corresponding value,\n         // we need to double the index. When we double the index,\n         // we basically interpolate a position, so 2i looks like\n         // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n         // of each 2k positions by - elemIdx % k. E.g. for output at\n         // index 4,5,6,7, we want to get the corresponding element at\n         // original index 8,9,10,11, for output at index 8,9,10,11,\n         // we want to get the corresponding element at original index\n         // 16,17,18,19, so on and so forth.\n\n         int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n         int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n         int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n         float x0 = getX(batch, i0);\n         float x1 = i1 < n ? getX(batch, i1) : x0;\n\n         setOutput(x0 >= x1 ? float(i0) : float(i1));\n       }\n     "}}function rE(e,t){null!==t&&e.disposeIntermediateTensorInfo(t)}function aE(e){let t=1;for(;t<e;)t*=2;return t}const sE={kernelName:Hn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{k:s,sorted:o}=r,i=W().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),u=W().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),l=a.shape,c=l[l.length-1];if(n.shouldExecuteOnCPU([a])||c<i||s>u){const e=n.readSync(a.dataId),[t,r]=Zw(e,l,a.dtype,s,o);return[n.makeTensorInfo(t.shape,t.dtype,t.values),n.makeTensorInfo(r.shape,r.dtype,r.values)]}if(0===s)return l[l.length-1]=0,[n.makeTensorInfo(l,a.dtype,[]),n.makeTensorInfo(l,"int32",[])];if(1===c)return[a,PS({attrs:{shape:l,dtype:"int32",value:0},backend:n})];const p=n.texData.get(a.dataId),h=null!==p&&p.isPacked,f=h?n.unpackTensor(a):a,m=d(l)/c,g=jk({inputs:{x:f},attrs:{shape:[m,c]},backend:n});h&&rE(n,f);const y=aE(s),b=aE(c);let x=null;const v=()=>null===x?[g,g]:[g,x],w=(e,t,r)=>{const a=v(),s=new tE(r),o=[[c],[null===x?1:0],[Number.NEGATIVE_INFINITY],[e],[t]],i=x;x=n.runWebGLProgram(s,a,"int32",o),rE(n,i)};for(let d=1;d<y;d*=2){const e=2*d;for(let t=d;t>=1;t/=2)w(e,t,[m,b])}for(let d=b;d>y;d/=2){const e=v(),t=new nE([m,d/2]),r=[[c],[null===x?1:0],[y]],a=x;x=n.runWebGLProgram(t,e,"int32",r),rE(n,a);const s=y/2,o=2*s;for(let n=s;n>=1;n/=2)w(o,n,x.shape)}let k=x;x=ZI({inputs:{x:x},backend:n,attrs:{begin:0,size:[m,s]}}),rE(n,k);let I=nT({inputs:{x:g,indices:x},backend:n,attrs:{axis:1,batchDims:1}});rE(n,g);const N=l.slice(0,-1);N.push(s),k=x,x=jk({inputs:{x:x},attrs:{shape:N},backend:n}),rE(n,k);const S=I;return I=jk({inputs:{x:I},attrs:{shape:N},backend:n}),rE(n,S),[I,x]}};class oE{constructor(e,t,n,r,a,s){this.variableNames=["Image","Transforms"],this.outputShape=s;const o="nearest"===n?1:2;let i;switch(r){case"constant":default:i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4}this.userCode=`\n            float mapCoord(float outCoord, float len) {\n              float inCoord = outCoord;\n              if(${i} == 2) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    if (inCoord < sz2) {\n                      inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n                      inCoord;\n                    }\n                    inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz2 = 2.0 * len;\n                    inCoord -= sz2 * float(int(float(inCoord / sz2)));\n                    if (inCoord >= len) {\n                      inCoord = sz2 - inCoord - 1.0;\n                    }\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${i} == 3) {\n                if (inCoord < 0.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n                  }\n                } else if (inCoord > len - 1.0) {\n                  if (len <= 1.0) {\n                    inCoord = 0.0;\n                  } else {\n                    float sz = len - 1.0;\n                    inCoord -= len * float(int(float(inCoord / sz)));\n                  }\n                }\n                return clamp(inCoord, 0.0, len - 1.0);\n              } else if (${i} == 4) {\n                return clamp(outCoord, 0.0, len - 1.0);\n              } else {\n                return outCoord;\n              }\n            }\n\n            float readWithFillValue(int batch, int coordY, int coordX,\n              int channel) {\n              float outputValue;\n              if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n                  outputValue = getImage(batch, coordY, coordX, channel);\n              } else {\n                outputValue = float(${a});\n              }\n              return outputValue;\n            }\n\n            void main() {\n              ivec4 coords = getOutputCoords();\n              float outputValue;\n              int batch = coords[0];\n              int x = coords[2];\n              int y = coords[1];\n              int channel = coords[3];\n              float xf = float(x);\n              float yf = float(y);\n              float a1 = getTransforms(batch, 0);\n              float a2 = getTransforms(batch, 1);\n              float a3 = getTransforms(batch, 2);\n              float b1 = getTransforms(batch, 3);\n              float b2 = getTransforms(batch, 4);\n              float b3 = getTransforms(batch, 5);\n              float c1 = getTransforms(batch, 6);\n              float c2 = getTransforms(batch, 7);\n              float projection = c1 * xf + c2 * yf + 1.0;\n              if (projection == 0.0) {\n                outputValue = float(${a});\n              } else {\n                float inX = (a1 * xf + a2 * yf + a3) / projection;\n                float inY = (b1 * xf + b2 * yf + b3) / projection;\n                float mapX = mapCoord(inX, float(${t}));\n                float mapY = mapCoord(inY, float(${e}));\n\n                if (${o} == 1) {\n                  int coordY = int(round(mapY));\n                  int coordX = int(round(mapX));\n                  outputValue = readWithFillValue(batch, coordY, coordX,\n                    channel);\n                } else {\n                  float yFloor = floor(mapY);\n                  float xFloor = floor(mapX);\n                  float yCeil = yFloor + 1.0;\n                  float xCeil = xFloor + 1.0;\n                  float valueYFloor = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yFloor), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yFloor), int(xCeil), channel);\n                  float valueYCeil = (xCeil - mapX) *\n                  readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n                  (mapX - xFloor) *\n                  readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n                  outputValue = (yCeil - mapY) * valueYFloor +\n                  (mapY - yFloor) * valueYCeil;\n                }\n              }\n              setOutput(outputValue);\n            }\n        `}}const iE={kernelName:jn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:a,transforms:s}=t,{interpolation:o,fillMode:i,fillValue:u,outputShape:l}=r,[c,d,p,h]=a.shape,[f,m]=null!=l?l:[d,p],g=new oE(d,p,o,i,u,[c,f,m,h]);return n.runWebGLProgram(g,[a,s],"float32")}};const uE={kernelName:Kn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,attrs:n,backend:r}=e,{axis:a}=n,{x:s}=t;Wy(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");const o=r.readSync(s.dataId),{outputValues:i,outputShape:u,indices:l}=ek(o,a,s.shape,s.dtype);return[r.makeTensorInfo(u,s.dtype,i),r.makeTensorInfo([l.length],"int32",l)]}};const lE={kernelName:Xn,backendName:"webgl",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{value:a}=t;let{axis:s}=r;s<0&&(s+=a.shape.length);const o=a,i=o.shape.length,u=a.shape[s],l=new Array(i-1);let c=0;for(let m=0;m<i;m++)m!==s&&(l[c++]=o.shape[m]);const d=[],p=new Array(i).fill(0),h=o.shape.slice();h[s]=1;const f=new Array(u);for(let m=0;m<f.length;m++){p[s]=m;const e=ZI({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),t=jk({inputs:{x:e},backend:n,attrs:{shape:l}});f[m]=t,d.push(e)}return d.forEach(e=>n.disposeIntermediateTensorInfo(e)),f}};class cE{constructor(e,t){this.variableNames=["x","segmentIds"];const n=e.windowSize,r=e.batchSize,a=e.inSize,s=e.numSegments,o=s*Math.ceil(a/n);this.outputShape=[r,o];const i=4*Math.floor(n/4),u=n%4,l="\n        sumValue += dot(values, segFilter);\n    ";let c="";a%n>0&&(c=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return initializationValue;\n        }\n      `);let d="";a%n>0&&(d=`\n        if (inIdx < 0 || inIdx >= ${a}) {\n          return -1.0;\n        }\n      `),this.userCode=`\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        ${c}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${d}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${s})) * float(${n}));\n        int currentSeg = int(mod(float(outIdx), float(${s})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${i}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${l}\n        }\n\n        int inIdx = inOffset + ${i};\n        if (${1===u}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 segFilter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n  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c=io(s.shape,o,i,1,u,l),d=c.strideDepth,p=c.strideHeight,h=c.strideWidth,f=c.filterDepth,m=c.filterHeight,g=c.filterWidth,y=c.dilationDepth,b=c.dilationHeight,x=c.dilationWidth,v=c.effectiveFilterDepth,w=c.effectiveFilterHeight,k=c.effectiveFilterWidth,I=v-1-c.padInfo.front,N=k-1-c.padInfo.left,S=w-1-c.padInfo.top,T=Ls(s.shape,"float32"),C=1/(f*m*g),$=n.bufferSync(a);for(let E=0;E<c.batchSize;++E)for(let e=0;e<c.inChannels;++e)for(let t=0;t<c.inDepth;++t)for(let n=0;n<c.inHeight;++n)for(let r=0;r<c.inWidth;++r){const a=t-I,s=n-S,o=r-N;let i=0;for(let t=0;t<v;t+=y){const n=(a+t)/d;if(!(n<0||n>=c.outDepth||Math.floor(n)!==n))for(let t=0;t<w;t+=b){const r=(s+t)/p;if(!(r<0||r>=c.outHeight||Math.floor(r)!==r))for(let t=0;t<k;t+=x){const a=(o+t)/h;if(a<0||a>=c.outWidth||Math.floor(a)!==a)continue;i+=$.get(E,n,r,a,e)}}}T.set(i*C,E,t,n,r,e)}return n.makeTensorInfo(T.shape,T.dtype,T.values)}};const aR={kernelName:ie,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,input:s}=t,o=s;Ab([a,s],"avgPoolGrad");const{filterSize:i,strides:u,pad:l}=r,c=oo(o.shape,i,u,1,l),d=c.strideHeight,p=c.strideWidth,h=c.filterHeight,f=c.filterWidth,m=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,x=b-1-c.padInfo.left,v=y-1-c.padInfo.top,w=Ls(o.shape,"float32"),k=1/(h*f),I=n.data.get(a.dataId).values,N=Ls(a.shape,"float32",I);for(let S=0;S<c.batchSize;++S)for(let e=0;e<c.inChannels;++e)for(let t=0;t<c.inHeight;++t)for(let n=0;n<c.inWidth;++n){const r=t-v,a=n-x;let s=0;for(let t=0;t<y;t+=m){const n=(r+t)/d;if(!(n<0||n>=c.outHeight||Math.floor(n)!==n))for(let t=0;t<b;t+=g){const r=(a+t)/p;if(r<0||r>=c.outWidth||Math.floor(r)!==r)continue;s+=N.get(S,n,r,e)}}w.set(s*k,S,t,n,e)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}};const sR={kernelName:nt,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,scale:s,offset:o,mean:i,variance:l}=t;u(i.shape.length===l.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),u(null==o||i.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),u(null==s||i.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),Ab([a,i,l,s,o],"batchNorm");let{varianceEpsilon:c}=r;null==c&&(c=.001);const d=n.data.get(a.dataId).values,p=n.data.get(i.dataId).values,h=n.data.get(l.dataId).values,f=s?n.data.get(s.dataId).values:new Float32Array([1]),m=o?n.data.get(o.dataId).values:new Float32Array([0]),g=new Float32Array(d.length),y=m.length,b=f.length,x=h.length,v=p.length;let w=0,k=0,I=0,N=0;for(let 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n.makeTensorInfo([o],s.dtype,i)}};const uR={kernelName:fe,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{s0:r,s1:a}=t,s=n.data.get(r.dataId).values,o=n.data.get(a.dataId).values,i=ci(Array.from(s),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}},lR=ax(ye,(e,t)=>{const n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),cR={kernelName:ye,backendName:"cpu",kernelFunc:lR},dR={kernelName:xe,backendName:"cpu",kernelFunc:e=>{const{x:t}=e.inputs,n=e.backend,r=new Float32Array(d(t.shape)),a=n.data.get(t.dataId),s=a.complexTensorInfos.real,o=a.complexTensorInfos.imag,i=n.data.get(s.dataId).values,u=n.data.get(o.dataId).values;for(let l=0;l<i.length;l++){const e=i[l],t=u[l];r[l]=Math.hypot(e,t)}return n.makeOutput(r,t.shape,"float32")}};function pR(e){const{inputs:t,backend:n}=e,{input:r}=t,a=n.data.get(r.dataId).complexTensorInfos.imag,s=n.data.get(a.dataId).values;return n.makeTensorInfo(a.shape,a.dtype,s)}const hR={kernelName:lt,backendName:"cpu",kernelFunc:pR};function fR(e){const{inputs:t,backend:n,attrs:r}=e,{axis:a}=r,s=b(a,t[0].shape)[0],o=t.map(e=>e.shape);_p(o,s);let i=Ap(t.map(e=>e.shape),s);if(0===d(i))return n.makeTensorInfo(i,t[0].dtype,[]);const u=t.filter(e=>d(e.shape)>0);if(1===u.length)return Bb({inputs:{x:u[0]},backend:n});if("complex64"===u[0].dtype){const e=u.map(e=>Wb({inputs:{input:e},backend:n})),t=u.map(e=>pR({inputs:{input:e},backend:n})),r=fR({inputs:e,backend:n,attrs:{axis:s}}),a=fR({inputs:t,backend:n,attrs:{axis:s}}),o=Mb({inputs:{real:r,imag:a},backend:n});return e.forEach(e=>n.disposeIntermediateTensorInfo(e)),t.forEach(e=>n.disposeIntermediateTensorInfo(e)),n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),o}const l=u.map(e=>{const t=d(e.shape.slice(s));return CE({inputs:{x:e},backend:n,attrs:{shape:[-1,t]}})}),c=l.map(e=>({vals:n.data.get(e.dataId).values,shape:e.shape}));i=Ap(l.map(e=>e.shape),1);const p=1===l[0].shape[0],h=lx(c,i,t[0].dtype,p),f=Ap(u.map(e=>e.shape),s),m=n.makeTensorInfo(f,t[0].dtype,h);return l.forEach(e=>n.disposeIntermediateTensorInfo(e)),m}const mR={kernelName:ve,backendName:"cpu",kernelFunc:fR};function gR(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:o,pad:i,dataFormat:u,dilations:l,dimRoundingMode:c}=r;Ab([a,s],"conv2d");const d=xo(u),p=uo(a.shape,s.shape,o,l,i,c,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,b=p.padInfo.top,x="channelsLast"===p.dataFormat,v=new Kr(p.outShape,a.dtype),w=$(a.shape),k=$(s.shape),I=w[0],N=x?w[1]:w[2],S=x?w[2]:1,T=x?1:w[1],C=v.strides[0],E=x?v.strides[1]:v.strides[2],R=x?v.strides[2]:1,_=x?1:v.strides[1],A=n.data.get(a.dataId).values,O=n.data.get(s.dataId).values,F=v.values;for(let $=0;$<p.batchSize;++$){const e=$*I,t=$*C;for(let n=0;n<p.outHeight;++n){const r=t+n*E,a=n*p.strideHeight-b;for(let t=0;t<h;++t){const n=a+t*m;if(n<0||n>=p.inHeight)continue;const s=t*k[0],o=e+n*N;for(let e=0;e<p.outWidth;++e){const t=r+e*R,n=e*p.strideWidth-y;for(let e=0;e<f;++e){const r=n+e*g;if(r<0||r>=p.inWidth)continue;const a=o+r*S;let i=s+e*k[1];for(let e=0;e<p.inChannels;++e){const n=A[a+e*T];for(let e=0;e<p.outChannels;++e)F[t+e*_]+=n*O[i+e];i+=p.outChannels}}}}}}return n.makeTensorInfo(v.shape,v.dtype,F)}const yR={kernelName:we,backendName:"cpu",kernelFunc:gR};const bR={kernelName:ke,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:o,pad:i,dataFormat:u,dimRoundingMode:l,filterShape:c}=r;Ab([a,s],"conv2dBackpropFilter");const d=xo(u),p=uo(a.shape,c,o,1,i,l,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,y="channelsLast"===p.dataFormat,b=new Kr(p.filterShape,"float32"),x=p.padInfo.left,v=p.padInfo.top,w=n.data.get(a.dataId).values,k=n.data.get(s.dataId).values,I=new Kr(a.shape,a.dtype,w),N=new Kr(s.shape,s.dtype,k);for(let S=0;S<m;++S){const e=Math.max(0,Math.ceil((v-S)/h)),t=Math.min(p.outHeight,(p.inHeight+v-S)/h);for(let n=0;n<g;++n){const r=Math.max(0,Math.ceil((x-n)/f)),a=Math.min(p.outWidth,(p.inWidth+x-n)/f);for(let s=0;s<p.inChannels;++s)for(let o=0;o<p.outChannels;++o){let i=0;for(let u=0;u<p.batchSize;++u)for(let l=e;l<t;++l){const e=S+l*h-v;for(let t=r;t<a;++t){const r=n+t*f-x;i+=y?I.get(u,e,r,s)*N.get(u,l,t,o):I.get(u,s,e,r)*N.get(u,o,l,t)}}b.set(i,S,n,s,o)}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}};const xR={kernelName:Ie,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{inputShape:o,strides:i,pad:u,dataFormat:l,dimRoundingMode:c}=r;Ab([a,s],"conv2dBackpropInput");const d=$(s.shape),p=$(a.shape);let h=xo(l);const f=uo(o,s.shape,i,1,u,c,!1,h),m=new 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vR={kernelName:Ne,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:o,pad:i,dilations:u}=r;Ab([a,s],"conv3d");const l=lo(a.shape,s.shape,o,u,i),{filterDepth:c,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=l,y=g.front,b=g.left,x=g.top,v=new Kr(l.outShape,a.dtype),w=n.data.get(a.dataId).values,k=n.data.get(s.dataId).values,I=v.values,N=$(a.shape),S=$(s.shape);for(let T=0;T<l.batchSize;++T){const e=T*N[0],t=T*v.strides[0];for(let n=0;n<l.outDepth;++n){const r=t+n*v.strides[1],a=n*l.strideDepth-y;for(let t=0;t<c;++t){const n=a+t*h;if(n<0||n>=l.inDepth)continue;const s=t*S[0],o=e+n*N[1];for(let e=0;e<l.outHeight;++e){const t=r+e*v.strides[2],n=e*l.strideHeight-x;for(let e=0;e<d;++e){const r=n+e*f;if(r<0||r>=l.inHeight)continue;const a=s+e*S[1],i=o+r*N[2];for(let e=0;e<l.outWidth;++e){const n=t+e*l.outChannels,r=e*l.strideWidth-b;for(let e=0;e<p;++e){const 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a=Math.max(0,Math.ceil((P-r)/h)),s=Math.min(d.outHeight,(d.inHeight+P-r)/h),o=r*w+n;for(let n=0;n<y;++n){const i=Math.max(0,Math.ceil((M-n)/f)),u=Math.min(d.outWidth,(d.inWidth+M-n)/f),l=n*k+o;for(let o=0;o<d.inChannels;++o){const c=o*I+l;for(let l=0;l<d.outChannels;++l){let m=0;for(let c=0;c<d.batchSize;++c){const d=c*_,g=c*S;for(let c=e;c<t;++c){const e=($+c*p-D)*A+d,t=c*T+g;for(let c=a;c<s;++c){const a=(r+c*h-P)*O+e,s=c*C+t;for(let e=i;e<u;++e){const t=e*E+s;m+=R[(n+e*f-M)*F+a+o]*N[t+l]}}}}x[c+l]=m}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}};const kR={kernelName:Te,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{pad:o,strides:i,inputShape:u}=r;Ab([a],"conv3dBackpropInputV2");const l=$(a.shape),c=$(s.shape),d=lo(u,s.shape,i,1,o),p=new Kr(d.inShape,"float32"),h=p.values,[f,m,g,y]=p.strides,b=n.data.get(a.dataId).values,[x,v,w,k]=l,I=n.data.get(s.dataId).values,[N,S,T,C]=c,{batchSize:E,filterDepth:R,filterHeight:_,filterWidth:A,inChannels:O,inDepth:F,inHeight:D,inWidth:M,outChannels:P,outDepth:L,outHeight:B,outWidth:V,strideDepth:W,strideHeight:z,strideWidth:U}=d,G=R-1-d.padInfo.front,H=_-1-d.padInfo.top,j=A-1-d.padInfo.left;for(let $=0;$<E;++$)for(let e=0;e<O;++e)for(let t=0;t<F;++t){const n=t-G,r=Math.max(0,Math.ceil(n/W)),a=Math.min(L,(R+n)/W);for(let s=0;s<D;++s){const o=s-H,i=Math.max(0,Math.ceil(o/z)),u=Math.min(B,(_+o)/z);for(let l=0;l<M;++l){const c=l-j,d=Math.max(0,Math.ceil(c/U)),p=Math.min(V,(A+c)/U);let E=0;for(let t=r;t<a;++t){const r=t*W-n;for(let n=i;n<u;++n){const a=n*z-o;for(let s=d;s<p;++s){const o=x*$+v*t+w*n+k*s,i=N*(R-1-r)+S*(_-1-a)+T*(A-1-(s*U-c))+C*e;for(let e=0;e<P;++e){E+=b[o+e]*I[i+e]}}}}h[f*$+m*t+g*s+y*l+e]=E}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}},IR=ax(Ce,e=>Math.cos(e)),NR={kernelName:Ce,backendName:"cpu",kernelFunc:IR},SR=ax($e,e=>Math.cosh(e)),TR={kernelName:$e,backendName:"cpu",kernelFunc:SR};const CR={kernelName:_e,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:a,boxes:s,boxInd:o}=t,{cropSize:i,method:u,extrapolationValue:l}=r,[c,d,p,h]=a.shape,f=s.shape[0],[m,g]=i,y=Ls([f,m,g,h],"float32"),b=n.data.get(s.dataId).values,x=n.data.get(o.dataId).values,v=n.data.get(a.dataId).values,w=$(a.shape),k=$(y.shape);for(let I=0;I<f;I++){const e=4*I,t=b[e],n=b[e+1],r=b[e+2],a=b[e+3],s=x[I];if(s>=c)continue;const o=m>1?(r-t)*(d-1)/(m-1):0,i=g>1?(a-n)*(p-1)/(g-1):0;for(let c=0;c<m;c++){const e=m>1?t*(d-1)+c*o:.5*(t+r)*(d-1);if(e<0||e>d-1)for(let t=0;t<g;t++)for(let e=0;e<h;e++){const n=e+t*k[2]+c*k[1]+I*k[0];y.values[n]=l}else if("bilinear"===u){const t=Math.floor(e),r=Math.ceil(e),o=e-t;for(let e=0;e<g;e++){const u=g>1?n*(p-1)+e*i:.5*(n+a)*(p-1);if(u<0||u>p-1){for(let t=0;t<h;t++){const n=t+e*k[2]+c*k[1]+I*k[0];y.values[n]=l}continue}const d=Math.floor(u),f=Math.ceil(u),m=u-d;for(let n=0;n<h;n++){let a=n+d*w[2]+t*w[1]+s*w[0];const i=v[a];a=n+f*w[2]+t*w[1]+s*w[0];const u=v[a];a=n+d*w[2]+r*w[1]+s*w[0];const l=v[a];a=n+f*w[2]+r*w[1]+s*w[0];const p=i+(u-i)*m,h=l+(v[a]-l)*m;a=n+e*k[2]+c*k[1]+I*k[0],y.values[a]=p+(h-p)*o}}}else for(let t=0;t<g;++t){const r=g>1?n*(p-1)+t*i:.5*(n+a)*(p-1);if(r<0||r>p-1){for(let e=0;e<h;e++){const n=e+t*k[2]+c*k[1]+I*k[0];y.values[n]=l}continue}const o=Math.round(r),u=Math.round(e);for(let e=0;e<h;e++){const n=e+o*w[2]+u*w[1]+s*w[0],r=e+t*k[2]+c*k[1]+I*k[0];y.values[r]=v[n]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}};const $R={kernelName:Ee,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:o,reverse:i}=r;Ab(a,"cumprod");const u=Si([s],a.shape.length);let l=a;null!=u&&(l=ov({inputs:{x:a},backend:n,attrs:{perm:u}}));const c=Ci(1,a.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumprod in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);const p=da(l.dtype,"int32"),h=_(d(l.shape),p),f=n.data.get(l.dataId).values,m=l.shape[l.shape.length-1],g=i?(e,t)=>e+m-t-1:(e,t)=>e+t;for(let d=0;d<f.length;d+=m)for(let e=0;e<m;e++){const t=g(d,e);if(0===e)h[t]=o?1:f[t];else{const n=g(d,e-1);h[t]=o?f[n]*h[n]:f[t]*h[n]}}const y=n.makeTensorInfo(l.shape,p,h);if(null!=u){const e=ov({inputs:{x:y},backend:n,attrs:{perm:Ti(u)}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(l),e}return y}};const ER={kernelName:Re,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{axis:s,exclusive:o,reverse:i}=r;Ab(a,"cumsum");const u=Si([s],a.shape.length);let l=a;null!=u&&(l=ov({inputs:{x:a},backend:n,attrs:{perm:u}}));const c=Ci(1,a.shape.length)[0];if(c!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${c}`);const p=da(l.dtype,"int32"),h=A(d(l.shape),p),f=n.data.get(l.dataId).values,m=l.shape[l.shape.length-1],g=i?(e,t)=>e+m-t-1:(e,t)=>e+t;for(let d=0;d<f.length;d+=m)for(let e=0;e<m;e++){const t=g(d,e);if(0===e)h[t]=o?0:f[t];else{const n=g(d,e-1);h[t]=o?f[n]+h[n]:f[t]+h[n]}}const y=n.makeTensorInfo(l.shape,p,h);if(null!=u){const e=ov({inputs:{x:y},backend:n,attrs:{perm:Ti(u)}});return n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(l),e}return y}};const RR={kernelName:Ae,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,weights:s}=t,{size:o,binaryOutput:i}=r;if(1===a.shape.length){const e=Zb(n.data.get(a.dataId).values,n.data.get(s.dataId).values,s.dtype,s.shape,o);return n.makeTensorInfo([o],s.dtype,e)}if(2===a.shape.length){const e=Jb(n.bufferSync(a),n.bufferSync(s),o,i);return n.makeTensorInfo(e.shape,s.dtype,e.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${a.shape.length}.`)}};const _R={kernelName:Oe,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a}=t,{blockSize:s,dataFormat:o}=r;u("NHWC"===o,()=>`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${o}`);const i=a.shape[0],l=a.shape[1],c=a.shape[2],d=a.shape[3],p=l*s,h=c*s,f=d/(s*s),m=n.data.get(a.dataId).values,g=new Float32Array(i*p*h*f);let y=0;for(let u=0;u<i;++u)for(let e=0;e<p;++e){const t=Math.floor(e/s),n=e%s;for(let e=0;e<h;++e){const r=Math.floor(e/s),a=(n*s+e%s)*f;for(let e=0;e<f;++e){const n=e+a+d*(r+c*(t+l*u));g[y++]=m[n]}}}return n.makeTensorInfo([i,p,h,f],a.dtype,g)}};function AR(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,filter:s}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=r;Ab([a,s],"depthwiseConv2DNative");const d=$(a.shape),p=$(s.shape);let h=l;null==h&&(h=[1,1]),u(yo(o,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${h}'`);const f=uo(a.shape,s.shape,o,h,i,c,!0),{filterHeight:m,filterWidth:g,dilationHeight:y,dilationWidth:b,padInfo:x}=f,v=x.left,w=x.top,k=f.outChannels/f.inChannels,I=new Kr(f.outShape,a.dtype),N=n.data.get(a.dataId).values,S=n.data.get(s.dataId).values,T=I.values;for(let u=0;u<f.batchSize;++u){const e=u*d[0],t=u*I.strides[0];for(let n=0;n<f.outHeight;++n){const r=t+n*I.strides[1],a=n*f.strideHeight-w;for(let t=0;t<m;++t){const n=a+t*y;if(n<0||n>=f.inHeight)continue;const s=t*p[0],o=e+n*d[1];for(let e=0;e<f.outWidth;++e){const t=r+e*I.strides[2],n=e*f.strideWidth-v;for(let e=0;e<g;++e){const r=n+e*b;if(r<0||r>=f.inWidth)continue;const a=s+e*p[1],i=o+r*f.inChannels;let u=t,l=a;for(let e=0;e<f.inChannels;++e){const t=N[i+e];for(let e=0;e<k;++e)T[u+e]+=t*S[l+e];u+=k,l+=k}}}}}}return n.makeTensorInfo(I.shape,I.dtype,I.values)}const OR={kernelName:Fe,backendName:"cpu",kernelFunc:AR};const FR={kernelName:De,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{x:a,dy:s}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,filterShape:c}=r;Ab([a,s],"depthwiseConv2dNativeBackpropFilter");const d=uo(a.shape,c,o,i,u,l,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new Kr(d.filterShape,"float32"),y=d.padInfo.left,b=d.padInfo.top,x=d.outChannels/d.inChannels,v=n.data.get(a.dataId).values,w=new Kr(a.shape,a.dtype,v),k=n.data.get(s.dataId).values,I=new Kr(s.shape,s.dtype,k);for(let N=0;N<f;++N){const e=Math.max(0,Math.ceil((b-N)/p)),t=Math.min(d.outHeight,(d.inHeight+b-N)/p);for(let n=0;n<m;++n){const r=Math.max(0,Math.ceil((y-n)/h)),a=Math.min(d.outWidth,(d.inWidth+y-n)/h);for(let s=0;s<d.outChannels;++s){const o=Math.trunc(s/x),i=s%x;let u=0;for(let l=0;l<d.batchSize;++l)for(let i=e;i<t;++i){const e=N+i*p-b;for(let t=r;t<a;++t){const r=n+t*h-y;u+=w.get(l,e,r,o)*I.get(l,i,t,s)}}g.set(u,N,n,o,i)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}};const DR={kernelName:Me,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{dy:a,filter:s}=t,{strides:o,dilations:i,pad:u,dimRoundingMode:l,inputShape:c}=r;Ab([a,s],"depthwiseConv2DNativeBackpropInput");const d=$(a.shape),p=$(s.shape),h=uo(c,s.shape,o,i,u,l,!0),f=new Kr(h.inShape,"float32"),m=f.values,[g,y,b]=f.strides,x=n.data.get(a.dataId).values,[v,w,k]=d,I=n.data.get(s.dataId).values,[N,S,T]=p,{batchSize:C,filterHeight:E,filterWidth:R,inChannels:_,inHeight:A,inWidth:O,outChannels:F,outHeight:D,outWidth:M,strideHeight:P,strideWidth:L}=h,B=E-1-h.padInfo.top,V=R-1-h.padInfo.left,W=F/_;for(let $=0;$<C;++$)for(let e=0;e<_;++e)for(let t=0;t<A;++t){const n=t-B,r=Math.max(0,Math.ceil(n/P)),a=Math.min(D,(E+n)/P);for(let s=0;s<O;++s){const o=s-V,i=Math.max(0,Math.ceil(o/L)),u=Math.min(M,(R+o)/L);let l=0;for(let t=r;t<a;++t){const r=t*P-n;for(let n=i;n<u;++n){const a=v*$+w*t+k*n,s=N*(E-1-r)+S*(R-1-(n*L-o))+T*e;for(let t=0;t<W;++t){l+=x[a+(e*W+t)]*I[s+t]}}}m[g*$+y*t+b*s+e]=l}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}};const MR={kernelName:Pe,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n}=e,{x:r}=t,a=d(r.shape),s=n.data.get(r.dataId).values,o=Ls([a,a],r.dtype),i=o.values;for(let l=0;l<s.length;l++)i[l*a+l]=s[l];const u=[...r.shape,...r.shape];return n.makeTensorInfo(u,o.dtype,o.values)}},PR={kernelName:Le,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:a}=e,{strides:s,pad:o,dilations:i}=n,u=t,l=u.data.get(r.dataId).values,c=r.shape.length,p=u.data.get(a.dataId).values,h=a.shape.length,{batchSize:f,inHeight:m,inWidth:g,inChannels:y,outHeight:b,outWidth:x,padInfo:v,strideHeight:k,strideWidth:I,filterHeight:N,filterWidth:S,dilationHeight:T,dilationWidth:C,outShape:E}=so(r.shape,a.shape,s,o,"NHWC",i),R=d(E),_=E.length,A=w(r.dtype,R);for(let d=0;d<f;++d)for(let e=0;e<b;++e){const t=e*k-v.top;for(let n=0;n<x;++n){const s=n*I-v.left;for(let o=0;o<y;++o){let i=Number.MIN_SAFE_INTEGER;for(let e=0;e<N;++e){const n=t+e*T;if(n>=0&&n<m)for(let t=0;t<S;++t){const u=s+t*C;if(u>=0&&u<g){const s=D([d,n,u,o],c,$(r.shape)),f=D([e,t,o],h,$(a.shape)),m=l[s]+p[f];m>i&&(i=m)}}}A[D([d,e,n,o],_,$(E))]=i}}}return{dataId:u.write(Fr(A,r.dtype),E,r.dtype),shape:E,dtype:r.dtype}}},LR={kernelName:Ve,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:a,dy:s}=e,{strides:o,pad:i,dilations:l}=n,c=t,d=R(r.shape,c.data.get(r.dataId).values),p=R(a.shape,c.data.get(a.dataId).values),{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:v,strideWidth:w,filterHeight:k,filterWidth:I,dilationHeight:N,dilationWidth:S,outShape:T}=so(r.shape,a.shape,o,i,"NHWC",l);u(s.rank===T.length,()=>`Error in ${Ve}, dy must have the same rank as output ${T.length}, but got ${s.rank}`);const C=R(T,c.data.get(s.dataId).values),$=O(a.shape,a.dtype);for(let u=0;u<h;++u)for(let e=0;e<y;++e){const t=e*v-x.top;for(let n=0;n<b;++n){const r=n*w-x.left;for(let a=0;a<g;++a){let s=Number.MIN_SAFE_INTEGER,o=0,i=0;for(let e=0;e<k;++e){const n=t+e*N;if(n>=0&&n<f)for(let t=0;t<I;++t){const l=r+t*S;if(l>=0&&l<m){const r=d[u][n][l][a]+p[e][t][a];r>s&&(s=r,o=e,i=t)}}}$[o][i][a]+=C[u][e][n][a]}}}return{dataId:c.write(Fr($,r.dtype),a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},BR={kernelName:Be,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{const{x:r,filter:a,dy:s}=e,{strides:o,pad:i,dilations:l}=n,c=t,d=R(r.shape,c.data.get(r.dataId).values),p=R(a.shape,c.data.get(a.dataId).values),{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:b,padInfo:x,strideHeight:v,strideWidth:w,filterHeight:k,filterWidth:I,dilationHeight:N,dilationWidth:S,outShape:T}=so(r.shape,a.shape,o,i,"NHWC",l);u(s.rank===T.length,()=>`Error in ${Be}, dy must have the same rank as output ${T.length}, but got ${s.rank}`);const C=R(T,c.data.get(s.dataId).values),$=O(r.shape,r.dtype);for(let u=0;u<h;++u)for(let e=0;e<y;++e){const t=e*v-x.top;for(let n=0;n<b;++n){const r=n*w-x.left;for(let a=0;a<g;++a){let s=Number.MIN_SAFE_INTEGER,o=t<0?0:t,i=r<0?0:r;for(let e=0;e<k;++e){const n=t+e*N;if(n>=0&&n<f)for(let t=0;t<I;++t){const l=r+t*S;if(l>=0&&l<m){const r=d[u][n][l][a]+p[e][t][a];r>s&&(s=r,o=n,i=l)}}}$[u][o][i][a]+=C[u][e][n][a]}}}return{dataId:c.write(Fr($,r.dtype),r.shape,r.dtype),shape:r.shape,dtype:r.dtype}}};const VR={kernelName:We,backendName:"cpu",kernelFunc:function(e){const{inputs:t,backend:n,attrs:r}=e,{image:a}=t,{canvas:s,options:o}=r,{contextOptions:i,imageOptions:u}=o||{},l=(null===u||void 0===u?void 0:u.alpha)||1,c=(null===i||void 0===i?void 0:i.contextType)||"2d";if("2d"!==c)throw new Error(`Context type ${i.contextType} is not supported by the CPU backend.`);const d=s.getContext(c,(null===i||void 0===i?void 0:i.contextAttributes)||{});if(null==d)throw new Error(`Could not get the context with ${c} 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