diff --git a/docs/compiled/artplayer-plugin-ads.mjs b/docs/compiled/artplayer-plugin-ads.mjs index 59a19865c..b7f53a3f1 100644 --- a/docs/compiled/artplayer-plugin-ads.mjs +++ b/docs/compiled/artplayer-plugin-ads.mjs @@ -4,251 +4,4 @@ * (c) 2017-2026 Harvey Zhao * Released under the MIT License. */ -const style = ".artplayer-plugin-ads{position:absolute;z-index:150;inset:0;width:100%;height:100%;overflow:hidden;font-size:13px;line-height:1;color:#fff;background-color:#000}.artplayer-plugin-ads .artplayer-plugin-ads-html{display:flex;align-items:center;justify-content:center;width:100%;height:100%}.artplayer-plugin-ads .artplayer-plugin-ads-video{width:100%;height:100%}.artplayer-plugin-ads .artplayer-plugin-ads-timer{display:none;position:absolute;top:10px;right:10px}.artplayer-plugin-ads .artplayer-plugin-ads-timer>div{display:flex;align-items:center;background-color:#00000080;border-radius:15px;margin-left:5px;padding:5px 10px;cursor:pointer}.artplayer-plugin-ads .artplayer-plugin-ads-control{display:none;position:absolute;bottom:10px;right:10px}.artplayer-plugin-ads .artplayer-plugin-ads-control>div{display:flex;align-items:center;background-color:#00000080;border-radius:15px;margin-left:5px;padding:5px 10px;cursor:pointer}.artplayer-plugin-ads .artplayer-plugin-ads-control .art-icon svg{width:20px;height:20px}.artplayer-plugin-ads .artplayer-plugin-ads-loading{display:flex;align-items:center;justify-content:center;position:absolute;inset:0;width:100%;height:100%}"; -function checkVersion(art) { - const { - version, - utils: { errorHandle } - } = art.constructor; - const arr = version.split(".").map(Number); - const major = arr[0]; - const minor = arr[1] / 100; - errorHandle( - major + minor >= 5, - `Artplayer.js@${version} is not compatible the artplayerPluginAds@${artplayerPluginAds.version}. Please update it to version Artplayer.js@5.x.x` - ); -} -function artplayerPluginAds(option) { - return (art) => { - checkVersion(art); - const { - template: { $player }, - icons: { volume, volumeClose, fullscreenOn, fullscreenOff, loading }, - constructor: { - validator, - utils: { query, append, setStyle } - } - } = art; - option = validator( - { - html: "", - video: "", - url: "", - playDuration: 5, - totalDuration: 10, - muted: false, - i18n: { - close: "关闭广告", - countdown: "%s秒", - detail: "查看详情", - canBeClosed: "%s秒后可关闭广告" - }, - ...option - }, - { - html: "?string", - video: "?string", - url: "?string", - playDuration: "number", - totalDuration: "number", - muted: "?boolean", - i18n: { - close: "string", - countdown: "string", - detail: "string", - canBeClosed: "string" - } - } - ); - let $ads = null; - let $timer = null; - let $close = null; - let $countdown = null; - let $control = null; - let $loading = null; - let time = 0; - let timer = null; - let isEnd = false; - let isInit = false; - let isCanClose = false; - function getI18n(val, str) { - return str.replace("%s", val); - } - function skip() { - isEnd = true; - art.play(); - if (option.video) - $ads.pause(); - setStyle(art.template.$ads, "display", "none"); - art.emit("artplayerPluginAds:skip", option); - } - function play() { - if (isEnd) - return; - timer = setTimeout(() => { - time += 1; - const playDuration = option.playDuration - time; - if (playDuration >= 1) { - $close.innerHTML = getI18n(playDuration, option.i18n.canBeClosed); - } else { - $close.innerHTML = option.i18n.close; - if (!isCanClose) { - isCanClose = true; - } - } - $countdown.innerHTML = getI18n(option.totalDuration - time, option.i18n.countdown); - if (time >= option.totalDuration) { - skip(); - } else { - play(); - } - }, 1e3); - } - function pause() { - if (isEnd) - return; - clearTimeout(timer); - } - function show() { - art.template.$ads = append($player, '
'); - $ads = append( - art.template.$ads, - option.video ? `` : `
${option.html}
` - ); - $loading = append(art.template.$ads, '
'); - append($loading, loading); - $timer = append( - art.template.$ads, - `
-
${option.playDuration <= 0 ? option.i18n.close : getI18n(option.playDuration, option.i18n.canBeClosed)}
-
${getI18n( - option.totalDuration, - option.i18n.countdown - )}
-
` - ); - $close = query(".artplayer-plugin-ads-close", $timer); - $countdown = query(".artplayer-plugin-ads-countdown", $timer); - if (option.playDuration >= option.totalDuration) { - setStyle($close, "display", "none"); - } - art.proxy($close, "click", () => { - if (isCanClose) { - skip(); - } - }); - $control = append( - art.template.$ads, - `
-
${option.i18n.detail}
-
-
-
` - ); - const $detail = query(".artplayer-plugin-ads-detail", $control); - const $muted = query(".artplayer-plugin-ads-muted", $control); - const $fullscreen = query(".artplayer-plugin-ads-fullscreen", $control); - if (option.url) { - art.proxy($detail, "click", () => { - window.open(option.url); - art.emit("artplayerPluginAds:click", option); - }); - } else { - setStyle($detail, "display", "none"); - } - if (option.video) { - const $volume = append($muted, volume); - const $volumeClose = append($muted, volumeClose); - setStyle($volumeClose, "display", "none"); - if (option.muted) { - $ads.muted = true; - setStyle($volume, "display", "none"); - setStyle($volumeClose, "display", "inline-flex"); - } - art.proxy($muted, "click", () => { - $ads.muted = !$ads.muted; - if ($ads.muted) { - setStyle($volume, "display", "none"); - setStyle($volumeClose, "display", "inline-flex"); - } else { - setStyle($volume, "display", "inline-flex"); - setStyle($volumeClose, "display", "none"); - } - }); - } else { - setStyle($muted, "display", "none"); - } - const $fullscreenOn = append($fullscreen, fullscreenOn); - const $fullscreenOff = append($fullscreen, fullscreenOff); - setStyle($fullscreenOff, "display", "none"); - art.proxy($fullscreen, "click", () => { - art.fullscreen = !art.fullscreen; - if (art.fullscreen) { - setStyle($fullscreenOn, "display", "inline-flex"); - setStyle($fullscreenOff, "display", "none"); - } else { - setStyle($fullscreenOn, "display", "none"); - setStyle($fullscreenOff, "display", "inline-flex"); - } - }); - art.proxy($ads, "click", () => { - if (option.url) - window.open(option.url); - art.emit("artplayerPluginAds:click", option); - }); - } - function init() { - if (isInit) - return; - isInit = true; - show(); - art.pause(); - if (option.video) { - art.proxy($ads, "error", skip); - art.proxy($ads, "loadedmetadata", () => { - play(); - $ads.play(); - setStyle($timer, "display", "flex"); - setStyle($control, "display", "flex"); - setStyle($loading, "display", "none"); - }); - } else { - play(); - setStyle($timer, "display", "flex"); - setStyle($control, "display", "flex"); - setStyle($loading, "display", "none"); - } - art.on("document:visibilitychange", () => { - if (document.hidden) { - pause(); - } else { - play(); - } - }); - } - art.on("ready", () => { - art.once("play", init); - art.once("video:playing", init); - }); - return { - name: "artplayerPluginAds", - skip, - pause, - play - }; - }; -} -if (typeof document !== "undefined") { - if (!document.getElementById("artplayer-plugin-ads")) { - const $style = document.createElement("style"); - $style.id = "artplayer-plugin-ads"; - $style.textContent = style; - document.head.appendChild($style); - } -} -if (typeof window !== "undefined") { - window.artplayerPluginAds = artplayerPluginAds; -} -export { - artplayerPluginAds as default -}; +function l(e){return n=>{!function(e){const{version:n,utils:{errorHandle:a}}=e.constructor,i=n.split(".").map(Number);a(i[0]+i[1]/100>=5,`Artplayer.js@${n} is not compatible the artplayerPluginAds@${l.version}. Please update it to version Artplayer.js@5.x.x`)}(n);const{template:{$player:a},icons:{volume:i,volumeClose:t,fullscreenOn:r,fullscreenOff:d,loading:o},constructor:{validator:s,utils:{query:p,append:u,setStyle:c}}}=n;e=s({html:"",video:"",url:"",playDuration:5,totalDuration:10,muted:!1,i18n:{close:"\u5173\u95ed\u5e7f\u544a",countdown:"%s\u79d2",detail:"\u67e5\u770b\u8be6\u60c5",canBeClosed:"%s\u79d2\u540e\u53ef\u5173\u95ed\u5e7f\u544a"},...e},{html:"?string",video:"?string",url:"?string",playDuration:"number",totalDuration:"number",muted:"?boolean",i18n:{close:"string",countdown:"string",detail:"string",canBeClosed:"string"}});let y=null,g=null,m=null,f=null,v=null,x=null,h=0,w=null,b=!1,$=!1,k=!1;function D(l,e){return e.replace("%s",l)}function A(){b=!0,n.play(),e.video&&y.pause(),c(n.template.$ads,"display","none"),n.emit("artplayerPluginAds:skip",e)}function C(){b||(w=setTimeout(()=>{h+=1;const l=e.playDuration-h;l>=1?m.innerHTML=D(l,e.i18n.canBeClosed):(m.innerHTML=e.i18n.close,k||(k=!0)),f.innerHTML=D(e.totalDuration-h,e.i18n.countdown),h>=e.totalDuration?A():C()},1e3))}function P(){b||clearTimeout(w)}function B(){$||($=!0,function(){n.template.$ads=u(a,'
'),y=u(n.template.$ads,e.video?``:`
${e.html}
`),x=u(n.template.$ads,'
'),u(x,o),g=u(n.template.$ads,`
\n
${e.playDuration<=0?e.i18n.close:D(e.playDuration,e.i18n.canBeClosed)}
\n
${D(e.totalDuration,e.i18n.countdown)}
\n
`),m=p(".artplayer-plugin-ads-close",g),f=p(".artplayer-plugin-ads-countdown",g),e.playDuration>=e.totalDuration&&c(m,"display","none"),n.proxy(m,"click",()=>{k&&A()}),v=u(n.template.$ads,`
\n
${e.i18n.detail}
\n
\n
\n
`);const l=p(".artplayer-plugin-ads-detail",v),s=p(".artplayer-plugin-ads-muted",v),h=p(".artplayer-plugin-ads-fullscreen",v);if(e.url?n.proxy(l,"click",()=>{window.open(e.url),n.emit("artplayerPluginAds:click",e)}):c(l,"display","none"),e.video){const l=u(s,i),a=u(s,t);c(a,"display","none"),e.muted&&(y.muted=!0,c(l,"display","none"),c(a,"display","inline-flex")),n.proxy(s,"click",()=>{y.muted=!y.muted,y.muted?(c(l,"display","none"),c(a,"display","inline-flex")):(c(l,"display","inline-flex"),c(a,"display","none"))})}else c(s,"display","none");const w=u(h,r),b=u(h,d);c(b,"display","none"),n.proxy(h,"click",()=>{n.fullscreen=!n.fullscreen,n.fullscreen?(c(w,"display","inline-flex"),c(b,"display","none")):(c(w,"display","none"),c(b,"display","inline-flex"))}),n.proxy(y,"click",()=>{e.url&&window.open(e.url),n.emit("artplayerPluginAds:click",e)})}(),n.pause(),e.video?(n.proxy(y,"error",A),n.proxy(y,"loadedmetadata",()=>{C(),y.play(),c(g,"display","flex"),c(v,"display","flex"),c(x,"display","none")})):(C(),c(g,"display","flex"),c(v,"display","flex"),c(x,"display","none")),n.on("document:visibilitychange",()=>{document.hidden?P():C()}))}return n.on("ready",()=>{n.once("play",B),n.once("video:playing",B)}),{name:"artplayerPluginAds",skip:A,pause:P,play:C}}}if("undefined"!==typeof document&&!document.getElementById("artplayer-plugin-ads")){const l=document.createElement("style");l.id="artplayer-plugin-ads",l.textContent=".artplayer-plugin-ads{position:absolute;z-index:150;inset:0;width:100%;height:100%;overflow:hidden;font-size:13px;line-height:1;color:#fff;background-color:#000}.artplayer-plugin-ads .artplayer-plugin-ads-html{display:flex;align-items:center;justify-content:center;width:100%;height:100%}.artplayer-plugin-ads .artplayer-plugin-ads-video{width:100%;height:100%}.artplayer-plugin-ads .artplayer-plugin-ads-timer{display:none;position:absolute;top:10px;right:10px}.artplayer-plugin-ads .artplayer-plugin-ads-timer>div{display:flex;align-items:center;background-color:#00000080;border-radius:15px;margin-left:5px;padding:5px 10px;cursor:pointer}.artplayer-plugin-ads .artplayer-plugin-ads-control{display:none;position:absolute;bottom:10px;right:10px}.artplayer-plugin-ads .artplayer-plugin-ads-control>div{display:flex;align-items:center;background-color:#00000080;border-radius:15px;margin-left:5px;padding:5px 10px;cursor:pointer}.artplayer-plugin-ads .artplayer-plugin-ads-control .art-icon svg{width:20px;height:20px}.artplayer-plugin-ads .artplayer-plugin-ads-loading{display:flex;align-items:center;justify-content:center;position:absolute;inset:0;width:100%;height:100%}",document.head.appendChild(l)}"undefined"!==typeof window&&(window.artplayerPluginAds=l);export{l as default}; diff --git a/docs/compiled/artplayer-plugin-ambilight.mjs b/docs/compiled/artplayer-plugin-ambilight.mjs index 28ed518fc..292da28e0 100644 --- a/docs/compiled/artplayer-plugin-ambilight.mjs +++ b/docs/compiled/artplayer-plugin-ambilight.mjs @@ -4,104 +4,4 @@ * (c) 2017-2026 Harvey Zhao * Released under the MIT License. */ -function artplayerPluginAmbilight(option = {}) { - return (art) => { - const { $video } = art.template; - const { createElement, addClass, setStyles } = art.constructor.utils; - const { blur = "50px", opacity = 0.5, frequency = 10, duration = 0.3 } = option; - const $ambilight = createElement("div"); - const gridItems = createGridItems($ambilight); - setupAmbilight($ambilight, $video); - setupGridItems(gridItems, blur, opacity, duration); - const updateColors = createColorUpdater($video, gridItems, frequency); - let animationFrameId = null; - function start() { - if (!animationFrameId) { - updateColors(); - } - } - function stop() { - if (animationFrameId) { - cancelAnimationFrame(animationFrameId); - animationFrameId = null; - } - } - art.on("ready", start); - art.on("destroy", stop); - return { - name: "artplayerPluginAmbilight", - start, - stop - }; - function createGridItems($ambilight2) { - $ambilight2.innerHTML = Array.from({ length: 9 }).fill("
").join(""); - return Array.from($ambilight2.children); - } - function setupAmbilight($ambilight2, $video2) { - addClass($ambilight2, "artplayer-plugin-ambilight"); - $video2.parentNode.insertBefore($ambilight2, $video2); - setStyles($ambilight2, { - position: "absolute", - top: 0, - left: 0, - zIndex: 9, - inset: 0, - display: "grid", - gridTemplateColumns: "1fr 1fr 1fr", - gridTemplateRows: "1fr 1fr 1fr" - }); - } - function setupGridItems(gridItems2, blur2, opacity2, duration2) { - gridItems2.forEach(($item) => { - setStyles($item, { - opacity: opacity2, - filter: `blur(${blur2})`, - transition: `background-color ${duration2}s ease` - }); - }); - } - function createColorUpdater($video2, gridItems2, frequency2) { - const canvas = createElement("canvas"); - const ctx = canvas.getContext("2d"); - canvas.width = 3; - canvas.height = 3; - function getAverageColor(x, y, w, h) { - ctx.drawImage($video2, x, y, w, h, 0, 0, 1, 1); - const [r, g, b] = ctx.getImageData(0, 0, 1, 1).data; - return `rgb(${r}, ${g}, ${b})`; - } - let lastUpdateTime = 0; - return function updateColors2() { - const now = performance.now(); - if (now - lastUpdateTime < 1e3 / frequency2 || !art.playing) { - animationFrameId = requestAnimationFrame(updateColors2); - return; - } - lastUpdateTime = now; - const w = $video2.videoWidth / 3; - const h = $video2.videoHeight / 3; - const colors = [ - [0, 0], - [w, 0], - [2 * w, 0], - [0, h], - [w, h], - [2 * w, h], - [0, 2 * h], - [w, 2 * h], - [2 * w, 2 * h] - ].map(([x, y]) => getAverageColor(x, y, w, h)); - gridItems2.forEach(($item, index) => { - $item.style.backgroundColor = colors[index]; - }); - animationFrameId = requestAnimationFrame(updateColors2); - }; - } - }; -} -if (typeof window !== "undefined") { - window.artplayerPluginAmbilight = artplayerPluginAmbilight; -} -export { - artplayerPluginAmbilight as default -}; +function t(t={}){return e=>{const{$video:n}=e.template,{createElement:r,addClass:i,setStyles:o}=e.constructor.utils,{blur:a="50px",opacity:l=.5,frequency:d=10,duration:u=.3}=t,s=r("div"),c=((f=s).innerHTML=Array.from({length:9}).fill("
").join(""),Array.from(f.children));var f,m,g,p;!function(t,e){i(t,"artplayer-plugin-ambilight"),e.parentNode.insertBefore(t,e),o(t,{position:"absolute",top:0,left:0,zIndex:9,inset:0,display:"grid",gridTemplateColumns:"1fr 1fr 1fr",gridTemplateRows:"1fr 1fr 1fr"})}(s,n),m=a,g=l,p=u,c.forEach(t=>{o(t,{opacity:g,filter:`blur(${m})`,transition:`background-color ${p}s ease`})});const y=function(t,n,i){const o=r("canvas"),a=o.getContext("2d");o.width=3,o.height=3;let l=0;return function r(){const o=performance.now();if(o-l<1e3/i||!e.playing)return void(h=requestAnimationFrame(r));l=o;const d=t.videoWidth/3,u=t.videoHeight/3,s=[[0,0],[d,0],[2*d,0],[0,u],[d,u],[2*d,u],[0,2*u],[d,2*u],[2*d,2*u]].map(([e,n])=>function(e,n,r,i){a.drawImage(t,e,n,r,i,0,0,1,1);const[o,l,d]=a.getImageData(0,0,1,1).data;return`rgb(${o}, ${l}, ${d})`}(e,n,d,u));n.forEach((t,e)=>{t.style.backgroundColor=s[e]}),h=requestAnimationFrame(r)}}(n,c,d);let h=null;function b(){h||y()}function v(){h&&(cancelAnimationFrame(h),h=null)}return e.on("ready",b),e.on("destroy",v),{name:"artplayerPluginAmbilight",start:b,stop:v}}}"undefined"!==typeof window&&(window.artplayerPluginAmbilight=t);export{t as default}; diff --git a/docs/compiled/artplayer-plugin-asr.mjs b/docs/compiled/artplayer-plugin-asr.mjs index e28b0f66f..a32b28f25 100644 --- a/docs/compiled/artplayer-plugin-asr.mjs +++ b/docs/compiled/artplayer-plugin-asr.mjs @@ -4,252 +4,4 @@ * (c) 2017-2026 Harvey Zhao * Released under the MIT License. */ -const style = ".art-video-player .art-layer-asr{pointer-events:none!important;position:absolute;z-index:150;inset:0;display:flex;flex-direction:column;justify-content:end;padding:0 2%;font-size:1rem;gap:var(--art-subtitle-gap);padding-bottom:var(--art-subtitle-bottom);transition:padding-bottom var(--art-transition-duration) ease;text-shadow:var(--art-subtitle-border) 1px 0 1px,var(--art-subtitle-border) 0 1px 1px,var(--art-subtitle-border) -1px 0 1px,var(--art-subtitle-border) 0 -1px 1px,var(--art-subtitle-border) 1px 1px 1px,var(--art-subtitle-border) -1px -1px 1px,var(--art-subtitle-border) 1px -1px 1px,var(--art-subtitle-border) -1px 1px 1px}.art-video-player.art-control-show .art-layer-asr{padding-bottom:calc(var(--art-control-height) + var(--art-subtitle-bottom))}"; -function artplayerPluginAsr(option = {}) { - const { - length = 3, - interval = 100, - sampleRate = 16e3, - autoHideTimeout = 1e4, - onAudioChunk = () => null - } = option; - return (art) => { - let started = false; - let audioCtx = null; - let sourceNode = null; - let recorderNode = null; - let gainNode = null; - let bufferChunks = []; - let timer = null; - let workletLoaded = false; - let hideTimer = null; - let mediaStream = null; - let mediaStreamSource = null; - const $asr = art.layers.add({ - name: "asr", - html: "" - }); - function splitByPunctuation(text) { - return text.split(/(?<=[、。！？!?.])\s*/u).map((s) => s.trim()).filter(Boolean).slice(-length); - } - function hide() { - $asr.style.display = "none"; - } - function append(subtitle) { - if (typeof subtitle !== "string") - return; - clearTimeout(hideTimer); - hideTimer = setTimeout(hide, autoHideTimeout); - $asr.style.display = ""; - $asr.innerHTML = splitByPunctuation(subtitle).map((line) => `
${line}
`).join(""); - } - const recorderProcessorCode = ` - class RecorderProcessor extends AudioWorkletProcessor { - process(inputs) { - const input = inputs[0]; - if (input && input[0]) { - this.port.postMessage(input[0]); - } - return true; - } - } - registerProcessor('recorder-processor', RecorderProcessor); - `; - const createWorkletBlobUrl = () => { - const blob = new Blob([recorderProcessorCode], { type: "application/javascript" }); - return URL.createObjectURL(blob); - }; - function floatTo16BitPCM(float32Array) { - const len = float32Array.length; - const buffer = new ArrayBuffer(len * 2); - const view = new DataView(buffer); - for (let i = 0; i < len; i++) { - const s = Math.max(-1, Math.min(1, float32Array[i])); - view.setInt16(i * 2, s < 0 ? s * 32768 : s * 32767, true); - } - return buffer; - } - function pcmToWav(pcmBuffer, sampleRate2) { - const pcmLength = pcmBuffer.byteLength; - const wavBuffer = new ArrayBuffer(44 + pcmLength); - const view = new DataView(wavBuffer); - const writeString = (offset, str) => { - for (let i = 0; i < str.length; i++) { - view.setUint8(offset + i, str.charCodeAt(i)); - } - }; - writeString(0, "RIFF"); - view.setUint32(4, 36 + pcmLength, true); - writeString(8, "WAVE"); - writeString(12, "fmt "); - view.setUint32(16, 16, true); - view.setUint16(20, 1, true); - view.setUint16(22, 1, true); - view.setUint32(24, sampleRate2, true); - view.setUint32(28, sampleRate2 * 2, true); - view.setUint16(32, 2, true); - view.setUint16(34, 16, true); - writeString(36, "data"); - view.setUint32(40, pcmLength, true); - new Uint8Array(wavBuffer).set(new Uint8Array(pcmBuffer), 44); - return wavBuffer; - } - async function setupAudioContext() { - if (audioCtx) - return audioCtx; - audioCtx = new (window.AudioContext || window.webkitAudioContext)({ sampleRate }); - if (audioCtx.state === "suspended") - await audioCtx.resume(); - return audioCtx; - } - async function setupAudioSource() { - if (!sourceNode && art.video) { - try { - sourceNode = audioCtx.createMediaElementSource(art.video); - return sourceNode; - } catch (err) { - console.warn("[artplayerPluginAsr] Direct connection failed:", err); - } - } - try { - if (!mediaStream) { - mediaStream = await captureAudioFromVideo(art.video); - } - if (mediaStream) { - mediaStreamSource = audioCtx.createMediaStreamSource(mediaStream); - return mediaStreamSource; - } - } catch (err) { - console.warn("[artplayerPluginAsr] MediaStream fallback failed:", err); - } - return null; - } - async function captureAudioFromVideo(videoElement) { - try { - if (videoElement.captureStream) - return videoElement.captureStream(); - if (videoElement.mozCaptureStream) - return videoElement.mozCaptureStream(); - console.warn("[artplayerPluginAsr] captureStream not supported"); - return null; - } catch (err) { - console.warn("[artplayerPluginAsr] Error capturing stream:", err); - return null; - } - } - async function startCapture() { - if (started) - return; - try { - await setupAudioContext(); - const audioSource = await setupAudioSource(); - if (!audioSource) - throw new Error("Could not establish audio source"); - if (!workletLoaded) { - const blobUrl = createWorkletBlobUrl(); - await audioCtx.audioWorklet.addModule(blobUrl); - URL.revokeObjectURL(blobUrl); - workletLoaded = true; - } - gainNode = audioCtx.createGain(); - gainNode.gain.value = 1; - recorderNode = new AudioWorkletNode(audioCtx, "recorder-processor"); - recorderNode.port.onmessage = (event) => { - bufferChunks.push(new Float32Array(event.data)); - }; - audioSource.connect(recorderNode); - audioSource.connect(gainNode); - gainNode.connect(audioCtx.destination); - const CHUNK_SAMPLES = sampleRate * (interval / 1e3); - timer = setInterval(async () => { - if (bufferChunks.length === 0) - return; - let accumulated = new Float32Array(0); - while (accumulated.length < CHUNK_SAMPLES && bufferChunks.length) { - const next = bufferChunks.shift(); - const tmp = new Float32Array(accumulated.length + next.length); - tmp.set(accumulated, 0); - tmp.set(next, accumulated.length); - accumulated = tmp; - } - if (accumulated.length < CHUNK_SAMPLES) - return; - const chunkToSend = accumulated.slice(0, CHUNK_SAMPLES); - const pcm = floatTo16BitPCM(chunkToSend); - const wav = pcmToWav(pcm, sampleRate); - const subtitle = await onAudioChunk({ pcm, wav }); - append(subtitle); - }, interval); - started = true; - } catch (err) { - console.error("[artplayerPluginAsr] Initialization failed:", err); - await stopCapture(); - } - } - async function stopCapture() { - if (!started) - return; - started = false; - clearInterval(timer); - timer = null; - if (recorderNode) { - recorderNode.disconnect(); - recorderNode.port.onmessage = null; - recorderNode = null; - } - if (gainNode) { - gainNode.disconnect(); - gainNode = null; - } - bufferChunks = []; - } - async function destroy() { - await stopCapture(); - if (mediaStreamSource) { - mediaStreamSource.disconnect(); - mediaStreamSource = null; - } - if (sourceNode) { - sourceNode.disconnect(); - sourceNode = null; - } - if (mediaStream) { - mediaStream.getTracks().forEach((track) => track.stop()); - mediaStream = null; - } - if (audioCtx) { - await audioCtx.close(); - audioCtx = null; - } - workletLoaded = false; - } - art.on("video:volumechange", () => { - if (gainNode) { - gainNode.gain.value = art.volume; - } - }); - art.on("play", startCapture); - art.on("pause", stopCapture); - art.on("destroy", destroy); - return { - name: "artplayerPluginAsr", - stop: destroy, - hide, - append - }; - }; -} -if (typeof document !== "undefined") { - if (!document.getElementById("artplayer-plugin-asr")) { - const $style = document.createElement("style"); - $style.id = "artplayer-plugin-asr"; - $style.textContent = style; - document.head.appendChild($style); - } -} -if (typeof window !== "undefined") { - window.artplayerPluginAsr = artplayerPluginAsr; -} -export { - artplayerPluginAsr as default -}; +function t(t={}){const{length:e=3,interval:n=100,sampleRate:r=16e3,autoHideTimeout:a=1e4,onAudioChunk:o=()=>null}=t;return t=>{let i=!1,l=null,s=null,u=null,c=null,d=[],p=null,y=!1,f=null,m=null,w=null;const g=t.layers.add({name:"asr",html:""});function h(){g.style.display="none"}function v(t){var n;"string"===typeof t&&(clearTimeout(f),f=setTimeout(h,a),g.style.display="",g.innerHTML=(n=t,n.split(/(?<=[\u3001\u3002\uff01\uff1f!?.])\s*/u).map(t=>t.trim()).filter(Boolean).slice(-e)).map(t=>`
${t}
`).join(""))}async function b(){if(!s&&t.video)try{return s=l.createMediaElementSource(t.video),s}catch(e){console.warn("[artplayerPluginAsr] Direct connection failed:",e)}try{if(m||(m=await async function(t){try{return t.captureStream?t.captureStream():t.mozCaptureStream?t.mozCaptureStream():(console.warn("[artplayerPluginAsr] captureStream not supported"),null)}catch(e){return console.warn("[artplayerPluginAsr] Error capturing stream:",e),null}}(t.video)),m)return w=l.createMediaStreamSource(m),w}catch(e){console.warn("[artplayerPluginAsr] MediaStream fallback failed:",e)}return null}async function x(){i&&(i=!1,clearInterval(p),p=null,u&&(u.disconnect(),u.port.onmessage=null,u=null),c&&(c.disconnect(),c=null),d=[])}async function A(){await x(),w&&(w.disconnect(),w=null),s&&(s.disconnect(),s=null),m&&(m.getTracks().forEach(t=>t.stop()),m=null),l&&(await l.close(),l=null),y=!1}return t.on("video:volumechange",()=>{c&&(c.gain.value=t.volume)}),t.on("play",async function(){if(!i)try{await async function(){return l||(l=new(window.AudioContext||window.webkitAudioContext)({sampleRate:r}),"suspended"===l.state&&await l.resume(),l)}();const t=await b();if(!t)throw new Error("Could not establish audio source");if(!y){const t=(()=>{const t=new Blob(["\n class RecorderProcessor extends AudioWorkletProcessor {\n process(inputs) {\n const input = inputs[0];\n if (input && input[0]) {\n this.port.postMessage(input[0]);\n }\n return true;\n }\n }\n registerProcessor('recorder-processor', RecorderProcessor);\n "],{type:"application/javascript"});return URL.createObjectURL(t)})();await l.audioWorklet.addModule(t),URL.revokeObjectURL(t),y=!0}c=l.createGain(),c.gain.value=1,u=new AudioWorkletNode(l,"recorder-processor"),u.port.onmessage=t=>{d.push(new Float32Array(t.data))},t.connect(u),t.connect(c),c.connect(l.destination);const e=r*(n/1e3);p=setInterval(async()=>{if(0===d.length)return;let t=new Float32Array(0);for(;t.length{for(let n=0;n { - let { url, offset = 0, sync = 0.3 } = option; - const audio = new Audio(); - audio.preload = "auto"; - if (url) { - audio.src = url; - } - function syncAudio() { - if (!art.video || !url) - return; - const videoTime = art.currentTime; - const targetTime = videoTime + offset; - if (Math.abs(audio.currentTime - targetTime) > sync) { - audio.currentTime = targetTime; - } - } - art.on("play", () => { - if (!url) - return; - syncAudio(); - audio.play().catch((err) => { - console.warn(err); - }); - }); - art.on("pause", () => { - audio.pause(); - }); - art.on("seek", () => { - syncAudio(); - }); - art.on("video:timeupdate", () => { - if (art.playing) { - syncAudio(); - } - }); - art.on("video:ratechange", () => { - audio.playbackRate = art.video.playbackRate; - }); - art.on("video:volumechange", () => { - audio.volume = art.volume; - audio.muted = art.muted; - }); - art.on("video:waiting", () => { - audio.pause(); - }); - art.on("video:playing", () => { - if (url && art.playing) { - audio.play().catch((err) => { - console.warn(err); - }); - } - }); - art.on("destroy", () => { - audio.pause(); - audio.src = ""; - audio.load(); - }); - function update(newOption) { - if (newOption.url && newOption.url !== url) { - url = newOption.url; - audio.src = url; - if (art.playing) { - audio.play().catch((err) => console.warn(err)); - } - } - if (newOption.offset !== void 0) { - offset = newOption.offset; - } - if (newOption.sync !== void 0) { - sync = newOption.sync; - } - } - audio.volume = art.volume; - audio.muted = art.muted; - audio.playbackRate = art.video?.playbackRate || 1; - return { - name: "artplayerPluginAudioTrack", - audio, - update - }; - }; -} -if (typeof window !== "undefined") { - window.artplayerPluginAudioTrack = artplayerPluginAudioTrack; -} -export { - artplayerPluginAudioTrack as default -}; +function e(e){return a=>{let{url:o,offset:n=0,sync:t=.3}=e;const u=new Audio;function i(){if(!a.video||!o)return;const e=a.currentTime+n;Math.abs(u.currentTime-e)>t&&(u.currentTime=e)}return u.preload="auto",o&&(u.src=o),a.on("play",()=>{o&&(i(),u.play().catch(e=>{console.warn(e)}))}),a.on("pause",()=>{u.pause()}),a.on("seek",()=>{i()}),a.on("video:timeupdate",()=>{a.playing&&i()}),a.on("video:ratechange",()=>{u.playbackRate=a.video.playbackRate}),a.on("video:volumechange",()=>{u.volume=a.volume,u.muted=a.muted}),a.on("video:waiting",()=>{u.pause()}),a.on("video:playing",()=>{o&&a.playing&&u.play().catch(e=>{console.warn(e)})}),a.on("destroy",()=>{u.pause(),u.src="",u.load()}),u.volume=a.volume,u.muted=a.muted,u.playbackRate=a.video?.playbackRate||1,{name:"artplayerPluginAudioTrack",audio:u,update:function(e){e.url&&e.url!==o&&(o=e.url,u.src=o,a.playing&&u.play().catch(e=>console.warn(e))),void 0!==e.offset&&(n=e.offset),void 0!==e.sync&&(t=e.sync)}}}}"undefined"!==typeof window&&(window.artplayerPluginAudioTrack=e);export{e as default}; diff --git a/docs/compiled/artplayer-plugin-auto-thumbnail.mjs b/docs/compiled/artplayer-plugin-auto-thumbnail.mjs index 920b8918c..31b907bf5 100644 --- a/docs/compiled/artplayer-plugin-auto-thumbnail.mjs +++ b/docs/compiled/artplayer-plugin-auto-thumbnail.mjs @@ -4,63 +4,4 @@ * (c) 2017-2026 Harvey Zhao * Released under the MIT License. */ -function create({ url, width, number }, callback) { - const video = document.createElement("video"); - video.crossOrigin = "anonymous"; - video.src = url; - video.onloadedmetadata = () => { - const duration = video.duration; - const canvas = document.createElement("canvas"); - const ctx = canvas.getContext("2d"); - const height = Math.floor(width * video.videoHeight / video.videoWidth); - canvas.width = width * 10; - canvas.height = height * Math.ceil(number / 10); - let blobUrl = null; - function seekAndDraw(index) { - canvas.toBlob((blob) => { - URL.revokeObjectURL(blobUrl); - blobUrl = URL.createObjectURL(blob); - callback({ - url: blobUrl, - height - }); - }, "image/jpeg"); - if (index >= number) - return; - video.currentTime = duration * index / number; - video.onseeked = () => { - ctx.drawImage(video, index % 10 * width, Math.floor(index / 10) * height, width, height); - seekAndDraw(index + 1); - }; - } - seekAndDraw(0); - }; -} -function artplayerPluginAutoThumbnail(option) { - return async (art) => { - art.on("video:loadedmetadata", () => { - const url = option.url || art.option.url; - const width = option.width || 160; - const number = option.number || 100; - const scale = option.scale || 1; - create({ url, width, number }, (config) => { - art.thumbnails = { - ...config, - column: 10, - number, - width, - scale - }; - }); - }); - return { - name: "artplayerPluginAutoThumbnail" - }; - }; -} -if (typeof window !== "undefined") { - window.artplayerPluginAutoThumbnail = artplayerPluginAutoThumbnail; -} -export { - artplayerPluginAutoThumbnail as default -}; +function e(e){return async t=>(t.on("video:loadedmetadata",()=>{const n=e.url||t.option.url,o=e.width||160,a=e.number||100,i=e.scale||1;!function({url:e,width:t,number:n},o){const a=document.createElement("video");a.crossOrigin="anonymous",a.src=e,a.onloadedmetadata=()=>{const e=a.duration,i=document.createElement("canvas"),r=i.getContext("2d"),u=Math.floor(t*a.videoHeight/a.videoWidth);i.width=10*t,i.height=u*Math.ceil(n/10);let d=null;!function l(c){i.toBlob(e=>{URL.revokeObjectURL(d),d=URL.createObjectURL(e),o({url:d,height:u})},"image/jpeg"),c>=n||(a.currentTime=e*c/n,a.onseeked=()=>{r.drawImage(a,c%10*t,Math.floor(c/10)*u,t,u),l(c+1)})}(0)}}({url:n,width:o,number:a},e=>{t.thumbnails={...e,column:10,number:a,width:o,scale:i}})}),{name:"artplayerPluginAutoThumbnail"})}"undefined"!==typeof window&&(window.artplayerPluginAutoThumbnail=e);export{e as default}; diff --git a/docs/compiled/artplayer-plugin-chapter.mjs b/docs/compiled/artplayer-plugin-chapter.mjs index 46d60db75..4e3afc45c 100644 --- a/docs/compiled/artplayer-plugin-chapter.mjs +++ b/docs/compiled/artplayer-plugin-chapter.mjs @@ -4,159 +4,4 @@ * (c) 2017-2026 Harvey Zhao * Released under the MIT License. */ -const style = ".artplayer-plugin-chapter .art-control-progress-inner{height:100%!important;background-color:transparent!important}.artplayer-plugin-chapter .art-control-progress-inner>.art-progress-hover,.artplayer-plugin-chapter .art-control-progress-inner>.art-progress-loaded,.artplayer-plugin-chapter .art-control-progress-inner>.art-progress-played{display:none!important}.artplayer-plugin-chapter .art-control-thumbnails{bottom:calc(var(--art-bottom-gap) + 64px)!important}.artplayer-plugin-chapter .art-chapters{position:absolute;z-index:0;inset:0;display:flex;align-items:center;gap:4px;height:100%;transform:scaleY(1.25)}.artplayer-plugin-chapter .art-chapters .art-chapter{display:flex;align-items:center;height:100%}.artplayer-plugin-chapter .art-chapters .art-chapter .art-chapter-inner{position:relative;cursor:pointer;width:100%;height:50%;border-radius:10px;overflow:hidden;transition:height var(--art-transition-duration) ease;background-color:var(--art-progress-color)}.artplayer-plugin-chapter .art-chapters .art-chapter:hover .art-chapter-inner{height:100%}.artplayer-plugin-chapter .art-chapter-title{transform-origin:bottom center;transform:scale(.5);opacity:0;position:absolute;z-index:70;top:-50px;left:0;padding:3px 5px;line-height:1;font-size:14px;border-radius:var(--art-border-radius);white-space:nowrap;background-color:var(--art-tip-background);transition:transform var(--art-transition-duration) ease,opacity var(--art-transition-duration) ease}.artplayer-plugin-chapter.art-progress-hover .art-chapter-title{transform:scale(1);opacity:1}"; -function artplayerPluginChapter(option = {}) { - return (art) => { - const { $player } = art.template; - const { setStyle, append, clamp, query, addClass, removeClass } = art.constructor.utils; - const html = ` -
-
-
-
-
-
-
- `; - let $chapters = []; - const $inner = art.query(".art-control-progress-inner"); - const $control = append($inner, '
'); - const $title = append($inner, '
'); - function showTitle({ $chapter, width }) { - const title = $chapter.dataset.title.trim(); - if (title) { - $title.textContent = title; - const titleWidth = $title.clientWidth; - if (width <= titleWidth / 2) { - setStyle($title, "left", 0); - } else if (width > $inner.clientWidth - titleWidth / 2) { - setStyle($title, "left", `${$inner.clientWidth - titleWidth}px`); - } else { - setStyle($title, "left", `${width - titleWidth / 2}px`); - } - } - } - function update(chapters = []) { - $chapters = []; - $control.textContent = ""; - removeClass($player, "artplayer-plugin-chapter"); - if (!Array.isArray(chapters)) - return; - if (!chapters.length) - return; - if (!art.duration) - return; - chapters = chapters.sort((a, b) => a.start - b.start); - for (let i = 0; i < chapters.length; i++) { - const chapter = chapters[i]; - const nextChapter = chapters[i + 1]; - if (chapter.end === Infinity) { - chapter.end = art.duration; - } - if (typeof chapter.start !== "number" || typeof chapter.end !== "number" || typeof chapter.title !== "string") { - throw new TypeError("Illegal chapter data type"); - } - if (chapter.start < 0 || chapter.end > art.duration || chapter.start >= chapter.end) { - throw new Error("Illegal chapter time point"); - } - if (nextChapter && chapter.end > nextChapter.start) { - throw new Error("Illegal chapter time point"); - } - } - if (chapters[0].start > 0) { - chapters.unshift({ start: 0, end: chapters[0].start, title: "" }); - } - if (chapters[chapters.length - 1].end < art.duration) { - chapters.push({ start: chapters[chapters.length - 1].end, end: art.duration, title: "" }); - } - for (let i = 0; i < chapters.length - 1; i++) { - if (chapters[i].end !== chapters[i + 1].start) { - chapters.splice(i + 1, 0, { - start: chapters[i].end, - end: chapters[i + 1].start, - title: "" - }); - } - } - $chapters = chapters.map((chapter) => { - const $chapter = append($control, html); - const start = clamp(chapter.start, 0, art.duration); - const end = clamp(chapter.end, 0, art.duration); - const duration = end - start; - const percentage = duration / art.duration; - $chapter.dataset.start = start; - $chapter.dataset.end = end; - $chapter.dataset.duration = duration; - $chapter.dataset.title = chapter.title.trim(); - $chapter.style.width = `${percentage * 100}%`; - return { - $chapter, - $hover: query(".art-progress-hover", $chapter), - $loaded: query(".art-progress-loaded", $chapter), - $played: query(".art-progress-played", $chapter) - }; - }); - addClass($player, "artplayer-plugin-chapter"); - art.emit("setBar", "loaded", art.loaded || 0); - } - art.on("setBar", (type, percentage) => { - if (!$chapters.length) - return; - for (let i = 0; i < $chapters.length; i++) { - const { $chapter, $loaded, $played, $hover } = $chapters[i]; - const $target = { - hover: $hover, - loaded: $loaded, - played: $played - }[type]; - if (!$target) - return; - const width = $control.clientWidth * percentage; - const currentTime = art.duration * percentage; - const duration = Number.parseFloat($chapter.dataset.duration); - const start = Number.parseFloat($chapter.dataset.start); - const end = Number.parseFloat($chapter.dataset.end); - if (currentTime < start) { - setStyle($target, "width", 0); - } - if (currentTime > end) { - setStyle($target, "width", "100%"); - } - if (currentTime >= start && currentTime <= end) { - const percentage2 = (currentTime - start) / duration; - setStyle($target, "width", `${percentage2 * 100}%`); - if (type === "hover" && width > 0) { - showTitle({ $chapter, width }); - } - } - } - }); - art.once("video:loadedmetadata", () => update(option.chapters)); - return { - name: "artplayerPluginChapter", - update: ({ chapters }) => update(chapters) - }; - }; -} -if (typeof document !== "undefined") { - const id = "artplayer-plugin-chapter"; - let $style = document.getElementById(id); - if (!$style) { - $style = document.createElement("style"); - $style.id = id; - if (document.readyState === "loading") { - document.addEventListener("DOMContentLoaded", () => { - document.head.appendChild($style); - }); - } else { - (document.head || document.documentElement).appendChild($style); - } - } - $style.textContent = style; -} -if (typeof window !== "undefined") { - window.artplayerPluginChapter = artplayerPluginChapter; -} -export { - artplayerPluginChapter as default -}; +function t(t={}){return r=>{const{$player:e}=r.template,{setStyle:a,append:n,clamp:o,query:i,addClass:p,removeClass:d}=r.constructor.utils;let s=[];const l=r.query(".art-control-progress-inner"),c=n(l,'
'),h=n(l,'
');function u({$chapter:t,width:r}){const e=t.dataset.title.trim();if(e){h.textContent=e;const t=h.clientWidth;r<=t/2?a(h,"left",0):r>l.clientWidth-t/2?a(h,"left",l.clientWidth-t+"px"):a(h,"left",r-t/2+"px")}}function g(t=[]){if(s=[],c.textContent="",d(e,"artplayer-plugin-chapter"),Array.isArray(t)&&t.length&&r.duration){t=t.sort((t,r)=>t.start-r.start);for(let e=0;er.duration||a.start>=a.end)throw new Error("Illegal chapter time point");if(n&&a.end>n.start)throw new Error("Illegal chapter time point")}t[0].start>0&&t.unshift({start:0,end:t[0].start,title:""}),t[t.length-1].end{const e=n(c,'\n
\n
\n
\n
\n
\n
\n
\n '),a=o(t.start,0,r.duration),p=o(t.end,0,r.duration),d=p-a,s=d/r.duration;return e.dataset.start=a,e.dataset.end=p,e.dataset.duration=d,e.dataset.title=t.title.trim(),e.style.width=100*s+"%",{$chapter:e,$hover:i(".art-progress-hover",e),$loaded:i(".art-progress-loaded",e),$played:i(".art-progress-played",e)}}),p(e,"artplayer-plugin-chapter"),r.emit("setBar","loaded",r.loaded||0)}}return r.on("setBar",(t,e)=>{if(s.length)for(let n=0;nf&&a(l,"width","100%"),g>=m&&g<=f){a(l,"width",100*((g-m)/y)+"%"),"hover"===t&&h>0&&u({$chapter:o,width:h})}}}),r.once("video:loadedmetadata",()=>g(t.chapters)),{name:"artplayerPluginChapter",update:({chapters:t})=>g(t)}}}if("undefined"!==typeof document){const t="artplayer-plugin-chapter";let r=document.getElementById(t);r||(r=document.createElement("style"),r.id=t,"loading"===document.readyState?document.addEventListener("DOMContentLoaded",()=>{document.head.appendChild(r)}):(document.head||document.documentElement).appendChild(r)),r.textContent=".artplayer-plugin-chapter .art-control-progress-inner{height:100%!important;background-color:transparent!important}.artplayer-plugin-chapter .art-control-progress-inner>.art-progress-hover,.artplayer-plugin-chapter .art-control-progress-inner>.art-progress-loaded,.artplayer-plugin-chapter .art-control-progress-inner>.art-progress-played{display:none!important}.artplayer-plugin-chapter .art-control-thumbnails{bottom:calc(var(--art-bottom-gap) + 64px)!important}.artplayer-plugin-chapter .art-chapters{position:absolute;z-index:0;inset:0;display:flex;align-items:center;gap:4px;height:100%;transform:scaleY(1.25)}.artplayer-plugin-chapter .art-chapters .art-chapter{display:flex;align-items:center;height:100%}.artplayer-plugin-chapter .art-chapters .art-chapter .art-chapter-inner{position:relative;cursor:pointer;width:100%;height:50%;border-radius:10px;overflow:hidden;transition:height var(--art-transition-duration) ease;background-color:var(--art-progress-color)}.artplayer-plugin-chapter .art-chapters .art-chapter:hover .art-chapter-inner{height:100%}.artplayer-plugin-chapter .art-chapter-title{transform-origin:bottom center;transform:scale(.5);opacity:0;position:absolute;z-index:70;top:-50px;left:0;padding:3px 5px;line-height:1;font-size:14px;border-radius:var(--art-border-radius);white-space:nowrap;background-color:var(--art-tip-background);transition:transform var(--art-transition-duration) ease,opacity var(--art-transition-duration) ease}.artplayer-plugin-chapter.art-progress-hover .art-chapter-title{transform:scale(1);opacity:1}"}"undefined"!==typeof window&&(window.artplayerPluginChapter=t);export{t as default}; diff --git a/docs/compiled/artplayer-plugin-chromecast.mjs b/docs/compiled/artplayer-plugin-chromecast.mjs index 234f0d2f8..b17d71fc4 100644 --- a/docs/compiled/artplayer-plugin-chromecast.mjs +++ b/docs/compiled/artplayer-plugin-chromecast.mjs @@ -4,176 +4,4 @@ * (c) 2017-2026 Harvey Zhao * Released under the MIT License. */ -function loadScript(src) { - return new Promise((resolve, reject) => { - const script = document.createElement("script"); - script.src = src; - script.onload = resolve; - script.onerror = reject; - document.body.appendChild(script); - }); -} -function getMimeType(url) { - const extension = url.split("?")[0].split("#")[0].split(".").pop().toLowerCase(); - const mimeTypes = { - mp4: "video/mp4", - webm: "video/webm", - ogg: "video/ogg", - ogv: "video/ogg", - mp3: "audio/mp3", - wav: "audio/wav", - flv: "video/x-flv", - mov: "video/quicktime", - avi: "video/x-msvideo", - wmv: "video/x-ms-wmv", - mpd: "application/dash+xml", - m3u8: "application/x-mpegURL" - }; - return mimeTypes[extension] || "application/octet-stream"; -} -function artplayerPluginChromecast(option) { - const DEFAULT_ICON = ``; - const DEFAULT_SDK = "https://www.gstatic.com/cv/js/sender/v1/cast_sender.js?loadCastFramework=1"; - let isCastInitialized = false; - let castSession = null; - let castState = null; - const updateCastButton = (state) => { - const button = document.querySelector(".art-icon-cast"); - if (button) { - switch (state) { - case "connected": - button.style.color = "red"; - break; - case "connecting": - case "disconnecting": - button.style.color = "orange"; - break; - case "disconnected": - default: - button.style.color = "white"; - break; - } - } - }; - const initializeCastApi = () => { - return new Promise((resolve, reject) => { - window.__onGCastApiAvailable = (isAvailable) => { - if (isAvailable) { - const context = window.cast.framework.CastContext.getInstance(); - context.setOptions({ - receiverApplicationId: window.chrome.cast.media.DEFAULT_MEDIA_RECEIVER_APP_ID, - autoJoinPolicy: window.chrome.cast.AutoJoinPolicy.ORIGIN_SCOPED - }); - context.addEventListener( - window.cast.framework.CastContextEventType.SESSION_STATE_CHANGED, - (event) => { - const SessionState = window.cast.framework.SessionState; - castState = event.sessionState; - castSession = event.session; - switch (event.sessionState) { - case SessionState.NO_SESSION: - option.onStateChange?.("disconnected"); - updateCastButton("disconnected"); - break; - case SessionState.SESSION_STARTING: - option.onStateChange?.("connecting"); - updateCastButton("connecting"); - break; - case SessionState.SESSION_STARTED: - option.onStateChange?.("connected"); - updateCastButton("connected"); - break; - case SessionState.SESSION_ENDING: - option.onStateChange?.("disconnecting"); - updateCastButton("disconnecting"); - break; - case SessionState.SESSION_RESUMED: - option.onStateChange?.("connected"); - updateCastButton("connected"); - break; - } - } - ); - context.addEventListener(window.cast.framework.CastContextEventType.CAST_STATE_CHANGED, (event) => { - const CastState = window.cast.framework.CastState; - switch (event.castState) { - case CastState.NO_DEVICES_AVAILABLE: - option.onCastAvailable?.(false); - break; - case CastState.NOT_CONNECTED: - option.onCastAvailable?.(true); - break; - case CastState.CONNECTING: - case CastState.CONNECTED: - option.onCastAvailable?.(true); - break; - } - }); - isCastInitialized = true; - resolve(); - } else { - reject(new Error("Cast API is not available")); - } - }; - if (!window.chrome || !window.chrome.cast) { - loadScript(option.sdk || DEFAULT_SDK).catch(reject); - } - }); - }; - const castVideo = (art, session) => { - const url = option.url || art.option.url; - const mediaInfo = new window.chrome.cast.media.MediaInfo(url, option.mimeType || getMimeType(url)); - const request = new window.chrome.cast.media.LoadRequest(mediaInfo); - session.loadMedia(request).then(() => { - art.notice.show = "Casting started"; - option.onCastStart?.(); - }).catch((error) => { - art.notice.show = "Error casting media"; - option.onError?.(error); - throw error; - }); - }; - return async (art) => { - art.controls.add({ - name: "chromecast", - position: "right", - tooltip: "Chromecast", - html: `${option.icon || DEFAULT_ICON}`, - click: async () => { - if (!isCastInitialized) { - try { - await initializeCastApi(); - } catch (error) { - art.notice.show = "Failed to initialize Cast API"; - option.onError?.(error); - throw error; - } - } - const context = window.cast.framework.CastContext.getInstance(); - if (castSession) { - castVideo(art, castSession); - } else { - try { - const session = await context.requestSession(); - castVideo(art, session); - } catch (error) { - art.notice.show = "Error connecting to cast session"; - option.onError?.(error); - throw error; - } - } - } - }); - return { - name: "artplayerPluginChromecast", - getCastState: () => castState, - isCasting: () => castSession !== null - }; - }; -} -if (typeof window !== "undefined") { - window.artplayerPluginChromecast = artplayerPluginChromecast; -} -export { - artplayerPluginChromecast as default -}; +function e(e){let t=!1,o=null,n=null;const a=e=>{const t=document.querySelector(".art-icon-cast");if(t)switch(e){case"connected":t.style.color="red";break;case"connecting":case"disconnecting":t.style.color="orange";break;default:t.style.color="white"}},c=()=>new Promise((c,s)=>{var i;window.__onGCastApiAvailable=i=>{if(i){const s=window.cast.framework.CastContext.getInstance();s.setOptions({receiverApplicationId:window.chrome.cast.media.DEFAULT_MEDIA_RECEIVER_APP_ID,autoJoinPolicy:window.chrome.cast.AutoJoinPolicy.ORIGIN_SCOPED}),s.addEventListener(window.cast.framework.CastContextEventType.SESSION_STATE_CHANGED,t=>{const c=window.cast.framework.SessionState;switch(n=t.sessionState,o=t.session,t.sessionState){case c.NO_SESSION:e.onStateChange?.("disconnected"),a("disconnected");break;case c.SESSION_STARTING:e.onStateChange?.("connecting"),a("connecting");break;case c.SESSION_STARTED:e.onStateChange?.("connected"),a("connected");break;case c.SESSION_ENDING:e.onStateChange?.("disconnecting"),a("disconnecting");break;case c.SESSION_RESUMED:e.onStateChange?.("connected"),a("connected")}}),s.addEventListener(window.cast.framework.CastContextEventType.CAST_STATE_CHANGED,t=>{const o=window.cast.framework.CastState;switch(t.castState){case o.NO_DEVICES_AVAILABLE:e.onCastAvailable?.(!1);break;case o.NOT_CONNECTED:case o.CONNECTING:case o.CONNECTED:e.onCastAvailable?.(!0)}}),t=!0,c()}else s(new Error("Cast API is not available"))},window.chrome&&window.chrome.cast||(i=e.sdk||"https://www.gstatic.com/cv/js/sender/v1/cast_sender.js?loadCastFramework=1",new Promise((e,t)=>{const o=document.createElement("script");o.src=i,o.onload=e,o.onerror=t,document.body.appendChild(o)})).catch(s)}),s=(t,o)=>{const n=e.url||t.option.url,a=new window.chrome.cast.media.MediaInfo(n,e.mimeType||function(e){return{mp4:"video/mp4",webm:"video/webm",ogg:"video/ogg",ogv:"video/ogg",mp3:"audio/mp3",wav:"audio/wav",flv:"video/x-flv",mov:"video/quicktime",avi:"video/x-msvideo",wmv:"video/x-ms-wmv",mpd:"application/dash+xml",m3u8:"application/x-mpegURL"}[e.split("?")[0].split("#")[0].split(".").pop().toLowerCase()]||"application/octet-stream"}(n)),c=new window.chrome.cast.media.LoadRequest(a);o.loadMedia(c).then(()=>{t.notice.show="Casting started",e.onCastStart?.()}).catch(o=>{throw t.notice.show="Error casting media",e.onError?.(o),o})};return async a=>(a.controls.add({name:"chromecast",position:"right",tooltip:"Chromecast",html:`${e.icon||''}`,click:async()=>{if(!t)try{await c()}catch(i){throw a.notice.show="Failed to initialize Cast API",e.onError?.(i),i}const n=window.cast.framework.CastContext.getInstance();if(o)s(a,o);else try{const e=await n.requestSession();s(a,e)}catch(i){throw a.notice.show="Error connecting to cast session",e.onError?.(i),i}}}),{name:"artplayerPluginChromecast",getCastState:()=>n,isCasting:()=>null!==o})}"undefined"!==typeof window&&(window.artplayerPluginChromecast=e);export{e as default}; 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n(e)||(e=c(e)),this.add(e.neg())},I.sub=I.subtract,I.multiply=function(t){if(this.isZero())return m;if(n(t)||(t=c(t)),e)return i(e.mul(this.low,this.high,t.low,t.high),e.get_high(),this.unsigned);if(t.isZero())return m;if(this.eq(k))return t.isOdd()?k:m;if(t.eq(k))return this.isOdd()?k:m;if(this.isNegative())return t.isNegative()?this.neg().mul(t.neg()):this.neg().mul(t).neg();if(t.isNegative())return this.mul(t.neg()).neg();if(this.lt(f)&&t.lt(f))return o(this.toNumber()*t.toNumber(),this.unsigned);var r=this.high>>>16,a=65535&this.high,s=this.low>>>16,u=65535&this.low,l=t.high>>>16,d=65535&t.high,p=t.low>>>16,h=65535&t.low,g=0,y=0,b=0,x=0;return b+=(x+=u*h)>>>16,y+=(b+=s*h)>>>16,b&=65535,y+=(b+=u*p)>>>16,g+=(y+=a*h)>>>16,y&=65535,g+=(y+=s*p)>>>16,y&=65535,g+=(y+=u*d)>>>16,g+=r*h+a*p+s*d+u*l,i((b&=65535)<<16|(x&=65535),(g&=65535)<<16|(y&=65535),this.unsigned)},I.mul=I.multiply,I.divide=function(t){if(n(t)||(t=c(t)),t.isZero())throw Error("division by zero");var r,a,s;if(e)return 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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 n(e)&&(e=e.toInt()),0===(e&=63)?this:e<32?i(this.low<>>32-e,this.unsigned):i(0,this.low<>>e|this.high<<32-e,this.high>>e,this.unsigned):i(this.high>>e-32,this.high>=0?0:-1,this.unsigned)},I.shr=I.shiftRight,I.shiftRightUnsigned=function(e){if(n(e)&&(e=e.toInt()),0===(e&=63))return this;var t=this.high;return e<32?i(this.low>>>e|t<<32-e,t>>>e,this.unsigned):i(32===e?t:t>>>e-32,0,this.unsigned)},I.shru=I.shiftRightUnsigned,I.shr_u=I.shiftRightUnsigned,I.toSigned=function(){return this.unsigned?i(this.low,this.high,!1):this},I.toUnsigned=function(){return this.unsigned?this:i(this.low,this.high,!0)},I.toBytes=function(e){return e?this.toBytesLE():this.toBytesBE()},I.toBytesLE=function(){var e=this.high,t=this.low;return[255&t,t>>>8&255,t>>>16&255,t>>>24,255&e,e>>>8&255,e>>>16&255,e>>>24]},I.toBytesBE=function(){var 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)},pr}();const br=mr(yr),xr=br||e({__proto__:null,default:br},[yr]);function vr(e){return xr.fromString(e,!0,16)}const wr=vr("c3a5c85c97cb3127"),kr=vr("b492b66fbe98f273"),Ir=vr("9ae16a3b2f90404f");function Nr(e){return e.xor(e.shru(47))}function Sr(e,t,n){const r=e.slice(t,t+n);return xr.fromBytes(Array.from(r),!0,!0)}function Tr(e,t){return Sr(e,t,8)}function Cr(e,t){return Sr(e,t,4)}function $r(e,t){return 0===t?e:e.shru(t).or(e.shl(64-t))}function Er(e,t,n=vr("9ddfea08eb382d69")){let r=e.xor(t).mul(n);r=r.xor(r.shru(47));let a=t.xor(r).mul(n);return a=a.xor(a.shru(47)),a=a.mul(n),a}function Rr(e,t,n,r){return function(e,t,n,r,a,s){a=a.add(e),s=$r(s.add(a).add(r),21);const o=a;return a=(a=a.add(t)).add(n),s=s.add($r(a,44)),[a.add(r),s.add(o)]}(Tr(e,t),Tr(e,t+8),Tr(e,t+16),Tr(e,t+24),n,r)}function _r(e,t=e.length){const n=xr.fromNumber(81,!0);if(t<=32)return t<=16?function(e,t=e.length){if(t>=8){const n=Ir.add(2*t),r=Tr(e,0).add(Ir),a=Tr(e,t-8);return Er($r(a,37).mul(n).add(r),$r(r,25).add(a).mul(n),n)}if(t>=4){const n=Ir.add(2*t);return 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Yr{constructor(e,t,n,r){super(e.shape,e.dtype,e.dataId,r),this.trainable=t,this.name=n}assign(e){if(e.dtype!==this.dtype)throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);if(!d(e.shape,this.shape))throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);Kr().disposeTensor(this),this.dataId=e.dataId,Kr().incRef(this,null)}dispose(){Kr().disposeVariable(this),this.isDisposedInternal=!0}}var Jr,ea,ta,na,ra,aa,sa,oa,ia,ua;Object.defineProperty(Zr,Symbol.hasInstance,{value:e=>e instanceof Yr&&null!=e.assign&&e.assign instanceof Function}),(ea=Jr||(Jr={})).R0="R0",ea.R1="R1",ea.R2="R2",ea.R3="R3",ea.R4="R4",ea.R5="R5",ea.R6="R6",(na=ta||(ta={})).float32="float32",na.int32="int32",na.bool="int32",na.complex64="complex64",(aa=ra||(ra={})).float32="float32",aa.int32="int32",aa.bool="bool",aa.complex64="complex64",(oa=sa||(sa={})).float32="float32",oa.int32="float32",oa.bool="float32",oa.complex64="complex64",(ua=ia||(ia={})).float32="complex64",ua.int32="complex64",ua.bool="complex64",ua.complex64="complex64";const la={float32:sa,int32:ta,bool:ra,complex64:ia};function ca(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error(`Can not upcast ${e} with ${t}`)}return la[e][t]}function da(e){return ca(e,"int32")}function pa(e){return null!=e&&"object"===typeof e&&"texture"in e&&e.texture instanceof WebGLTexture}function ha(e){return"undefined"!==typeof GPUBuffer&&null!=e&&"object"===typeof e&&"buffer"in e&&e.buffer instanceof GPUBuffer}function fa(e,t){if(e.dtype===t.dtype)return[e,t];const n=ca(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function ma(e){const t=[];return ga(e,t,new Set),t}function ga(e,t,n){if(null==e)return;if(e instanceof Yr)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),ga(e,t,n))}}function ya(e){return null!=e.kernelName}class ba{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 xa{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new ba}async ready(){if(null!=this.pendingBackendInit)return this.pendingBackendInit.then(()=>{});if(null!=this.backendInstance)return;const e=this.getSortedBackends();for(let t=0;t{null!=e.setupFunc&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){ur(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 ${e}, no registration found.`);try{const r=t.factory();if(!r||r instanceof n||"function"!==typeof r.then)return this.registry[e]=r,{success:!0,asyncInit:!1};{const t=++this.pendingBackendInitId,n=r.then(n=>!(t(tthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){const e=this.getSortedBackends();for(let t=0;tthis.startScope(r),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),n))}scopedRun(e,t,n){e();try{const e=n();return t(),e}catch(r){throw t(),r}}nextTensorId(){return xa.nextTensorId++}nextVariableId(){return xa.nextVariableId++}clone(e){const t=wa.runKernel(ot,{x:e}),n={x:e};return this.addTapeNode(this.state.activeScope.name,n,[t],e=>({x:()=>{const t={x:e},n={dtype:"float32"};return wa.runKernel(fe,t,n)}}),[],{}),t}runKernel(e,t,n){null==this.backendName&&this.backend;if(!(null!=or(e,this.backendName)))throw new Error(`Kernel '${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,u;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0),null==this.backendName&&this.backend;const l=ya(e)?e.kernelName:null!=this.state.activeScope?this.state.activeScope.name:"";if(ya(e)){const{kernelName:t,inputs:a,attrs:s}=e;null==this.backendName&&this.backend;const l=or(t,this.backendName);i(null!=l,()=>`Cannot find registered kernel '${t}' for backend '${this.backendName}'`),o=()=>{const e=this.backend.numDataIds();u=l.kernelFunc({inputs:a,attrs:s,backend:this.backend});const o=Array.isArray(u)?u:[u];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(t,e,o);const i=o.map(e=>null!=e.rank?e:this.makeTensorFromTensorInfo(e));if(r){const e=this.getTensorsForGradient(t,a,i);n=this.saveTensorsForBackwardMode(e)}return i}}else{const{forwardFunc:t}=e,a=e=>{r&&(n=e.map(e=>this.keep(this.clone(e))))};o=()=>{const e=this.backend.numDataIds();u=this.tidy(()=>t(this.backend,a));const n=Array.isArray(u)?u:[u];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(l,e,n),n}}const{inputs:c,attrs:d}=e,p=ya(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(u)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(e=>this.keep(this.clone(e)))}getTensorsForGradient(e,t,n){const r=ir(e);if(null!=r){const e=r.inputsToSave||[],a=r.outputsToSave||[];let s;r.saveAllInputs?(i(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&&I(e[0])&&(a=e.map(e=>Dr(e)));const s=r.write(a,t,n),o=new Yr(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 Yr(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 Zr(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*k(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 Zr||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*k(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}async profile(e){this.state.profiling=!0;const t=this.state.numBytes,n=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),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-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(const r of this.state.activeProfile.kernels)r.kernelTimeMs=await r.kernelTimeMs,r.extraInfo=await r.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=ir(e);null!=i&&(r=i.gradFunc),null!=r&&(o.gradient=e=>(e=e.map((e,t)=>{if(null==e){const e=n[t],r=_(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=ma(e),n=new Set(t.map(e=>e.id));for(let a=0;a{e.kept||e.scopeId!==r.id||this.track(e)})}gradients(e,t,n,r=!1){if(i(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));i(a instanceof Yr,()=>"The result y returned by f() must be a tensor.");const s=function(e,t,n){const r={},a={};for(let u=0;ur[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;e0)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]=n??function(e){const t=R(c(e),"float32");return wa.makeTensor(t,e,"float32")}(a.shape),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(!d(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),ka);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 i(S(e),()=>"The f passed in customGrad(f) must be a function."),(...t)=>{let n;i(t.every(e=>e instanceof Yr),()=>"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),i(n.value instanceof Yr,()=>"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),i(S(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 a=n.gradFunc(e,r),s=Array.isArray(a)?a:[a];i(s.length===t.length,()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),i(s.every(e=>e instanceof Yr),()=>"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");const o={};return s.forEach((e,t)=>{o[t]=()=>e}),o},inputs:r})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){const t=Fr(),n=await this.backend.time(e);return n.wallMs=Fr()-t,n}track(e){return null!=this.state.activeScope&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new ba;for(const e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}}function va(){const e=U();if(null==e._tfengine){const t=new L(e);e._tfengine=new xa(t)}var t;return t=e._tfengine.ENV,z=t,Kr=()=>e._tfengine,e._tfengine}xa.nextTensorId=0,xa.nextVariableId=0;const wa=va();function ka(e,t){const n={a:e,b:t};return wa.runKernel(K,n)}function Ia(e){if(e||"undefined"!==typeof navigator&&null!=navigator){if(e||(e=navigator),"ReactNative"===e.product)return!0;const t=e.userAgent||e.vendor||("undefined"!==typeof window?window.opera:"");if(!t){const t=e;return t.userAgentData&&t.userAgentData.mobile}return/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge 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window&&null!=window.document||"undefined"!==typeof WorkerGlobalScope}const Sa=V();function Ta(e,t){let n=e;if(Pr(e))return"string"===t?[]:[e.length];if(pa(e)){const t=e.channels||"RGBA";return[e.height,e.width*t.length]}if(ha(e))return[e.buffer.size/(null==t?4:k(t))];if(!Array.isArray(e))return[];const r=[];for(;Array.isArray(n)||Pr(n)&&"string"!==t;)r.push(n.length),n=n[0];return Array.isArray(e)&&V().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&Ca(e,r,[]),r}function Ca(e,t,n){if(n=n||[],!Array.isArray(e)&&!Pr(e))return void i(0===t.length,()=>`Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);i(t.length>0,()=>`Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`),i(e.length===t[0],()=>`Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);const r=t.slice(1);for(let a=0;a=0&&(a=r),$a(r,a,t,n),null==e||!Pr(e)&&!Array.isArray(e)&&"number"!==typeof 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wa.runKernel(Z,n,r)}});const Zs=Aa({argMin_:function(e,t=0){const n={x:Ea(e,"x","argMin")},r={axis:t};return wa.runKernel(J,n,r)}});const Js=Aa({asin_:function(e){const t={x:Ea(e,"x","asin")};return wa.runKernel(ee,t)}});const eo=Aa({asinh_:function(e){const t={x:Ea(e,"x","asinh")};return wa.runKernel(te,t)}});const to=Aa({atan_:function(e){const t={x:Ea(e,"x","atan")};return wa.runKernel(ne,t)}});const no=Aa({atan2_:function(e,t){let n=Ea(e,"a","atan2"),r=Ea(t,"b","atan2");[n,r]=fa(n,r);const a={a:n,b:r};return wa.runKernel(ae,a)}});const ro=Aa({atanh_:function(e){const t={x:Ea(e,"x","atanh")};return wa.runKernel(re,t)}});function ao(e,t,n,r,a="NHWC",s){return io(e,[...t,e[3]],n,s,r,null,null,bo(a))}function so(e,t,n,r,a,s,o="channelsLast"){const[i,u]=co(t);let l;if("channelsLast"===o)l=[i,u,e[3],e[3]];else{if("channelsFirst"!==o)throw new Error(`Unknown dataFormat ${o}`);l=[i,u,e[1],e[1]]}return io(e,l,n,r,a,s,!1,o)}function oo(e,t,n,r,a,s,o="NDHWC"){const[i,u,l]=po(t);let c,d;if("NDHWC"===o)d="channelsLast",c=[i,u,l,e[4],e[4]];else{if("NCDHW"!==o)throw new Error(`Unknown dataFormat ${o}`);d="channelsFirst",c=[i,u,l,e[1],e[1]]}return uo(e,c,n,r,a,!1,d,s)}function io(e,t,n,r,a,s,o=!1,i="channelsLast"){let[u,l,c,d]=[-1,-1,-1,-1];if("channelsLast"===i)[u,l,c,d]=e;else{if("channelsFirst"!==i)throw new Error(`Unknown dataFormat ${i}`);[u,d,l,c]=e}const[p,h,,f]=t,[m,g]=co(n),[y,b]=co(r),x=ho(p,y),v=ho(h,b),{padInfo:w,outHeight:k,outWidth:I}=function(e,t,n,r,a,s,o,i,u){let l,c,d;if("number"===typeof e){l={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};const a=function(e,t,n,r,a){null==r&&(r=lo(e,t,n));const s=e[0],o=e[1],i=fo((s-t+2*r)/n+1,a),u=fo((o-t+2*r)/n+1,a);return[i,u]}([t,n],s,r,e,i);c=a[0],d=a[1]}else if("same"===e){c=Math.ceil(t/r),d=Math.ceil(n/a);const e=Math.max(0,(c-1)*r+s-t),i=Math.max(0,(d-1)*a+o-n),u=Math.floor(e/2),p=e-u,h=Math.floor(i/2);l={top:u,bottom:p,left:h,right:i-h,type:"SAME"}}else if("valid"===e)l={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((t-s+1)/r),d=Math.ceil((n-o+1)/a);else{if("object"!==typeof e)throw Error(`Unknown padding parameter: ${e}`);{const p="channelsLast"===u?e[1][0]:e[2][0],h="channelsLast"===u?e[1][1]:e[2][1],f="channelsLast"===u?e[2][0]:e[3][0],m="channelsLast"===u?e[2][1]:e[3][1];l={top:p,bottom:h,left:f,right:m,type:0===p&&0===h&&0===f&&0===m?"VALID":"EXPLICIT"},c=fo((t-s+p+h)/r+1,i),d=fo((n-o+f+m)/a+1,i)}}return{padInfo:l,outHeight:c,outWidth:d}}(a,l,c,m,g,x,v,s,i),N=o?f*d:f;let S;return"channelsFirst"===i?S=[u,N,k,I]:"channelsLast"===i&&(S=[u,k,I,N]),{batchSize:u,dataFormat:i,inHeight:l,inWidth:c,inChannels:d,outHeight:k,outWidth:I,outChannels:N,padInfo:w,strideHeight:m,strideWidth:g,filterHeight:p,filterWidth:h,effectiveFilterHeight:x,effectiveFilterWidth:v,dilationHeight:y,dilationWidth:b,inShape:e,outShape:S,filterShape:t}}function 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e=(p-1)*a+i-t,c=(h-1)*s+u-n,m=(f-1)*o+l-r,g=Math.floor(e/2),y=e-g,b=Math.floor(c/2),x=c-b,v=Math.floor(m/2);d={top:b,bottom:x,left:v,right:m-v,front:g,back:y,type:"SAME"}}}return{padInfo:d,outDepth:p,outHeight:h,outWidth:f}}(a,l,c,d,y,b,x,I,N,S,i),R=s?g*p:g;let _;return"channelsFirst"===o?_=[u,R,C,$,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 lo(e,t,n,r=1){const a=ho(t,r);return Math.floor((e[0]*(n-1)-n+a)/2)}function co(e){return"number"===typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function po(e){return"number"===typeof e?[e,e,e]:e}function ho(e,t){return t<=1?e:e+(e-1)*(t-1)}function fo(e,t){if(!t)return 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Got strides ${n} and dilations '1'`);let o=s,u=!1;3===s.rank&&(u=!0,o=vo(s,[1,s.shape[0],s.shape[1],s.shape[2]])),i(4===o.rank,()=>`Error in avgPool: x must be rank 4 but got rank ${o.rank}.`),xo("avgPool",r,a);const l={x:o},c={filterSize:t,strides:n,pad:r,dimRoundingMode:a};let d=wa.runKernel(se,l,c);return d=Ls(d,s.dtype),u?vo(d,[d.shape[1],d.shape[2],d.shape[3]]):d}});const ko=Aa({avgPool3d_:function(e,t,n,r,a,s="NDHWC"){const o=Ea(e,"x","avgPool3d","float32");let u=o,l=!1;4===o.rank&&(l=!0,u=vo(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),i(5===u.rank,()=>`Error in avgPool3d: x must be rank 5 but got rank ${u.rank}.`),i("NDHWC"===s,()=>`Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${s}`),i("number"===typeof n&&n>0||Array.isArray(n)&&n[0]>0&&n[1]>0&&n[2]>0,()=>`Error in avgPool3d: Stride must be > 0, but got '${n}'`),xo("avgPool3d",r,a);const c={x:u},d={filterSize:t,strides:n,pad:r,dimRoundingMode:a,dataFormat:s};let p=wa.runKernel(ie,c,d);return p=Ls(p,u.dtype),l?vo(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}});const Io=Aa({concat_:function(e,t=0){i(e.length>=1,()=>"Pass at least one tensor to concat");const n=Ra(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 Bs(n[0]);const r=n,a={axis:t};return wa.runKernel(xe,r,a)}});const No=Aa({matMul_:function(e,t,n=!1,r=!1){let a=Ea(e,"a","matMul"),s=Ea(t,"b","matMul");[a,s]=fa(a,s);const o={a,b:s},i={transposeA:n,transposeB:r};return wa.runKernel(le,o,i)}});const So=Aa({sigmoid_:function(e){const t={x:Ea(e,"x","sigmoid","float32")};return wa.runKernel(wn,t)}});const To=Aa({slice_:function(e,t,n){const r=Ea(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 wa.runKernel(yn,a,s)}});const Co=Aa({tanh_:function(e){const t={x:Ea(e,"x","tanh","float32")};return wa.runKernel(zn,t)}});const $o=Aa({basicLSTMCell_:function(e,t,n,r,a,s){const o=Ea(e,"forgetBias","basicLSTMCell"),i=Ea(t,"lstmKernel","basicLSTMCell"),u=Ea(n,"lstmBias","basicLSTMCell"),l=Ea(r,"data","basicLSTMCell"),c=Ea(a,"c","basicLSTMCell"),d=Ea(s,"h","basicLSTMCell"),p=Io([l,d],1),h=No(p,i),f=Ws(h,u),m=f.shape[0],g=f.shape[1]/4,y=[m,g],b=To(f,[0,0],y),x=To(f,[0,g],y),v=To(f,[0,2*g],y),w=To(f,[0,3*g],y),k=Ws(Gs(So(b),Co(x)),Gs(c,So(Ws(o,v))));return[k,Gs(Co(k),So(w))]}});const Eo=Aa({batchToSpaceND_:function(e,t,n){const r=Ea(e,"x","batchToSpaceND"),a=t.reduce((e,t)=>e*t);i(r.rank>=1+t.length,()=>`input rank is ${r.rank} but should be > than blockShape.length ${t.length}`),i(n.length===t.length,()=>`crops.length is ${n.length} but should be equal to blockShape.length ${t.length}`),i(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 wa.runKernel(ce,s,o)}});const Ro=Aa({batchNorm_:function(e,t,n,r,a,s){null==s&&(s=.001);const o=Ea(e,"x","batchNorm"),u=Ea(t,"mean","batchNorm"),l=Ea(n,"variance","batchNorm");let c,d;null!=a&&(c=Ea(a,"scale","batchNorm")),null!=r&&(d=Ea(r,"offset","batchNorm")),i(u.rank===l.rank,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),i(null==d||u.rank===d.rank,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),i(null==c||u.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?vo(e,[1,1,1,e.size]):2===e.rank?vo(e,[1,1,e.shape[0],e.shape[1]]):3===e.rank?vo(e,[1,e.shape[0],e.shape[1],e.shape[2]]):e,t}(o),scale:c,offset:d,mean:u,variance:l},h={varianceEpsilon:s},f=wa.runKernel(tt,p,h);return vo(f,o.shape)}});const _o=Aa({batchNorm2d_:function(e,t,n,r,a,s){const o=Ea(e,"x","batchNorm"),u=Ea(t,"mean","batchNorm"),l=Ea(n,"variance","batchNorm");let c,d;return null!=a&&(c=Ea(a,"scale","batchNorm")),null!=r&&(d=Ea(r,"offset","batchNorm")),i(2===o.rank,()=>`Error in batchNorm2D: x must be rank 2 but got rank ${o.rank}.`),i(2===u.rank||1===u.rank,()=>`Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${u.rank}.`),i(2===l.rank||1===l.rank,()=>`Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${l.rank}.`),null!=c&&i(2===c.rank||1===c.rank,()=>`Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${c.rank}.`),null!=d&&i(2===d.rank||1===d.rank,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${d.rank}.`),Ro(o,u,l,d,c,s)}});const Ao=Aa({batchNorm3d_:function(e,t,n,r,a,s){const o=Ea(e,"x","batchNorm"),u=Ea(t,"mean","batchNorm"),l=Ea(n,"variance","batchNorm");let c,d;return null!=a&&(c=Ea(a,"scale","batchNorm")),null!=r&&(d=Ea(r,"offset","batchNorm")),i(3===o.rank,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),i(3===u.rank||1===u.rank,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${u.rank}.`),i(3===l.rank||1===l.rank,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),null!=c&&i(3===c.rank||1===c.rank,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),null!=d&&i(3===d.rank||1===d.rank,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${d.rank}.`),Ro(o,u,l,d,c,s)}});const Oo=Aa({batchNorm4d_:function(e,t,n,r,a,s){const o=Ea(e,"x","batchNorm"),u=Ea(t,"mean","batchNorm"),l=Ea(n,"variance","batchNorm");let c,d;return null!=a&&(c=Ea(a,"scale","batchNorm")),null!=r&&(d=Ea(r,"offset","batchNorm")),i(4===o.rank,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),i(4===u.rank||1===u.rank,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${u.rank}.`),i(4===l.rank||1===l.rank,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),null!=c&&i(4===c.rank||1===c.rank,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),null!=d&&i(4===d.rank||1===d.rank,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),Ro(o,u,l,d,c,s)}});const Fo=Aa({bincount_:function(e,t,n){const r=Ea(e,"x","bincount"),a=Ea(t,"weights","bincount");i("int32"===r.dtype,()=>`Error in bincount: input dtype must be int32, but got ${r.dtype}`),i(n>=0,()=>`size must be non-negative, but got ${n}.`),i(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 wa.runKernel(de,s,o)}});const Do=Aa({bitwiseAnd_:function(e,t){const n=Ea(e,"x","bitwiseAnd"),r=Ea(t,"y","bitwiseAnd");if(!d(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 wa.runKernel(pe,a)}});const Mo=Aa({broadcastArgs_:function(e,t){const n=Ea(e,"s0","broadcastArgs","int32"),r=Ea(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 ${r.rank}`);const a={s0:n,s1:r};return wa.runKernel(he,a)}});const Po=Aa({broadcastTo_:function(e,t){let n=Ea(e,"broadcastTo","x");const r=n.shape;if(O(t),t.lengthn.rank){const e=n.shape.slice();for(;e.length=0;u--)if(a[u]===t[u])s[u]=1;else if(1!==n.shape[u])throw new Error(`broadcastTo(): [${r}] cannot be broadcast to [${t}].`);if(0===s.map((e,t)=>e>1?t:-1).filter(e=>e>=0).length)return Bs(n);const o={x:n},i={reps:s};return wa.runKernel(Un,o,i)}});const Lo=Aa({ceil_:function(e){const t={x:Ea(e,"x","ceil","float32")};return wa.runKernel(me,t)}});function Bo(e,t,n){O(e);const r={shape:e,value:t,dtype:n=n||N(t)};return wa.runKernel(Qe,{},r)}const Vo=Aa({clipByValue_:function(e,t,n){const r=Ea(e,"x","clipByValue");if(i(t<=n,()=>`Error in clip: min (${t}) must be less than or equal to max (${n}).`),t===n)return Bo(r.shape,t,r.dtype);const a={x:r},s={clipValueMin:t,clipValueMax:n};return wa.runKernel(ge,a,s)}});const Wo=Aa({concat1d_:function(e){return Io(e,0)}});const zo=Aa({concat2d_:function(e,t){return Io(e,t)}});const Uo=Aa({concat3d_:function(e,t){return Io(e,t)}});const Go=Aa({concat4d_:function(e,t){return Io(e,t)}});const Ho=Aa({conv2d_:function(e,t,n,r,a="NHWC",s=[1,1],o){const u=Ea(e,"x","conv2d","float32"),l=Ea(t,"filter","conv2d","float32");let c=u,d=!1;3===u.rank&&(d=!0,c=vo(u,[1,u.shape[0],u.shape[1],u.shape[2]])),i(4===c.rank,()=>`Error in conv2d: input must be rank 4, but got rank ${c.rank}.`),i(4===l.rank,()=>`Error in conv2d: filter must be rank 4, but got rank ${l.rank}.`),xo("conv2d",r,o);const p="NHWC"===a?c.shape[3]:c.shape[1];i(p===l.shape[2],()=>`Error in conv2d: depth of input (${p}) must match input depth for filter ${l.shape[2]}.`),i(go(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i(yo(s),()=>"Error in conv2D: Dilated rates should be larger than 0."),i(yo(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=wa.runKernel(ve,h,f);return d?vo(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const jo=Aa({conv1d_:function(e,t,n,r,a="NWC",s=1,o){const u=Ea(e,"x","conv1d"),l=Ea(t,"filter","conv1d");let c=u,d=!1;2===u.rank&&(d=!0,c=vo(u,[1,u.shape[0],u.shape[1]])),i(3===c.rank,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),i(3===l.rank,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),xo("conv1d",r,o),i(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),i(go(n,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${s}'`),i(yo(s),()=>"Error in conv1D: Dilated rates should be larger than 0."),i(yo(n),()=>"Error in conv1D: Stride should be larger than 0."),i("NWC"===a,()=>`Error in conv1d: got dataFormat of ${a} but only NWC is currently supported.`);const p=vo(l,[1,l.shape[0],l.shape[1],l.shape[2]]),h=vo(c,[c.shape[0],1,c.shape[1],c.shape[2]]),f=Ho(h,p,[1,n],r,"NHWC",[1,s],o);return vo(f,d?[f.shape[2],f.shape[3]]:[f.shape[0],f.shape[2],f.shape[3]])}});const qo=Aa({conv2DBackpropInput_:function(e,t,n,r,a,s="NHWC",o){i(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let u=e,l=t,c=!1;3===t.rank&&(c=!0,l=vo(t,[1,t.shape[0],t.shape[1],t.shape[2]]),u=[1,e[0],e[1],e[2]]),i(4===u.length,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${u.length}.`),i(4===l.rank,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),i(4===n.rank,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);const d="NHWC"===s?u[3]:u[1],p="NHWC"===s?l.shape[3]:l.shape[1];i(d===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${d}) must match input depth for filter ${n.shape[2]}.`),i(p===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`),xo("conv2dDerInput",a,o);const h={dy:l,filter:n},f={strides:r,pad:a,dataFormat:s,dimRoundingMode:o,inputShape:u},m=wa.runKernel(ke,h,f);return c?vo(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const Ko=Aa({conv2dTranspose_:function(e,t,n,r,a,s){const o=Ea(e,"x","conv2dTranspose"),i=Ea(t,"filter","conv2dTranspose");return qo(n,o,i,r,a,"NHWC",s)}});const Xo=Aa({conv3d_:function(e,t,n,r,a="NDHWC",s=[1,1,1]){const o=Ea(e,"x","conv3d"),u=Ea(t,"filter","conv3d");let l=o,c=!1;4===o.rank&&(c=!0,l=vo(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),i(5===l.rank,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),i(5===u.rank,()=>`Error in conv3d: filter must be rank 5, but got rank ${u.rank}.`),i(l.shape[4]===u.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${u.shape[3]}.`),i(go(n,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),i("NDHWC"===a,()=>`Error in conv3d: got dataFormat of ${a} but only NDHWC is currently supported.`),i(yo(s),()=>"Error in conv3D: Dilated rates should be larger than 0."),i(yo(n),()=>"Error in conv3D: Strides should be larger than 0.");const d={x:l,filter:u},p={strides:n,pad:r,dataFormat:a,dilations:s},h=wa.runKernel(Ie,d,p);return c?vo(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}});const Yo=Aa({conv3DBackpropInput_:function(e,t,n,r,a){i(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let s=e,o=t,u=!1;4===t.rank&&(u=!0,o=vo(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];i(5===s.length,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),i(5===o.rank,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),i(5===n.rank,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),i(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),i(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);const d={dy:o,filter:n},p={pad:a,strides:r,inputShape:s},h=wa.runKernel(Se,d,p);return u?vo(h,[h.shape[1],h.shape[2],h.shape[3],h.shape[4]]):h}});const Qo=Aa({conv3dTranspose_:function(e,t,n,r,a){const s=Ea(e,"x","conv3dTranspose"),o=Ea(t,"filter","conv3dTranspose");return Yo(n,s,o,r,a)}});const Zo=Aa({cos_:function(e){const t={x:Ea(e,"x","cos","float32")};return wa.runKernel(Te,t)}});const Jo=Aa({cosh_:function(e){const t={x:Ea(e,"x","cosh","float32")};return wa.runKernel(Ce,t)}});const ei=Aa({cumprod_:function(e,t=0,n=!1,r=!1){const a={x:Ea(e,"x","cumprod")},s={axis:t,exclusive:n,reverse:r};return wa.runKernel($e,a,s)}});const ti=Aa({cumsum_:function(e,t=0,n=!1,r=!1){const a={x:Ea(e,"x","cumsum")},s={axis:t,exclusive:n,reverse:r};return wa.runKernel(Ee,a,s)}});const ni=Aa({denseBincount_:function(e,t,n,r=!1){const a=Ea(e,"x","denseBincount"),s=Ea(t,"weights","denseBincount");i("int32"===a.dtype,()=>`Error in denseBincount: input dtype must be int32, but got ${a.dtype}`),i(a.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${a.rank}.`),i(n>=0,()=>`size must be non-negative, but got ${n}.`),i(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},u={size:n,binaryOutput:r};return wa.runKernel(_e,o,u)}});const ri=Aa({depthToSpace_:function(e,t,n="NHWC"){const r=Ea(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];i(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),i(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${a} and ${t} for depthToSpace with input shape\n ${r.shape}`),i(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${s} and ${t} for depthToSpace with input shape\n ${r.shape}`),i(o%(t*t)===0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${r.shape}`);const u={x:r},l={blockSize:t,dataFormat:n};return wa.runKernel(Ae,u,l)}});const ai=Aa({depthwiseConv2d_:function(e,t,n,r,a="NHWC",s=[1,1],o){const u=Ea(e,"x","depthwiseConv2d","float32"),l=Ea(t,"filter","depthwiseConv2d","float32");let c=u,d=!1;3===u.rank&&(d=!0,c=vo(u,[1,u.shape[0],u.shape[1],u.shape[2]])),i(4===c.rank,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),i(4===l.rank,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`);const p="NHWC"===a?c.shape[3]:c.shape[1];i(p===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${p}) must match the inChannels dimension in filter ${l.shape[2]}.`),xo("depthwiseConv2d",r,o);const h={x:c,filter:l},f={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:o},m=wa.runKernel(Oe,h,f);return d?vo(m,[m.shape[1],m.shape[2],m.shape[3]]):m}});const si=Aa({diag_:function(e){const t={x:Ea(e,"x","diag")};return wa.runKernel(Me,t)}});const oi=Aa({dilation2d_:function(e,t,n,r,a=[1,1],s="NHWC"){const o=Ea(e,"x","dilation2d"),u=Ea(t,"filter","dilation2d");i(3===o.rank||4===o.rank,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),i(3===u.rank,()=>`Error in dilation2d: filter must be rank 3, but got rank ${u.rank}.`),i("NHWC"===s,()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=o,c=!1;3===o.rank&&(l=vo(o,[1,o.shape[0],o.shape[1],o.shape[2]]),c=!0),i(l.shape[3]===u.shape[2],()=>`Error in dilation2d: input and filter must have the same depth: ${l.shape[3]} vs ${u.shape[2]}`);const d={x:l,filter:u},p={strides:n,pad:r,dilations:a},h=wa.runKernel(Pe,d,p);return c?vo(h,[h.shape[1],h.shape[2],h.shape[3]]):h}});function ii(e,t){const n=e.length,r=[];for(let a=0;a1&&1===o&&r.unshift(s)}return r}function ui(e,t){const n=[];for(let r=0;r1)&&n.unshift(s)}return n}function li(e,t){const n=Math.max(e.length,t.length),r=new Array(n);for(let a=0;a`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(i(a===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${a} and ${s}.`),1===n.rank&&1===r.rank){const e=vo(n,[1,-1]),t=vo(r,[-1,1]),a=No(e,t);return vo(a,[])}if(1===n.rank&&2===r.rank){const e=vo(n,[1,-1]),t=vo(r,[r.shape[0],r.shape[1]]),a=No(e,t);return vo(a,[a.size])}if(2===n.rank&&1===r.rank){const e=vo(r,[-1,1]),t=No(n,e);return vo(t,[t.size])}{const e=vo(r,[r.shape[0],r.shape[1]]);return No(n,e)}}});const mi=Aa({einsum_:function(e,...t){const n=t.map((e,t)=>Ea(e,`tensors${t}`,"einsum")),r={equation:e};return wa.runKernel(ze,n,r)}});const gi=Aa({elu_:function(e){const t={x:Ea(e,"x","elu","float32")};return wa.runKernel(Ue,t)}});const yi=Aa({ensureShape_:function(e,t){const n=Ea(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"Input dtype must be `int32` or `float32`."),"int32"===t.dtype&&(t=Ls(t,"float32"));const n={x:t};return wa.runKernel(He,n)}});function xi(e,t){for(let n=0;ne[t])]}function ki(e,t){return vi(e,t.map(e=>1),t)}function Ii(e,t,n){i(xi(t,n),()=>`${e} supports only inner-most axes for now. 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i(4===n.rank,()=>`Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`),El(n,t)}});const Fl=Aa({round_:function(e){const t={x:Ea(e,"x","round")};return wa.runKernel(cn,t)}});const Dl=Aa({rsqrt_:function(e){const t={x:Ea(e,"x","rsqrt","float32")};return wa.runKernel(dn,t)}});const Ml=Aa({selu_:function(e){const t={x:Ea(e,"x","selu")};return wa.runKernel(gn,t)}});const Pl=Aa({separableConv2d_:function(e,t,n,r,a,s=[1,1],o="NHWC"){const u=Ea(e,"x","separableConv2d"),l=Ea(t,"depthwiseFilter","separableConv2d"),c=Ea(n,"pointwiseFilter","separableConv2d");let d=u,p=!1;if(3===u.rank&&(p=!0,d=vo(u,[1,u.shape[0],u.shape[1],u.shape[2]])),"NCHW"===o)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");i(4===d.rank,()=>`Error in separableConv2d: input must be rank 4, but got rank ${d.rank}.`),i(4===l.rank,()=>`Error in separableConv2d: depthwise filter must be rank 4, but got rank ${l.rank}.`),i(4===c.rank,()=>`Error in separableConv2d: 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uc(e,t,n){if(l(e),null!=t&&5!==t.length)throw new Error("tensor5d() requires shape to have five numbers");const r=Ta(e,n);if(5!==r.length&&1!==r.length)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return Fa(e,t,r,n)}function lc(e,t,n){if(l(e),null!=t&&6!==t.length)throw new Error("tensor6d() requires shape to have six numbers");const r=Ta(e,n);if(6!==r.length&&1!==r.length)throw new Error("tensor6d() requires values to be number[][][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return Fa(e,t=t||r,r,n)}function cc(e,t,n){const r=t.rank>1?t.shape[t.rank-1]:1,a=t.rank>1?t.rank-1:1,s=`Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: ${n.shape}, indices.shape: ${t.shape}, shape: ${e}, sliceDim: 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matrix, but got shape ${e.shape}.`);const a=e.rank>0?e.shape[0]:1,s=e.rank>1?e.shape[1]:1;if(n.length!==s)throw new Error(`outputShape has incorrect number of elements:, ${n.length}, should be: ${s}.`);const o=t.size;if(0!==t.rank&&(1!==t.rank||o!==a))throw new Error(`sparseValues has incorrect shape ${t.shape}, should be [] or [${a}]`);if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}(a,s,n,o);const i={sparseIndices:a,sparseValues:s,defaultValue:o},u={outputShape:n};return wa.runKernel(An,i,u)}});const $c=Aa({gatherND_:function(e,t){const n=Ea(t,"indices","gatherND","int32"),r={params:Ea(e,"x","gatherND","string_or_numeric"),indices:n};return wa.runKernel(rt,r)}});const Ec=Aa({dropout_:function(e,t,n,r){const a=Ea(e,"x","dropout");if(i("float32"===a.dtype,()=>`x has to be a floating point tensor since it's going to be scaled, but got a ${a.dtype} tensor instead.`),i(t>=0&&t<1,()=>`rate must be a float in the range [0, 1), but got ${t}.`),0===t)return e instanceof Yr?a.clone():a;const s=function(e,t){if(null==t)return e.shape.slice();if(d(e.shape,t))return t;if(e.shape.length===t.length){const n=[];for(let r=0;r1,()=>`inTopK() expects the predictions to be of rank 2 or higher, but got ${r.rank}`),i(r.rank-1===a.rank,()=>`predictions rank should be 1 larger than targets rank, but got predictions rank ${r.rank} and targets rank ${a.rank}`),u(r.shape.slice(0,r.shape.length-1),a.shape,"predictions's shape should be align with the targets' shape, except the last dimension.");const s=r.shape[r.shape.length-1];i(n>0&&n<=s,()=>`'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${s}), but got ${n}`);const o=await r.data(),l=await a.data(),[c,d]=[o.length/s,s],p=x("bool",c);for(let i=0;it.value-e.value),p[i]=0;for(let a=0;a`Error in conv2dDerFilter: input must be rank 4, but got shape ${u.shape}.`),i(4===l.rank,()=>`Error in conv2dDerFilter: dy must be rank 4, but got shape ${l.shape}.`),i(4===n.length,()=>`Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);const c="NHWC"===s?u.shape[3]:u.shape[1],d="NHWC"===s?l.shape[3]:l.shape[1];i(c===n[2],()=>`Error in conv2dDerFilter: depth of input ${c}) must match input depth in filter (${n[2]}.`),i(d===n[3],()=>`Error in conv2dDerFilter: depth of dy (${d}) must match output depth for filter (${n[3]}).`),xo("conv2dDerFilter",a,o);const p={x:u,dy:l},h={strides:r,pad:a,dataFormat:s,dimRoundingMode:o,filterShape:n};return wa.runKernel(we,p,h)}});function Fc(e,t,n){if(null==n||"linear"===n)return e;if("relu"===n)return Gs(e,tc(t));throw new Error(`Cannot compute gradient for fused activation ${n}.`)}function Dc(e,t){let n=t;const r=ui(e.shape,t.shape);return r.length>0&&(n=Oi(n,r)),vo(n,e.shape)}function Mc(e,t,n,r){if("linear"===t)return e;if("relu"===t)return Cl(e);if("elu"===t)return gi(e);if("relu6"===t)return $l(e);if("prelu"===t)return Uu(e,n);if("leakyrelu"===t)return Yi(e,r);if("sigmoid"===t)return So(e);throw new Error(`Unknown fused activation ${t}.`)}const Pc=(e,t)=>!(e>0)||"linear"===t;const Lc=Aa({fusedConv2d_:function({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:o,bias:u,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:d}){if(l=l||"linear",!1===Pc(wa.state.gradientDepth,l)){i("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=Ho(e,t,n,r,a,s,o);return null!=u&&(p=Ws(p,u)),Mc(p,l,c,d)}const p=Ea(e,"x","conv2d","float32"),h=Ea(t,"filter","conv2d","float32");let f=p,m=!1;3===p.rank&&(m=!0,f=vo(p,[1,p.shape[0],p.shape[1],p.shape[2]])),i(4===f.rank,()=>`Error in fused conv2d: input must be rank 4, but got rank ${f.rank}.`),i(4===h.rank,()=>`Error in fused conv2d: filter must be rank 4, but got rank ${h.rank}.`),xo("fused conv2d",r,o);const g="NHWC"===a?f.shape[3]:f.shape[1];i(h.shape[2]===g,()=>`Error in conv2d: depth of input (${g}) must match input depth for filter ${h.shape[2]}.`),i(go(n,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`);const y=io(f.shape,h.shape,n,s,r,o);let b,x;if(null!=u&&(b=Ea(u,"bias","fused conv2d"),[b]=fa(b,p),"NHWC"===a?li(y.outShape,b.shape):(i(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-${b.shape.length}.`),i(0===b.shape.length||b.shape[0]===y.outChannels||1===b.shape[0],()=>`Error in fused conv2d: bias shape (${b.shape}) is not compatible with the number of output channels (${y.outChannels})`))),null!=c){const e=c.shape;if(i(e.length<=1||3===e.length,()=>`Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${e.length}.`),1===e.length)i(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{li(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=Ea(c,"prelu weights","fused conv2d")}const v=(e,t)=>{i("NHWC"===a,()=>`Error in gradient of fused conv2D: got dataFormat of ${a} but only NHWC is currently supported.`);const[o,u,c,d]=t,p=Fc(e,c,l);i(mo(s),()=>`Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);const h=[qo(u.shape,p,o,n,r),Oc(u,p,o.shape,n,r)];if(null!=d){const e=Dc(d,p);h.push(e)}return h},w={x:f,filter:h,bias:b,preluActivationWeights:x},k={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:o,activation:l,leakyreluAlpha:d};if(null==u){return au((e,t,n)=>{let r=wa.runKernel(tr,w,k);return n([t,e,r]),m&&(r=vo(r,[r.shape[1],r.shape[2],r.shape[3]])),{value:r,gradFunc:v}})(f,h)}return au((e,t,n,r)=>{let a=wa.runKernel(tr,w,k);return r([t,e,a,n]),m&&(a=vo(a,[a.shape[1],a.shape[2],a.shape[3]])),{value:a,gradFunc:v}})(f,h,b)}});const Bc=Aa({depthwiseConv2dNativeBackpropFilter_:function(e,t,n,r,a,s=[1,1],o){let i=e;3===e.rank&&(i=vo(e,[1,e.shape[0],e.shape[1],e.shape[2]]));let u=t;3===u.rank&&(u=vo(t,[1,t.shape[0],t.shape[1],t.shape[2]]));const l={x:i,dy:u},c={strides:r,pad:a,dimRoundingMode:o,dilations:s,filterShape:n};return wa.runKernel(Fe,l,c)}});const Vc=Aa({depthwiseConv2dNativeBackpropInput_:function(e,t,n,r,a,s=[1,1],o){let i=t,u=!1;3===t.rank&&(u=!0,i=vo(t,[1,t.shape[0],t.shape[1],t.shape[2]]));const l={dy:i,filter:n},c={strides:r,pad:a,dimRoundingMode:o,dilations:s,inputShape:e},d=wa.runKernel(De,l,c);return u?vo(d,[d.shape[1],d.shape[2],d.shape[3]]):d}});const Wc=Aa({fusedDepthwiseConv2d_:function({x:e,filter:t,strides:n,pad:r,dataFormat:a="NHWC",dilations:s=[1,1],dimRoundingMode:o,bias:u,activation:l="linear",preluActivationWeights:c,leakyreluAlpha:d}){if(!1===Pc(wa.state.gradientDepth,l)){let i=ai(e,t,n,r,a,s,o);return null!=u&&(i=Ws(i,u)),Mc(i,l,c,d)}const p=Ea(e,"x","depthwiseConv2d","float32"),h=Ea(t,"filter","depthwiseConv2d","float32");let f=p,m=!1;3===p.rank&&(m=!0,f=vo(p,[1,p.shape[0],p.shape[1],p.shape[2]])),i(4===f.rank,()=>`Error in fused depthwiseConv2d: input must be rank 4, but got rank ${f.rank}.`),i(4===h.rank,()=>`Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${h.rank}.`),i(f.shape[3]===h.shape[2],()=>`Error in fused depthwiseConv2d: number of input channels (${f.shape[3]}) must match the inChannels dimension in filter ${h.shape[2]}.`),null==s&&(s=[1,1]),i(go(n,s),()=>`Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${s}'`),xo("fused depthwiseConv2d",r,o);const g=io(f.shape,h.shape,n,s,r,o,!0);let y,b;null!=u&&(y=Ea(u,"bias","fused conv2d"),[y]=fa(y,p),li(g.outShape,y.shape)),null!=c&&(b=Ea(c,"prelu weights","fused depthwiseConv2d"));const x=(e,t)=>{i(mo(s),()=>`Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${s}'`);const[a,u,c,d]=t,p=Fc(e,c,l),h=Vc(u.shape,p,a,n,r,s,o),f=Bc(u,p,a.shape,n,r,s,o);if(null!=d){return[h,f,Dc(y,p)]}return[h,f]},v={x:f,filter:h,bias:y,preluActivationWeights:b},w={strides:n,pad:r,dataFormat:a,dilations:s,dimRoundingMode:o,activation:l,leakyreluAlpha:d};if(null==u){return au((e,t,n)=>{let r=wa.runKernel(nr,v,w);return n([t,e,r]),m&&(r=vo(r,[r.shape[1],r.shape[2],r.shape[3]])),{value:r,gradFunc:x}})(f,h)}return au((e,t,n,r)=>{let a=wa.runKernel(nr,v,w);return r([t,e,a,n]),m&&(a=vo(a,[a.shape[1],a.shape[2],a.shape[3]])),{value:a,gradFunc:x}})(f,h,y)}});const zc=Aa({fusedMatMul_:function({a:e,b:t,transposeA:n=!1,transposeB:r=!1,bias:a,activation:s="linear",preluActivationWeights:o,leakyreluAlpha:u=.2}){if(!1===Pc(wa.state.gradientDepth,s)){let i=No(e,t,n,r);return null!=a&&(i=Ws(i,a)),Mc(i,s,o,u)}let l=Ea(e,"a","fused matMul"),d=Ea(t,"b","fused matMul");[l,d]=fa(l,d);const p=n?l.shape[l.rank-2]:l.shape[l.rank-1],h=r?d.shape[d.rank-1]:d.shape[d.rank-2],f=n?l.shape[l.rank-1]:l.shape[l.rank-2],m=r?d.shape[d.rank-2]:d.shape[d.rank-1],g=l.shape.slice(0,-2),y=d.shape.slice(0,-2),b=c(g),x=c(y);i(p===h,()=>`Error in fused matMul: inner shapes (${p}) and (${h}) of Tensors with shapes ${l.shape} and ${d.shape} and transposeA=${n} and transposeB=${r} must match.`);const v=li(l.shape.slice(0,-2),d.shape.slice(0,-2)).concat([f,m]),w=vo(l,n?[b,p,f]:[b,f,p]),k=vo(d,r?[x,m,h]:[x,h,m]);let I,N;null!=a&&(I=Ea(a,"bias","fused matMul"),[I]=fa(I,l),li(v,I.shape)),null!=o&&(N=Ea(o,"prelu weights","fused matMul"));const S=(e,t)=>{const[o,i,u,l]=t,c=Fc(vo(e,u.shape),u,s);let d,p;if(n||r?!n&&r?(d=No(c,i,!1,!1),p=No(c,o,!0,!1)):n&&!r?(d=No(i,c,!1,!0),p=No(o,c,!1,!1)):(d=No(i,c,!0,!0),p=No(c,o,!0,!0)):(d=No(c,i,!1,!0),p=No(o,c,!0,!1)),null!=a){return[d,p,Dc(l,c)]}return[d,p]},T={a:w,b:k,bias:I,preluActivationWeights:N},C={transposeA:n,transposeB:r,activation:s,leakyreluAlpha:u};if(null==a){return au((e,t,n)=>{const r=wa.runKernel(er,T,C);return n([e,t,r]),{value:vo(r,v),gradFunc:S}})(w,k)}return au((e,t,n,r)=>{const a=wa.runKernel(er,T,C);return r([e,t,a,n]),{value:vo(a,v),gradFunc:S}})(w,k,I)}}),Uc=Object.freeze(Object.defineProperty({__proto__:null,conv2d:Lc,depthwiseConv2d:Wc,matMul:zc},Symbol.toStringTag,{value:"Module"}));const Gc=Aa({hammingWindow_:function(e){return _c(e,.54,.46)}});const Hc=Aa({hannWindow_:function(e){return _c(e,.5,.5)}});const jc=Aa({frame_:function(e,t,n,r=!1,a=0){let s=0;const o=[];for(;s+t<=e.size;)o.push(To(e,s,t)),s+=n;if(r)for(;s`Error in cropAndResize: image must be rank 4,but got rank 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images must be at least rank 2, but got rank ${t.rank}.`),i(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,Vi(t,a)}});const Qc=Aa({rgbToGrayscale_:function(e){const t=Ea(e,"image","RGBToGrayscale"),n=t.rank-1,r=t.shape[n];i(t.rank>=2,()=>`Error in RGBToGrayscale: images must be at least rank 2, but got rank ${t.rank}.`),i(3===r,()=>`Error in RGBToGrayscale: last dimension of an RGB image should be size 3, but got size ${r}.`);const a=t.dtype,s=Ls(t,"float32"),o=ac([.2989,.587,.114]);let u;switch(t.rank){case 2:u=mi("ij,j->i",s,o);break;case 3:u=mi("ijk,k->ij",s,o);break;case 4:u=mi("ijkl,l->ijk",s,o);break;case 5:u=mi("ijklm,m->ijkl",s,o);break;case 6:u=mi("ijklmn,n->ijklm",s,o);break;default:throw new Error("Not a valid tensor rank.")}return u=Li(u,-1),Ls(u,a)}});const Zc=Aa({rotateWithOffset_:function(e,t,n=0,r=.5){const a=Ea(e,"image","rotateWithOffset","float32");i(4===a.rank,()=>`Error in rotateWithOffset: image must be rank 4,but got rank ${a.rank}.`);const s={image:a},o={radians:t,fillValue:n,center:r};return wa.runKernel(Jn,s,o)}});function Jc(e,t,n,r,a,s){null==r&&(r=.5),null==a&&(a=Number.NEGATIVE_INFINITY),null==s&&(s=0);const o=e.shape[0];return n=Math.min(n,o),i(0<=r&&r<=1,()=>`iouThreshold must be in [0, 1], but was '${r}'`),i(2===e.rank,()=>`boxes must be a 2D tensor, but was of rank '${e.rank}'`),i(4===e.shape[1],()=>`boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`),i(1===t.rank,()=>"scores must be a 1D tensor"),i(t.shape[0]===o,()=>`scores has incompatible shape with boxes. Expected ${o}, but was ${t.shape[0]}`),i(0<=s&&s<=1,()=>`softNmsSigma must be in [0, 1], but was '${s}'`),{maxOutputSize:n,iouThreshold:r,scoreThreshold:a,softNmsSigma:s}}const ed=Aa({nonMaxSuppression_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){const s=Ea(e,"boxes","nonMaxSuppression","float32"),o=Ea(t,"scores","nonMaxSuppression","float32"),i=Jc(s,o,n,r,a),u={maxOutputSize:n=i.maxOutputSize,iouThreshold:r=i.iouThreshold,scoreThreshold:a=i.scoreThreshold};return wa.runKernel(Bt,{boxes:s,scores:o},u)}});function td(e,t,n){const r=function(e,t,n){return function(e,t,n){let r=0,a=e.length,s=0,o=!1;for(;r>>1);const i=n(t,e[s]);i>0?r=s+1:(a=s,o=!i)}return o?r:-r-1}(e,t,n||nd)}(e,t,n),a=r<0?-(r+1):r;e.splice(a,0,t)}function nd(e,t){return e>t?1:ea&&l.push({score:t[g],boxIndex:g,suppressBeginIndex:0});l.sort(ld);const c=s>0?-.5/s:0,d=[],p=[];for(;d.length0;){const t=l.pop(),{score:n,boxIndex:s,suppressBeginIndex:o}=t;if(n=o;--u){const n=id(e,s,d[u]);if(n>=r){i=!0;break}if(t.score=t.score*ud(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&&td(l,t,ld))}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 id(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 ud(e,t,n){const r=Math.exp(t*n*n);return n<=e?r:0}function ld(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}const cd=async function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY){const s=Ea(e,"boxes","nonMaxSuppressionAsync"),o=Ea(t,"scores","nonMaxSuppressionAsync"),i=Jc(s,o,n,r,a);n=i.maxOutputSize,r=i.iouThreshold,a=i.scoreThreshold;const u=await Promise.all([s.data(),o.data()]),l=u[0],c=u[1],{selectedIndices:d}=rd(l,c,n,r,a);return s!==e&&s.dispose(),o!==t&&o.dispose(),ac(d,"int32")};const dd=Aa({nonMaxSuppressionWithScore_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){const o=Ea(e,"boxes","nonMaxSuppression"),i=Ea(t,"scores","nonMaxSuppression"),u=Jc(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=wa.runKernel(Wt,l,c);return{selectedIndices:d[0],selectedScores:d[1]}}});const pd=async function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=0){const o=Ea(e,"boxes","nonMaxSuppressionAsync"),i=Ea(t,"scores","nonMaxSuppressionAsync"),u=Jc(o,i,n,r,a,s);n=u.maxOutputSize,r=u.iouThreshold,a=u.scoreThreshold,s=u.softNmsSigma;const l=await Promise.all([o.data(),i.data()]),c=l[0],d=l[1],{selectedIndices:p,selectedScores:h}=sd(c,d,n,r,a,s);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:ac(p,"int32"),selectedScores:ac(h)}};const hd=Aa({nonMaxSuppressionPadded_:function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){const o=Ea(e,"boxes","nonMaxSuppression"),i=Ea(t,"scores","nonMaxSuppression"),u=Jc(o,i,n,r,a,null),l={boxes:o,scores:i},c={maxOutputSize:u.maxOutputSize,iouThreshold:u.iouThreshold,scoreThreshold:u.scoreThreshold,padToMaxOutputSize:s},d=wa.runKernel(Vt,l,c);return{selectedIndices:d[0],validOutputs:d[1]}}});const fd=async function(e,t,n,r=.5,a=Number.NEGATIVE_INFINITY,s=!1){const o=Ea(e,"boxes","nonMaxSuppressionAsync"),i=Ea(t,"scores","nonMaxSuppressionAsync"),u=Jc(o,i,n,r,a,null),l=u.maxOutputSize,c=u.iouThreshold,d=u.scoreThreshold,[p,h]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=ad(p,h,l,c,d,s);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:ac(f,"int32"),validOutputs:Ri(m,"int32")}};const md=Aa({resizeBilinear_:function(e,t,n=!1,r=!1){const a=Ea(e,"images","resizeBilinear");i(3===a.rank||4===a.rank,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${a.rank}.`),i(2===t.length,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${t}.`),i(!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=vo(a,[1,a.shape[0],a.shape[1],a.shape[2]]));const u={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=wa.runKernel(sn,u,l);return o?vo(c,[c.shape[1],c.shape[2],c.shape[3]]):c}});const gd=Aa({resizeNearestNeighbor_:function(e,t,n=!1,r=!1){const a=Ea(e,"images","resizeNearestNeighbor");i(3===a.rank||4===a.rank,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${a.rank}.`),i(2===t.length,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`),i("float32"===a.dtype||"int32"===a.dtype,()=>"`images` must have `int32` or `float32` as dtype"),i(!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=vo(a,[1,a.shape[0],a.shape[1],a.shape[2]]));const u={images:s},l={alignCorners:n,halfPixelCenters:r,size:t},c=wa.runKernel(rn,u,l);return o?vo(c,[c.shape[1],c.shape[2],c.shape[3]]):c}});const yd=Aa({threshold_:function(e,t="binary",n=!1,r=.5){const a=Ea(e,"image","threshold"),s=a.shape[0]*a.shape[1];let o,u,l,c,d=Gs(ac([r]),255);if(i(3===a.rank,()=>`Error in threshold: image must be rank 3,but got rank ${a.rank}.`),i(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]}.`),i("int32"===a.dtype||"float32"===a.dtype,()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${a.dtype}.`),i("otsu"===t||"binary"===t,()=>`Method must be binary or otsu, but was ${t}`),3===a.shape[2]){[o,u,l]=Yl(a,[1,1,1],-1);const e=Gs(o,.2989),t=Gs(u,.587),n=Gs(l,.114);c=Ws(Ws(e,t),n)}else c=e;if("otsu"===t){d=function(e,t){let n,r,a,s,o,i,u=ac([-1]),l=ac([0]),c=ac([0]);for(let d=0;d`Error in transform: image must be rank 4,but got rank ${o.rank}.`),i(2===u.rank&&(u.shape[0]===o.shape[0]||1===u.shape[0])&&8===u.shape[1],()=>"Error in transform: Input transform should be batch x 8 or 1 x 8"),i(null==s||2===s.length,()=>`Error in transform: outputShape must be [height, width] or null, but got ${s}.`);const l={image:o,transforms:u},c={interpolation:n,fillMode:r,fillValue:a,outputShape:s};return wa.runKernel(Hn,l,c)}});const xd=Aa({bandPart_:function(e,t,n){const r=Ea(e,"a","bandPart");i(r.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${r.rank}.`);const a=r.shape,[s,o]=r.shape.slice(-2);let u,l;"number"===typeof t?(i(t%1===0,()=>`bandPart(): numLower must be an integer, got ${t}.`),i(t<=s,()=>`bandPart(): numLower (${t}) must not be greater than the number of rows (${s}).`),u=Ea(t<0?s:t,"numLower","bandPart")):(i("int32"===t.dtype,()=>"bandPart(): numLower's dtype must be an int32."),u=di(Qi(t,0),s,Tu(t,s))),"number"===typeof n?(i(n%1===0,()=>`bandPart(): numUpper must be an integer, got ${n}.`),i(n<=o,()=>`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`),l=Ea(n<0?o:n,"numUpper","bandPart")):(i("int32"===n.dtype,()=>"bandPart(): numUpper's dtype must be an int32."),l=di(Qi(n,0),o,Tu(n,o)));const c=vo(Nl(0,s,1,"int32"),[-1,1]),d=Nl(0,o,1,"int32"),p=uu(c,d),h=du(Zi(p,u),Hi(p,su(l))),f=Iu([s,o],r.dtype);return vo(ec(bc(vo(r,[-1,s,o])).map(e=>di(h,e,f))),a)}});const vd=Aa({gramSchmidt_:function(e){let t;if(Array.isArray(e)){t=!1,i(null!=e&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");const n=e[0].shape[0];for(let t=1;t`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[t].shape[0]} vs. ${n})`)}else t=!0,e=Yl(e,e.shape[0],0).map(e=>Jl(e,[0]));i(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);const n=[],r=e;for(let a=0;a{let e=r[a];if(a>0)for(let t=0;t{i(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=Wi(n),s=Bs(e);const o=sc([[1]],[1,1]);let u=Bs(o);const l=n>=r?r:n;for(let e=0;e{const t=To(s,[e,e],[n-e,1]),i=Di(t),l=To(s,[e,e],[1,1]),c=di(Gi(l,0),sc([[-1]]),sc([[1]])),d=uu(l,Gs(c,i)),p=Us(t,d);u=1===p.shape[0]?Bs(o):Io([o,To(p,[1,0],[p.shape[0]-1,p.shape[1]])],0);const h=su(Us(No(c,d),i)),f=To(s,[e,0],[n-e,r]),m=Gs(h,u),g=Nc(u);if(0===e)s=uu(f,No(m,No(g,f)));else{const t=uu(f,No(m,No(g,f)));s=Io([To(s,[0,0],[e,r]),t],0)}const y=Nc(m),b=To(a,[0,e],[n,a.shape[1]-e]);if(0===e)a=uu(b,No(No(b,u),y));else{const t=uu(b,No(No(b,u),y));a=Io([To(a,[0,0],[n,e]),t],1)}return[u,s,a]}),Va([t,i,l])}return!t&&n>r&&(a=To(a,[0,0],[n,r]),s=To(s,[0,0],[r,r])),[a,s]})}const kd=Aa({qr_:function(e,t=!1){if(i(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),2===e.rank)return wd(e,t);{const n=e.shape.slice(0,e.shape.length-2).reduce((e,t)=>e*t),r=bc(vo(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),a=[],s=[];r.forEach(e=>{const[n,r]=wd(e,t);a.push(n),s.push(r)});return[vo(ec(a,0),e.shape),vo(ec(s,0),e.shape)]}}});var Id,Nd;(Nd=Id||(Id={}))[Nd.NONE=0]="NONE",Nd[Nd.MEAN=1]="MEAN",Nd[Nd.SUM=2]="SUM",Nd[Nd.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";const Sd=Aa({computeWeightedLoss_:function(e,t,n=Id.SUM_BY_NONZERO_WEIGHTS){const r=Ea(e,"losses","computeWeightedLoss");let a=null;null!=t&&(a=Ea(t,"weights","computeWeightedLoss"));const s=null==a?r:Gs(r,a);if(n===Id.NONE)return s;if(n===Id.SUM)return Oi(s);if(n===Id.MEAN){if(null==a)return ku(s);{const e=r.size/a.size,t=Us(Oi(s),Oi(a));return e>1?Us(t,Ri(e)):t}}if(n===Id.SUM_BY_NONZERO_WEIGHTS){if(null==a)return Us(Oi(s),Ri(r.size));{const e=Gs(a,Nu(r.shape)),t=Ls(Oi(Au(e,Ri(0))),"float32");return Us(Oi(s),t)}}throw Error(`Unknown reduction: ${n}`)}});const Td=Aa({absoluteDifference_:function(e,t,n,r=Id.SUM_BY_NONZERO_WEIGHTS){const a=Ea(e,"labels","absoluteDifference"),s=Ea(t,"predictions","absoluteDifference");let o=null;null!=n&&(o=Ea(n,"weights","absoluteDifference")),u(a.shape,s.shape,"Error in absoluteDifference: ");const i=Hs(uu(a,s));return Sd(i,o,r)}});const Cd=Aa({cosineDistance_:function(e,t,n,r,a=Id.SUM_BY_NONZERO_WEIGHTS){const s=Ea(e,"labels","cosineDistance"),o=Ea(t,"predictions","cosineDistance");let i=null;null!=r&&(i=Ea(r,"weights","cosineDistance")),u(s.shape,o.shape,"Error in cosineDistance: ");const l=Ri(1),c=uu(l,Oi(Gs(s,o),n,!0));return Sd(c,i,a)}});const $d=Aa({hingeLoss_:function(e,t,n,r=Id.SUM_BY_NONZERO_WEIGHTS){let a=Ea(e,"labels","hingeLoss");const s=Ea(t,"predictions","hingeLoss");let o=null;null!=n&&(o=Ea(n,"weights","hingeLoss")),u(a.shape,s.shape,"Error in hingeLoss: ");const i=Ri(1);a=uu(Gs(Ri(2),a),i);const l=Cl(uu(i,Gs(a,s)));return Sd(l,o,r)}});const Ed=Aa({huberLoss_:function(e,t,n,r=1,a=Id.SUM_BY_NONZERO_WEIGHTS){const s=Ea(e,"labels","huberLoss"),o=Ea(t,"predictions","huberLoss");let i=null;null!=n&&(i=Ea(n,"weights","huberLoss")),u(s.shape,o.shape,"Error in huberLoss: ");const l=Ri(r),c=Hs(uu(o,s)),d=Tu(c,l),p=uu(c,d),h=Ws(Gs(Ri(.5),Ai(d)),Gs(l,p));return Sd(h,i,a)}});const Rd=Aa({logLoss_:function(e,t,n,r=1e-7,a=Id.SUM_BY_NONZERO_WEIGHTS){const s=Ea(e,"labels","logLoss"),o=Ea(t,"predictions","logLoss");let i=null;null!=n&&(i=Ea(n,"weights","logLoss")),u(s.shape,o.shape,"Error in logLoss: ");const l=Ri(1),c=Ri(r),d=su(Gs(s,tu(Ws(o,c)))),p=Gs(uu(l,s),tu(Ws(uu(l,o),c))),h=uu(d,p);return Sd(h,i,a)}});const _d=Aa({meanSquaredError_:function(e,t,n,r=Id.SUM_BY_NONZERO_WEIGHTS){const a=Ea(e,"labels","meanSquaredError"),s=Ea(t,"predictions","meanSquaredError");let o=null;null!=n&&(o=Ea(n,"weights","meanSquaredError")),u(a.shape,s.shape,"Error in meanSquaredError: ");const i=Zl(a,s);return Sd(i,o,r)}});const Ad=Aa({sigmoidCrossEntropy_:function(e,t,n,r=0,a=Id.SUM_BY_NONZERO_WEIGHTS){let s=Ea(e,"multiClassLabels","sigmoidCrossEntropy");const o=Ea(t,"logits","sigmoidCrossEntropy");let i=null;if(null!=n&&(i=Ea(n,"weights","sigmoidCrossEntropy")),u(s.shape,o.shape,"Error in sigmoidCrossEntropy: "),r>0){const e=Ri(r),t=Ri(1),n=Ri(.5);s=Ws(Gs(s,uu(t,e)),Gs(n,e))}const l=function(e,t){const n=Ea(e,"labels","sigmoidCrossEntropyWithLogits"),r=Ea(t,"logits","sigmoidCrossEntropyWithLogits");u(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");const a=Cl(r),s=Gs(r,n),o=nu(Pi(su(Hs(r))));return Ws(uu(a,s),o)}(s,o);return Sd(l,i,a)}});const Od=Aa({softmaxCrossEntropy_:function(e,t,n,r=0,a=Id.SUM_BY_NONZERO_WEIGHTS){let s=Ea(e,"onehotLabels","softmaxCrossEntropy");const o=Ea(t,"logits","softmaxCrossEntropy");let i=null;if(null!=n&&(i=Ea(n,"weights","softmaxCrossEntropy")),u(s.shape,o.shape,"Error in softmaxCrossEntropy: "),r>0){const e=Ri(r),t=Ri(1),n=Ri(s.shape[1]);s=Ws(Gs(s,uu(t,e)),Us(e,n))}const l=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 ${n}`);return au((e,t,r)=>{const a=cu(t,[n],!0),s=uu(Ls(t,"float32"),a);r([e,s]);const o=su(Gs(s,e));return{value:Oi(o,[n]),gradFunc:(e,t)=>{const[r,a]=t,s=ki(e.shape,[n]);return[Gs(vo(e,s),uu(Ls(r,"float32"),Pi(a))),Gs(vo(e,s),uu(Pi(a),Ls(r,"float32")))]}}})(e,t)}(s,o);return Sd(l,i,a)}});const Fd={fft:ql,ifft:Kl,rfft:Ql,irfft:Xl},Dd={hammingWindow:Gc,hannWindow:Hc,frame:jc,stft:qc},Md={flipLeftRight:Xc,grayscaleToRGB:Yc,resizeNearestNeighbor:gd,resizeBilinear:md,rgbToGrayscale:Qc,rotateWithOffset:Zc,cropAndResize:Kc,nonMaxSuppression:ed,nonMaxSuppressionAsync:cd,nonMaxSuppressionWithScore:dd,nonMaxSuppressionWithScoreAsync:pd,nonMaxSuppressionPadded:hd,nonMaxSuppressionPaddedAsync:fd,threshold:yd,transform:bd},Pd={bandPart:xd,gramSchmidt:vd,qr:kd},Ld={absoluteDifference:Td,computeWeightedLoss:Sd,cosineDistance:Cd,hingeLoss:$d,huberLoss:Ed,logLoss:Rd,meanSquaredError:_d,sigmoidCrossEntropy:Ad,softmaxCrossEntropy:Od},Bd={sparseFillEmptyRows:Aa({sparseFillEmptyRows_:function(e,t,n,r){const a=Ea(e,"indices","sparseFillEmptyRows","int32"),s=Ea(t,"values","sparseFillEmptyRows"),o=Ea(n,"denseShape","sparseFillEmptyRows","int32"),i=Ea(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 ${i.shape}`);const u={indices:a,values:s,denseShape:o,defaultValue:i},l=wa.runKernel($n,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}}),sparseReshape:Aa({sparseReshape_:function(e,t,n){const r=Ea(e,"inputIndices","sparseReshape","int32"),a=Ea(t,"inputShape","sparseReshape","int32"),s=Ea(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=wa.runKernel(En,o);return{outputIndices:i[0],outputShape:i[1]}}}),sparseSegmentMean:Aa({sparseSegmentMean_:function(e,t,n){const r=Ea(e,"data","sparseSegmentMean"),a=Ea(t,"indices","sparseSegmentMean","int32"),s=Ea(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 wa.runKernel(Rn,o)}}),sparseSegmentSum:Aa({sparseSegmentSum_:function(e,t,n){const r=Ea(e,"data","sparseSegmentSum"),a=Ea(t,"indices","sparseSegmentSum","int32"),s=Ea(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 wa.runKernel(_n,o)}})},Vd={stringNGrams:Aa({stringNGrams_:function(e,t,n,r,a,s,o,i){const u=Ea(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=Ea(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=wa.runKernel(Pn,d,c);return{nGrams:p[0],nGramsSplits:p[1]}}}),stringSplit:Aa({stringSplit_:function(e,t,n=!0){const r=Ea(e,"input","stringSplit","string"),a=Ea(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=wa.runKernel(Ln,o,s);return{indices:i[0],values:i[1],shape:i[2]}}}),stringToHashBucketFast:Aa({stringToHashBucketFast_:function(e,t){const n=Ea(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 wa.runKernel(Bn,a,r)}}),staticRegexReplace:Aa({staticRegexReplace_:function(e,t,n,r=!0){const a=Ea(e,"input","staticRegexReplace","string"),s={pattern:t,rewrite:n,replaceGlobal:r};return wa.runKernel(Dn,{x:a},s)}})},Wd=new Map,zd=new Map;class Ud{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}}class Gd{constructor(){this.classNameMap={}}static getMap(){return null==Gd.instance&&(Gd.instance=new Gd),Gd.instance}static register(e){Gd.getMap().classNameMap[e.className]=[e,e.fromConfig]}}function Hd(e,t,n){i(null!=e.className,()=>"Class being registered does not have the static className property defined."),i("string"===typeof e.className,()=>"className is required to be a string, but got type "+typeof e.className),i(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 Gd.register(e),Wd.set(r,e),zd.set(e,r),e}class jd extends Ud{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 Va(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 ru(e,t)}dispose(){null!=this.iterations_&&Va(this.iterations_)}async saveIterations(){return null==this.iterations_&&(this.iterations_=0),{name:"iter",tensor:Ri(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}}Object.defineProperty(jd,Symbol.hasInstance,{value:e=>null!=e.minimize&&null!=e.computeGradients&&null!=e.applyGradients});class qd extends jd{static get className(){return"Adadelta"}constructor(e,t,n=null){super(),this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],null==n&&(this.epsilon=wa.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=wa.registeredVariables[t],a=!1;null==this.accumulatedGrads[n]&&(this.accumulatedGrads[n]={originalName:`${t}/accum_grad`,variable:Ba(()=>pi(r).variable(a))}),null==this.accumulatedUpdates[n]&&(this.accumulatedUpdates[n]={originalName:`${t}/accum_var`,variable:Ba(()=>pi(r).variable(a))});const s=Array.isArray(e)?e[n].tensor:e[t];if(null==s)return;const o=this.accumulatedGrads[n].variable,i=this.accumulatedUpdates[n].variable;Ba(()=>{const e=Ws(Gs(o,this.rho),Gs(Ai(s),1-this.rho)),t=Gs(Us(_i(Ws(i,this.epsilon)),_i(Ws(o,this.epsilon))),s),n=Ws(Gs(i,this.rho),Gs(Ai(t),1-this.rho));o.assign(e),i.assign(n);const a=Ws(Gs(t,-this.learningRate),r);r.assign(a)})}),this.incrementIterations()}dispose(){null!=this.accumulatedUpdates&&(Va(this.accumulatedGrads.map(e=>e.variable)),Va(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){const e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){const t=(e=await this.extractIterations(e)).length/2,n=!1;this.accumulatedGrads=e.slice(0,t).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.accumulatedUpdates=e.slice(t,2*t).map(e=>({originalName:e.name,variable:e.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}}class Kd extends jd{static get className(){return"Adagrad"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=wa.registeredVariables[t];if(null==this.accumulatedGrads[n]){const e=!1;this.accumulatedGrads[n]={originalName:`${t}/accumulator`,variable:Ba(()=>Bo(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;Ba(()=>{const e=Ws(s,Ai(a));s.assign(e);const t=Ws(Gs(Us(a,_i(Ws(e,wa.backend.epsilon()))),-this.learningRate),r);r.assign(t)})}),this.incrementIterations()}dispose(){null!=this.accumulatedGrads&&Va(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);this.accumulatedGrads=e.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 Xd extends jd{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=[],Ba(()=>{this.accBeta1=Ri(t).variable(),this.accBeta2=Ri(n).variable()}),null==r&&(this.epsilon=wa.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(e=>e.name):Object.keys(e);Ba(()=>{const n=uu(1,this.accBeta1),r=uu(1,this.accBeta2);t.forEach((t,a)=>{const s=wa.registeredVariables[t],o=!1;null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:Ba(()=>pi(s).variable(o))}),null==this.accumulatedSecondMoment[a]&&(this.accumulatedSecondMoment[a]={originalName:`${t}/v`,variable:Ba(()=>pi(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=Ws(Gs(u,this.beta1),Gs(i,1-this.beta1)),d=Ws(Gs(l,this.beta2),Gs(Ai(i),1-this.beta2)),p=Us(c,n),h=Us(d,r);u.assign(c),l.assign(d);const f=Ws(Gs(Us(p,Ws(_i(h),this.epsilon)),-this.learningRate),s);s.assign(f)}),this.accBeta1.assign(Gs(this.accBeta1,this.beta1)),this.accBeta2.assign(Gs(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),null!=this.accumulatedFirstMoment&&Va(this.accumulatedFirstMoment.map(e=>e.variable)),null!=this.accumulatedSecondMoment&&Va(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e),Ba(()=>{this.accBeta1.assign(Ei(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ei(this.beta2,this.iterations_+1))});const t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,2*t).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 Yd extends jd{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=[],Ba(()=>{this.iteration=Ri(0).variable(),this.accBeta1=Ri(t).variable()}),null==r&&(this.epsilon=wa.backend.epsilon())}applyGradients(e){const t=Array.isArray(e)?e.map(e=>e.name):Object.keys(e);Ba(()=>{const n=uu(1,this.accBeta1),r=Us(-this.learningRate,Ws(Gs(this.iteration,this.decay),1));t.forEach((t,a)=>{const s=wa.registeredVariables[t],o=!1;null==this.accumulatedFirstMoment[a]&&(this.accumulatedFirstMoment[a]={originalName:`${t}/m`,variable:pi(s).variable(o)}),null==this.accumulatedWeightedInfNorm[a]&&(this.accumulatedWeightedInfNorm[a]={originalName:`${t}/v`,variable:pi(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=Ws(Gs(u,this.beta1),Gs(i,1-this.beta1)),d=Gs(l,this.beta2),p=Hs(i),h=wu(d,p);u.assign(c),l.assign(h);const f=Ws(Gs(Us(r,n),Us(c,Ws(h,this.epsilon))),s);s.assign(f)}),this.iteration.assign(Ws(this.iteration,1)),this.accBeta1.assign(Gs(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),null!=this.accumulatedFirstMoment&&Va(this.accumulatedFirstMoment.map(e=>e.variable)),null!=this.accumulatedWeightedInfNorm&&Va(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){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 Qd extends jd{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=wa.registeredVariables[t];Ba(()=>{const e=Ws(Gs(this.c,r),a);a.assign(e)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=Wa(Ri(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(0!==(e=await this.extractIterations(e)).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 Zd extends Qd{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=Ri(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=wa.registeredVariables[t];if(null==this.accumulations[n]){const e=!1;this.accumulations[n]={originalName:`${t}/momentum`,variable:Ba(()=>pi(r).variable(e))}}const a=this.accumulations[n].variable,s=Array.isArray(e)?e[n].tensor:e[t];null!=s&&Ba(()=>{let e;const t=Ws(Gs(this.m,a),s);e=this.useNesterov?Ws(Gs(this.c,Ws(s,Gs(t,this.m))),r):Ws(Gs(this.c,t),r),a.assign(t),r.assign(e)})}),this.incrementIterations()}dispose(){this.m.dispose(),null!=this.accumulations&&Va(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);this.accumulations=e.map(e=>({originalName:e.name,variable:e.tensor.variable(false)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}}class Jd extends jd{static get className(){return"RMSProp"}constructor(e,t=.9,n=0,r=null,a=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=r,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=a,null==r&&(this.epsilon=wa.backend.epsilon()),null==e)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(e=>e.name):Object.keys(e)).forEach((t,n)=>{const r=wa.registeredVariables[t],a=!1;null==this.accumulatedMeanSquares[n]&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:Ba(()=>pi(r).variable(a))}),null==this.accumulatedMoments[n]&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:Ba(()=>pi(r).variable(a))}),null==this.accumulatedMeanGrads[n]&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:Ba(()=>pi(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;Ba(()=>{const e=Ws(Gs(o,this.decay),Gs(Ai(s),1-this.decay));if(this.centered){const t=this.accumulatedMeanGrads[n].variable,a=Ws(Gs(t,this.decay),Gs(s,1-this.decay)),u=Us(Gs(s,this.learningRate),_i(uu(e,Ws(Ai(a),this.epsilon)))),l=Ws(Gs(i,this.momentum),u);o.assign(e),t.assign(a),i.assign(l);const c=uu(r,l);r.assign(c)}else{const e=Ws(Gs(o,this.decay),Gs(Ai(s),1-this.decay)),t=Ws(Gs(i,this.momentum),Us(Gs(s,this.learningRate),_i(Ws(e,this.epsilon))));o.assign(e),i.assign(t);const n=uu(r,t);r.assign(n)}})}),this.incrementIterations()}dispose(){null!=this.accumulatedMeanSquares&&Va(this.accumulatedMeanSquares.map(e=>e.variable)),null!=this.accumulatedMeanGrads&&this.centered&&Va(this.accumulatedMeanGrads.map(e=>e.variable)),null!=this.accumulatedMoments&&Va(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){const e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);const t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,2*t).map(e=>({originalName:e.name,variable:e.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(2*t,3*t).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 ep=[qd,Kd,Xd,Yd,Zd,Jd,Qd];function tp(e){return new Promise(e=>setTimeout(e)).then(e)}class np{constructor(e){if(!V().getBool("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a 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s.data())[0]?n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[a].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{const r=Lh("body",e,t,n),a=Lh("cond",e,t,n),s=Lh("args",e,t,n),o=await n.functionMap[a].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),i=s.map(e=>e.id);let u=await o[0].data();o.forEach(e=>{e.kept||-1!==i.indexOf(e.id)||e.dispose()});let l=s;for(;u[0];){const e=l;l=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);const t=l.map(e=>e.id);e.forEach(e=>{e.kept||-1!==i.indexOf(e.id)||-1!==t.indexOf(e.id)||e.dispose()});const s=await n.functionMap[a].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await s[0].data(),s.forEach(e=>{e.kept||-1!==i.indexOf(e.id)||-1!==t.indexOf(e.id)||e.dispose()})}return l}case"LoopCond":return[Hh(Lh("pred",e,t,n))];case"Switch":{const r=Lh("pred",e,t,n);let a=Lh("data",e,t,n);return a.kept||(a=Hh(a)),(await r.data())[0]?[void 0,a]:[a,void 0]}case"Merge":{const r=e.inputNames.find(e=>void 0!==Bh(e,t,n));if(r){return[Hh(Bh(r,t,n))]}return}case"Enter":{const r=Lh("frameName",e,t,n),a=Lh("tensor",e,t,n);return n.enterFrame(r),[Hh(a)]}case"Exit":{const r=Lh("tensor",e,t,n);return n.exitFrame(),[Hh(r)]}case"NextIteration":{const r=Lh("tensor",e,t,n);return n.nextIteration(),[Hh(r)]}case"TensorArrayV3":{const r=Lh("size",e,t,n),a=Lh("dtype",e,t,n),s=Lh("elementShape",e,t,n),o=Lh("dynamicSize",e,t,n),i=Lh("clearAfterRead",e,t,n),u=Lh("identicalElementShapes",e,t,n),l=Lh("name",e,t,n),c=new Of(l,a,r,s,u,o,i);return n.addTensorArray(c),[c.idTensor,Ri(1)]}case"TensorArrayWriteV3":{const r=Lh("tensorArrayId",e,t,n),a=Lh("index",e,t,n),s=Lh("tensor",e,t,n),o=n.getTensorArray(r.id);return o.write(a,s),[o.idTensor]}case"TensorArrayReadV3":{const r=Lh("tensorArrayId",e,t,n),a=Lh("index",e,t,n);return[n.getTensorArray(r.id).read(a)]}case"TensorArrayGatherV3":{const r=Lh("tensorArrayId",e,t,n),a=Lh("indices",e,t,n),s=Lh("dtype",e,t,n);return[n.getTensorArray(r.id).gather(a,s)]}case"TensorArrayScatterV3":{const r=Lh("tensorArrayId",e,t,n),a=Lh("indices",e,t,n),s=Lh("tensor",e,t,n),o=n.getTensorArray(r.id);return o.scatter(a,s),[o.idTensor]}case"TensorArrayConcatV3":{const r=Lh("tensorArrayId",e,t,n),a=n.getTensorArray(r.id),s=Lh("dtype",e,t,n);return[a.concat(s)]}case"TensorArraySplitV3":{const r=Lh("tensorArrayId",e,t,n),a=Lh("tensor",e,t,n),s=Lh("lengths",e,t,n),o=n.getTensorArray(r.id);return o.split(s,a),[o.idTensor]}case"TensorArraySizeV3":{const r=Lh("tensorArrayId",e,t,n);return[Ri(n.getTensorArray(r.id).size(),"int32")]}case"TensorArrayCloseV3":{const r=Lh("tensorArrayId",e,t,n),a=n.getTensorArray(r.id);return a.clearAndClose(),[a.idTensor]}case"TensorListSetItem":{const r=Lh("tensorListId",e,t,n),a=Lh("index",e,t,n),s=Lh("tensor",e,t,n),o=n.getTensorList(r.id);return o.setItem(a,s),[o.idTensor]}case"TensorListGetItem":{const r=Lh("tensorListId",e,t,n),a=Lh("index",e,t,n),s=Lh("elementShape",e,t,n),o=Lh("elementDType",e,t,n);return[n.getTensorList(r.id).getItem(a,s,o)]}case"TensorListScatterV2":case"TensorListScatter":{const r=Lh("indices",e,t,n),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 Ff([],n,e.dtype,r),o=bc(e,0);return t.forEach((e,t)=>{s.setItem(e,o[t])}),s}(Lh("tensor",e,t,n),r,Lh("elementShape",e,t,n),Lh("numElements",e,t,n));return n.addTensorList(a),[a.idTensor]}case"TensorListReserve":case"EmptyTensorList":{const r=Lh("elementShape",e,t,n),a=Lh("elementDType",e,t,n);let s;s="TensorListReserve"===e.op?"numElements":"maxNumElements";const o=Lh(s,e,t,n),i=function(e,t,n,r){return new Ff([],e,t,r)}(r,a,0,"TensorListReserve"===e.op?-1:o);return 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r=Lh("tensorListId",e,t,n),a=n.getTensorList(r.id),s=Lh("dtype",e,t,n),o=Lh("elementShape",e,t,n);return[a.concat(s,o)]}case"TensorListPushBack":{const r=Lh("tensorListId",e,t,n),a=Lh("tensor",e,t,n),s=n.getTensorList(r.id);return s.pushBack(a),[s.idTensor]}case"TensorListPopBack":{const r=Lh("tensorListId",e,t,n),a=Lh("elementShape",e,t,n),s=Lh("elementDType",e,t,n);return[n.getTensorList(r.id).popBack(a,s)]}case"TensorListSplit":{const r=Lh("tensor",e,t,n),a=Lh("elementShape",e,t,n),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=Af(e.shape.slice(1),n),o=0===r?0:e.size/r,i=Ba(()=>{const n=[];e=vo(e,[1,r,o]);for(let r=0;re.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return Ri(this.size(),"int32")}async import(e,t){this.checkKeyAndValueTensor(e,t);const n=await e.data();return this.tensorMap.forEach(e=>e.dispose()),this.tensorMap.clear(),Ba(()=>{const e=bc(t),r=n.length,a=e.length;i(r===a,()=>`The number of elements doesn't match, keys has ${r} elements, the values has ${a} elements.`);for(let t=0;t{const e=[];for(let r=0;r{switch(e.category){case"arithmetic":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"BiasAdd":case"AddV2":case"Add":return[r.add(Lh("a",e,t,n),Lh("b",e,t,n))];case"AddN":return[r.addN(Lh("tensors",e,t,n))];case"FloorMod":case"Mod":return[r.mod(Lh("a",e,t,n),Lh("b",e,t,n))];case"Mul":return[r.mul(Lh("a",e,t,n),Lh("b",e,t,n))];case"RealDiv":case"Div":return[r.div(Lh("a",e,t,n),Lh("b",e,t,n))];case"DivNoNan":return[r.divNoNan(Lh("a",e,t,n),Lh("b",e,t,n))];case"FloorDiv":return[r.floorDiv(Lh("a",e,t,n),Lh("b",e,t,n))];case"Sub":return[r.sub(Lh("a",e,t,n),Lh("b",e,t,n))];case"Minimum":return[r.minimum(Lh("a",e,t,n),Lh("b",e,t,n))];case"Maximum":return[r.maximum(Lh("a",e,t,n),Lh("b",e,t,n))];case"Pow":return[r.pow(Lh("a",e,t,n),Lh("b",e,t,n))];case"SquaredDifference":return[r.squaredDifference(Lh("a",e,t,n),Lh("b",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"basic_math":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"Abs":case"ComplexAbs":return[r.abs(Lh("x",e,t,n))];case"Acos":return[r.acos(Lh("x",e,t,n))];case"Acosh":return[r.acosh(Lh("x",e,t,n))];case"Asin":return[r.asin(Lh("x",e,t,n))];case"Asinh":return[r.asinh(Lh("x",e,t,n))];case"Atan":return[r.atan(Lh("x",e,t,n))];case"Atan2":return[r.atan2(Lh("x",e,t,n),Lh("y",e,t,n))];case"Atanh":return[r.atanh(Lh("x",e,t,n))];case"Ceil":return[r.ceil(Lh("x",e,t,n))];case"Complex":return[r.complex(Lh("real",e,t,n),Lh("imag",e,t,n))];case"Cos":return[r.cos(Lh("x",e,t,n))];case"Cosh":return[r.cosh(Lh("x",e,t,n))];case"Elu":return[r.elu(Lh("x",e,t,n))];case"Erf":return[r.erf(Lh("x",e,t,n))];case"Exp":return[r.exp(Lh("x",e,t,n))];case"Expm1":return[r.expm1(Lh("x",e,t,n))];case"Floor":return[r.floor(Lh("x",e,t,n))];case"Log":return[r.log(Lh("x",e,t,n))];case"Log1p":return[r.log1p(Lh("x",e,t,n))];case"Imag":return[r.imag(Lh("x",e,t,n))];case"Neg":return[r.neg(Lh("x",e,t,n))];case"Reciprocal":return[r.reciprocal(Lh("x",e,t,n))];case"Real":return[r.real(Lh("x",e,t,n))];case"Relu":return[r.relu(Lh("x",e,t,n))];case"Round":return[r.round(Lh("x",e,t,n))];case"Selu":return[r.selu(Lh("x",e,t,n))];case"Sigmoid":return[r.sigmoid(Lh("x",e,t,n))];case"Sin":return[r.sin(Lh("x",e,t,n))];case"Sign":return[r.sign(Lh("x",e,t,n))];case"Sinh":return[r.sinh(Lh("x",e,t,n))];case"Softplus":return[r.softplus(Lh("x",e,t,n))];case"Sqrt":return[r.sqrt(Lh("x",e,t,n))];case"Square":return[r.square(Lh("x",e,t,n))];case"Tanh":return[r.tanh(Lh("x",e,t,n))];case"Tan":return[r.tan(Lh("x",e,t,n))];case"ClipByValue":return[r.clipByValue(Lh("x",e,t,n),Lh("clipValueMin",e,t,n),Lh("clipValueMax",e,t,n))];case"Relu6":return[r.relu6(Lh("x",e,t,n))];case"Rsqrt":return[r.rsqrt(Bh(e.inputNames[0],t,n))];case"LeakyRelu":return[r.leakyRelu(Lh("x",e,t,n),Lh("alpha",e,t,n))];case"Prelu":return[r.prelu(Lh("x",e,t,n),Lh("alpha",e,t,n))];case"IsNan":return[r.isNaN(Bh(e.inputNames[0],t,n))];case"IsInf":return[r.isInf(Bh(e.inputNames[0],t,n))];case"IsFinite":return[r.isFinite(Bh(e.inputNames[0],t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"control":return Df(e,t,n);case"convolution":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"Conv1D":{const a=Lh("stride",e,t,n),s=Lh("pad",e,t,n),o=Lh("dataFormat",e,t,n).toUpperCase(),i=Lh("dilation",e,t,n);return[r.conv1d(Lh("x",e,t,n),Lh("filter",e,t,n),a,s,o,i)]}case"Conv2D":{const 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a(()=>((e,t,n,r=$f)=>{switch(e.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[r.matMul(Lh("a",e,t,n),Lh("b",e,t,n),Lh("transposeA",e,t,n),Lh("transposeB",e,t,n))];case"Einsum":return[r.einsum(Lh("equation",e,t,n),...Lh("tensors",e,t,n))];case"Transpose":return[r.transpose(Lh("x",e,t,n),Lh("perm",e,t,n))];case"_FusedMatMul":const[a,s]=Lh("fusedOps",e,t,n),o="biasadd"===a,i="prelu"===s,u=Lh("numArgs",e,t,n),l=Lh("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]=Lh("args",e,t,n);return[r.fused.matMul({a:Lh("a",e,t,n),b:Lh("b",e,t,n),transposeA:Lh("transposeA",e,t,n),transposeB:Lh("transposeB",e,t,n),bias:c,activation:s,preluActivationWeights:d,leakyreluAlpha:l})];case"MatrixBandPart":return[r.linalg.bandPart(Lh("a",e,t,n),Lh("numLower",e,t,n),Lh("numUpper",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"normalization":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"EuclideanNorm":return[r.euclideanNorm(Lh("x",e,t,n),Lh("axis",e,t,n),Lh("keepDims",e,t,n))];case"FusedBatchNorm":case"FusedBatchNormV2":case"FusedBatchNormV3":return[r.batchNorm(Lh("x",e,t,n),Lh("mean",e,t,n),Lh("variance",e,t,n),Lh("offset",e,t,n),Lh("scale",e,t,n),Lh("epsilon",e,t,n))];case"LRN":return[r.localResponseNormalization(Lh("x",e,t,n),Lh("radius",e,t,n),Lh("bias",e,t,n),Lh("alpha",e,t,n),Lh("beta",e,t,n))];case"Softmax":return[r.softmax(Lh("x",e,t,n))];case"LogSoftmax":return[r.logSoftmax(Lh("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"ragged":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"RaggedGather":{const{outputNestedSplits:a,outputDenseValues:s}=r.raggedGather(Lh("paramsNestedSplits",e,t,n),Lh("paramsDenseValues",e,t,n),Lh("indices",e,t,n),Lh("outputRaggedRank",e,t,n));return 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a(()=>((e,t,n,r=$f)=>{switch(e.op){case"SparseFillEmptyRows":{const{outputIndices:a,outputValues:s,emptyRowIndicator:o,reverseIndexMap:i}=r.sparse.sparseFillEmptyRows(Lh("indices",e,t,n),Lh("values",e,t,n),Lh("denseShape",e,t,n),Lh("defaultValue",e,t,n));return[a,s,o,i]}case"SparseReshape":{const{outputIndices:a,outputShape:s}=r.sparse.sparseReshape(Lh("inputIndices",e,t,n),Lh("inputShape",e,t,n),Lh("newShape",e,t,n));return[a,s]}case"SparseSegmentMean":return[r.sparse.sparseSegmentMean(Lh("data",e,t,n),Lh("indices",e,t,n),Lh("segmentIds",e,t,n))];case"SparseSegmentSum":return[r.sparse.sparseSegmentSum(Lh("data",e,t,n),Lh("indices",e,t,n),Lh("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"spectral":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"FFT":return[r.fft(Lh("x",e,t,n))];case"IFFT":return[r.ifft(Lh("x",e,t,n))];case"RFFT":return[r.rfft(Lh("x",e,t,n))];case"IRFFT":return[r.irfft(Lh("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"string":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"StaticRegexReplace":return[r.string.staticRegexReplace(Lh("input",e,t,n),Lh("pattern",e,t,n),Lh("rewrite",e,t,n),Lh("replaceGlobal",e,t,n))];case"StringNGrams":{const{nGrams:a,nGramsSplits:s}=r.string.stringNGrams(Lh("data",e,t,n),Lh("dataSplits",e,t,n),Lh("separator",e,t,n),Lh("nGramWidths",e,t,n),Lh("leftPad",e,t,n),Lh("rightPad",e,t,n),Lh("padWidth",e,t,n),Lh("preserveShortSequences",e,t,n));return[a,s]}case"StringSplit":{const{indices:a,values:s,shape:o}=r.string.stringSplit(Lh("input",e,t,n),Lh("delimiter",e,t,n),Lh("skipEmpty",e,t,n));return[a,s,o]}case"StringToHashBucketFast":return[r.string.stringToHashBucketFast(Lh("input",e,t,n),Lh("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"transformation":return a(()=>((e,t,n,r=$f)=>{switch(e.op){case"Cast":return[r.cast(Lh("x",e,t,n),Lh("dtype",e,t,n))];case"ExpandDims":{const a=Lh("axis",e,t,n);return[r.expandDims(Lh("x",e,t,n),a)]}case"Squeeze":{const a=Lh("axis",e,t,n);return[r.squeeze(Lh("x",e,t,n),a)]}case"Reshape":return[r.reshape(Lh("x",e,t,n),Lh("shape",e,t,n))];case"EnsureShape":return[r.ensureShape(Lh("x",e,t,n),Lh("shape",e,t,n))];case"MirrorPad":return[r.mirrorPad(Lh("x",e,t,n),Lh("padding",e,t,n),Lh("mode",e,t,n))];case"PadV2":case"Pad":return[r.pad(Lh("x",e,t,n),Lh("padding",e,t,n),Lh("constantValue",e,t,n))];case"SpaceToBatchND":{const a=Lh("blockShape",e,t,n),s=Lh("paddings",e,t,n);return[r.spaceToBatchND(Lh("x",e,t,n),a,s)]}case"BatchToSpaceND":{const a=Lh("blockShape",e,t,n),s=Lh("crops",e,t,n);return[r.batchToSpaceND(Lh("x",e,t,n),a,s)]}case"DepthToSpace":{const a=Lh("blockSize",e,t,n),s=Lh("dataFormat",e,t,n).toUpperCase();return[r.depthToSpace(Lh("x",e,t,n),a,s)]}case"BroadcastTo":return[r.broadcastTo(Lh("x",e,t,n),Lh("shape",e,t,n))];case"BroadcastArgs":return[r.broadcastArgs(Lh("s0",e,t,n),Lh("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n));case"hash_table":return(async(e,t,n,r)=>{switch(e.op){case"HashTable":case"HashTableV2":{const a=r.getHashTableHandleByName(e.name);if(null!=a)return[a];{const a=Lh("keyDType",e,t,n),s=Lh("valueDType",e,t,n),o=new Lf(a,s);return r.addHashTable(e.name,o),[o.handle]}}case"InitializeTable":case"InitializeTableV2":case"LookupTableImport":case"LookupTableImportV2":{const a=Lh("tableHandle",e,t,n,r),s=Lh("keys",e,t,n),o=Lh("values",e,t,n),i=r.getHashTableById(a.id);return[await i.import(s,o)]}case"LookupTableFind":case"LookupTableFindV2":{const a=Lh("tableHandle",e,t,n,r),s=Lh("keys",e,t,n),o=Lh("defaultValue",e,t,n),i=r.getHashTableById(a.id);return[await i.find(s,o)]}case"LookupTableSize":case"LookupTableSizeV2":{const a=Lh("tableHandle",e,t,n,r);return[r.getHashTableById(a.id).tensorSize()]}default:throw TypeError(`Node type ${e.op} is not implemented`)}})(e,t,n,r);case"custom":const s=Ph(e.op);if(s&&s.customExecutor)return s.customExecutor(new Cf(e,t,n));throw TypeError(`Custom op ${e.op} is not registered.`);default:throw TypeError(`Unknown op '${e.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return M(s)?s.then(e=>[].concat(e)):[].concat(s)}class Vf{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;t0===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 Wf(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=>Uh(e)[0]));r=r||[];const c=new Set(r.map(e=>Uh(e.name)[0])),d=[...t];for(;d.length>0;){const e=d.pop();(qf(e)||Kf(e)||Xf(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 zf(e,t){const{usedNodes:n,inputs:r}=t,a=Object.keys(r).map(e=>Uh(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 Uf(`Child ${e.name} of node ${i.name} is unreachable.`);if(n.get(i.name)>n.get(e.name))throw new Uf(`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 Uf(`Input ${e.name} of node ${i.name} is unreachable.`);if(n.get(e.name)>n.get(i.name))throw new Uf(`Node ${i.name} is scheduled to run before its input ${e.name}.`)}}}(f,u),f}class Uf extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}}const Gf=new Set(["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"]),Hf=new Set(["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"]),jf=new Set(["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"]);function qf(e){return Gf.has(e.op)}function Kf(e){return Hf.has(e.op)}function Xf(e){return jf.has(e.op)}class Yf{get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){const t=Object.keys(e).map(t=>e[t].map(e=>e.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{const t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=",",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,null!=e.functions&&Object.keys(e.functions).forEach(t=>{this._functionExecutorMap[t]=new Yf(e.functions[t],this)})}getCompilationKey(e,t){const n=e.map(e=>e.name).sort(),r=t.map(e=>e.name).sort();return n.join(this.SEPARATOR)+"--"+r.join(this.SEPARATOR)}compile(e,t){const n=Wf(e,t,this.weightMap,this._initNodes),{missingInputs:r,dynamicNode:a,syncInputs:s}=n;if(null!=a)throw new Error(`This execution contains the node '${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: [${r}]`)}const o=zf(this.graph,n),i=function(e){const t=new Map(e.map((e,t)=>[e.name,t])),n=Number.MAX_SAFE_INTEGER,r=e.map((e,t)=>qf(e)?n:t),a=e=>r[t.get(e.name)]??-1,s=e.map((e,t)=>e.children.map(a).reduce((e,t)=>Math.max(e,t),r[t])),o=new Map;for(let i=0;ithis.cloneAndKeepTensor(e))}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([e,t])=>[e,this.cloneTensorList(t)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);const n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);const r=n.map(e=>this.graph.nodes[Uh(e)[0]]),a=t.map(e=>Uh(e)[0]),s=new Set(a);let o=a.map(e=>this.graph.nodes[e]);0===o.length&&(o=this._outputs);const i=this.getCompilationKey(r,o);let u=this.compiledMap.get(i);null==u&&(u=this.compile(e,o),this.compiledMap.set(i,u));try{this.keepIntermediateTensors=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}const l={},c={};return Ba(()=>{const n=new Vf(this.weightMap,l,c,this.functionExecutorMap,this.parseNodeNameCache),r=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(t=>{const[a,s]=Uh(t,n),o=[];o[s]=e[t],r[a]=o,this.keepIntermediateTensors&&(this.clonedTensorsMap[a]=this.cloneTensorList(o))});const a=this.getFrozenTensorIds(r),{orderedNodes:o,nodeLiveUntilMap:i}=u;for(const e of o){if(r[e.name])continue;const t=Bf(e,r,n,this._resourceManager);if(M(t))throw new Error(`The execution of the op '${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=>Bh(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(!qf(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(qf(e))continue;const t=Vh(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 qf(e)||a.has(e.name)}if(!qf(e)&&null!=s)for(const i of s){if(o(i))continue;const e=Vh(i.name,t,n);for(const t of e)!t||t.kept||r.has(t.id)||t.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}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}async _executeAsync(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=V().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(d){this.keepIntermediateTensors=!1,console.warn(d.message)}const s=new Vf(this.weightMap,r,a,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));const o=await this.executeWithControlFlow(e,s,t,n),i=t.map(e=>Bh(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}async executeFunctionAsync(e,t,n){const r=e.reduce((e,t,n)=>(e[this.inputs[n].name]=t,e),{});return this._executeAsync(r,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,r){const a=Object.keys(e),s=a.map(e=>this.graph.nodes[Uh(e)[0]]),o=n.map(e=>Uh(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}=Wf(e,u,this.weightMap,this._initNodes),h=[...s,...this.graph.weights,...this._initNodes||[]].map(e=>({node:e,contexts:t.currentContext})),f=Object.assign({},this.weightMap);Object.keys(e).forEach(t=>{const[n,r]=Uh(t),a=[];a[r]=e[t],f[n]=a});const m={},g=this.getFrozenTensorIds(f),y={};for(;h.length>0;){const e=this.processStack(s,h,t,f,y,g,i,m,l);await Promise.all(e)}null!=d||r||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=>!qf(e)&&!Bh(e.name,f,t)).map(e=>e.name);if(b.length>0){let e="";throw null!=d&&(e=`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 [${a}]. Consider providing the following inputs: [${c}]. ${e}`)}return f}processStack(e,t,n,r,a,s,o,i,u){const l=[];for(;t.length>0;){const e=t.pop();n.currentContext=e.contexts;let c="";if("Enter"===e.node.op&&Lh("isConstant",e.node,r,n)&&([c]=Wh(e.node.name,n)),null==r[e.node.name]){const d=Bf(e.node,r,n,this._resourceManager);c||([c]=Wh(e.node.name,n));const p=n.currentContext;M(d)?l.push(d.then(l=>(r[c]=l,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(l)),n.currentContext=p,this.checkTensorForDisposal(c,e.node,r,n,s,o,i),this.processChildNodes(e.node,t,n,r,a,u),l))):(r[c]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[c]=this.cloneTensorList(d)),this.checkTensorForDisposal(c,e.node,r,n,s,o,i),this.processChildNodes(e.node,t,n,r,a,u))}else this.processChildNodes(e.node,t,n,r,a,u)}return l}processChildNodes(e,t,n,r,a,s){e.children.forEach(e=>{const[o]=Wh(e.name,n);!a[o]&&s.has(e.name)&&("Merge"===e.op?e.inputNames.some(e=>!!Bh(e,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:e})):e.inputNames.every(e=>!!Bh(e,r,n))&&(a[o]=!0,t.push({contexts:n.currentContext,node:e})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(e=>e.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{const n=e[t],[r]=Uh(t),a=this.graph.nodes[r];if(a.attrParams.shape&&a.attrParams.shape.value){const e=a.attrParams.shape.value;i(e.length===n.shape.length&&n.shape.every((t,n)=>-1===e[n]||e[n]===t),()=>`The shape of dict['${a.name}'] provided in model.execute(dict) must be [${e}], but was [${n.shape}]`)}a.attrParams.dtype&&a.attrParams.dtype.value&&i(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 ${n.dtype}`)})}mapInputs(e){var t,n;const r={};for(const a in e){const s=null===(n=null===(t=this._signature)||void 0===t?void 0:t.inputs)||void 0===n?void 0:n[a];null!=s?r[s.name]=e[a]:r[a]=e[a]}return r}checkInputs(e){const t=Object.keys(e).filter(e=>{const[t]=Uh(e);return null==this.graph.nodes[t]});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=Uh(e);if(!this.graph.nodes[t])throw new Error(`The output '${e}' is not found in the graph`)})}}class Qf{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 Zf="?tfjs-format=file",Jf="model.json";class em{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=mp){this.modelUrl=e,this.loadOptions=t,this.version="n/a",this.io=n,null==t&&(this.loadOptions={}),this.resourceManager=new Qf}findIOHandler(){const e=this.modelUrl;if(null!=e.load)this.handler=e;else 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null!==e&&e.apply(this,arguments)||this}return im(t,e),t.prototype.preprocessInput=function(e){return Ba(function(){return uu(Us(e,127.5),1)})},t.prototype.nameOutputResults=function(e){return{offsets:e[0],segmentation:e[1],partHeatmaps:e[2],longOffsets:e[3],heatmap:e[4],displacementFwd:e[5],displacementBwd:e[6],partOffsets:e[7]}},t}(xm),wm=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],km=wm.length,Im=wm.reduce(function(e,t,n){return e[t]=n,e},{});function Nm(e,t,n){var r=e[0],a=e[1],s=t[0],o=t[1],i=n.top,u=n.bottom;return[o/(n.left+n.right+a),s/(i+u+r)]}function Sm(e,t,n,r){return{y:r.get(e,t,n),x:r.get(e,t,n+km)}}function Tm(e,t,n){var r=Sm(e.heatmapY,e.heatmapX,e.id,n),a=r.y,s=r.x;return{x:e.heatmapX*t+s,y:e.heatmapY*t+a}}function Cm(e,t,n){return en?n:e}function $m(e,t){return{x:e.x+t.x,y:e.y+t.y}}function Em(e,t,n){void 0===n&&(n=.3);for(var r=0,a=0,s=0;sn&&(a+=1,r+=Math.pow(e[s].x-t.keypoints[s].position.x,2)+Math.pow(e[s].y-t.keypoints[s].position.y,2));return 0===a?r=1/0:r/=a,r}function Rm(e,t,n,r,a,s,o){for(var i=o[0],u=o[1],l=n(e),c=l.y*r+l.x,d=a[km*(2*c)+t],p=a[km*(2*c+1)+t],h=e.y+d,f=e.x+p,m=0;m "+l+") {\n numKpt = numKpt + 1;\n curDistSum = curDistSum + dist(x, y, poseX, poseY);\n }\n }\n if (numKpt > 0 && curDistSum / float(numKpt) < minDist) {\n minDist = curDistSum / float(numKpt);\n iMin = i;\n }\n }\n return iMin;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int nearestPose = findNearestPose(coords[0], coords[1]);\n setOutput(float(nearestPose));\n }\n "};return Ha().compileAndRun(O,[e,x,R])}function Fm(){return"webgl"===Ua()}function Dm(e,t,n,r,a,s,o,i,u,l,c,d){var p=o[0],h=o[1];return void 0===u&&(u=.2),void 0===l&&(l=8),void 0===c&&(c=.3),void 0===d&&(d=10),lm(this,0,void 0,function(){var o,f,m,g,y;return cm(this,function(b){switch(b.label){case 0:return o=n.filter(function(e){return e.score>=u}),Fm()?(m=Ba(function(){var n=Om(e,t,o,r,a,s,[p,h],i,l,c,d),u=La().makeTensorFromDataId(n.dataId,n.shape,n.dtype);return o.map(function(e,t){return n=u,r=t,Ba(function(){return Ls(ci(n,Ri(r)),"int32")});var n,r})}),[4,Promise.all(m.map(function(e){return e.data()}))]):[3,2];case 1:return f=b.sent(),m.forEach(function(e){return e.dispose()}),[3,5];case 2:return[4,e.data()];case 3:return g=b.sent(),[4,t.data()];case 4:y=b.sent(),f=function(e,t,n,r,a,s,o,i,u,l){var c=o[0],d=o[1];void 0===l&&(l=5);for(var p=n.map(function(e){return new Uint8Array(r*a).fill(0)}),h=i.top,f=i.left,m=Nm([r,a],[c,d],i),g=m[0],y=m[1],b=Am([c,d],s)[0],x=0;x=0&&(p[k][w]=1)}}return p}(g,y,o,r,a,s,[p,h],i,l),b.label=5;case 5:return[2,f.map(function(e,t){return{data:e,pose:o[t],width:a,height:r}})]}})})}function Mm(e,t,n,r,a,s,o,i,u,l,c,d,p){var h=i[0],f=i[1];return void 0===l&&(l=.2),void 0===c&&(c=8),void 0===d&&(d=.3),void 0===p&&(p=10),lm(this,0,void 0,function(){var i,m,g,y,b,x;return cm(this,function(v){switch(v.label){case 0:return i=r.filter(function(e){return e.score>=l}),Fm()?(g=Ba(function(){var r=Om(e,t,i,a,s,o,[h,f],u,c,d,p),l=La().makeTensorFromDataId(r.dataId,r.shape,r.dtype);return i.map(function(e,t){return r=l,a=n,s=t,Ba(function(){return uu(Gs(Ls(ci(r,Ri(s)),"int32"),Ws(a,1)),1)});var r,a,s})}),[4,Promise.all(g.map(function(e){return e.data()}))]):[3,2];case 1:return m=v.sent(),g.forEach(function(e){return e.dispose()}),[3,6];case 2:return[4,e.data()];case 3:return y=v.sent(),[4,t.data()];case 4:return b=v.sent(),[4,n.data()];case 5:x=v.sent(),m=function(e,t,n,r,a,s,o,i,u,l,c){var d=i[0],p=i[1];void 0===c&&(c=5);for(var h=r.map(function(e){return new Int32Array(a*s).fill(-1)}),f=u.top,m=u.left,g=Nm([a,s],[d,p],u),y=g[0],b=g[1],x=Am([d,p],o)[0],v=0;v=0&&(h[I][k]=n[k])}}return h}(y,b,x,i,a,s,o,[h,f],u,c),v.label=6;case 6:return[2,m.map(function(e,t){return{pose:i[t],data:e,height:a,width:s}})]}})})}function Pm(e){return Math.floor(e/2)}[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]].map(function(e){var t=e[0],n=e[1];return[Im[t],Im[n]]});var Lm=function(){function e(e,t){this.priorityQueue=new Array(e),this.numberOfElements=-1,this.getElementValue=t}return e.prototype.enqueue=function(e){this.priorityQueue[++this.numberOfElements]=e,this.swim(this.numberOfElements)},e.prototype.dequeue=function(){var e=this.priorityQueue[0];return this.exchange(0,this.numberOfElements--),this.sink(0),this.priorityQueue[this.numberOfElements+1]=null,e},e.prototype.empty=function(){return-1===this.numberOfElements},e.prototype.size=function(){return this.numberOfElements+1},e.prototype.all=function(){return this.priorityQueue.slice(0,this.numberOfElements+1)},e.prototype.max=function(){return this.priorityQueue[0]},e.prototype.swim=function(e){for(;e>0&&this.less(Pm(e),e);)this.exchange(e,Pm(e)),e=Pm(e)},e.prototype.sink=function(e){for(;2*e<=this.numberOfElements;){var t=2*e;if(tt){l=!1;break}if(!l)break}return l}var Vm=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]].map(function(e){var t=e[0],n=e[1];return[Im[t],Im[n]]}),Wm=Vm.map(function(e){return e[1]}),zm=Vm.map(function(e){return e[0]});function Um(e,t,n,r){return{y:Cm(Math.round(e.y/t),0,n-1),x:Cm(Math.round(e.x/t),0,r-1)}}function Gm(e,t,n,r,a,s,o,i){void 0===i&&(i=2);for(var u=r.shape,l=u[0],c=u[1],d=function(e,t,n){var r=n.shape[2]/2;return{y:n.get(t.y,t.x,e),x:n.get(t.y,t.x,r+e)}}(e,Um(t.position,s,l,c),o),p=$m(t.position,d),h=0;h=0;--p){var h=Wm[p],f=zm[p];u[h]&&!u[f]&&(u[f]=Gm(p,u[h],f,t,n,r,s))}for(p=0;p=.1,function(){return"inputResolution must be a string or number between 0.1 and 2, but was "+e}),e}(e);return[tg(r*s,t),tg(a*s,t)]}function sg(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 Ba(function(){var t=Md.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 Ba(function(){var t=Li(e);return Jl(Md.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=So(t),[s,o],[[c,d],[h,f]])})}function og(e,t){var n=t[0],r=t[1],a=eg(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/s1)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 yg(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 bg=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=eg(e),s=a[0],o=a[1],i=ag(t,this.baseModel.outputStride,[s,o]),u=og(e,i),l=u.resized,c=u.padding,d=Ba(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=sg(t,[s,o],[h,f],[[c.top,c.bottom],[c.left,c.right]]);return{segmentation:ym(Jl(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=fg),lm(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 cm(this,function(v){switch(v.label){case 0:return gg(t=um(um({},fg),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,ig([a,s,o,i])];case 2:return f=v.sent(),m=f[0],g=f[1],y=f[2],b=f[3],x=ug(x=Km(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=mg),lm(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 cm(this,function(N){switch(N.label){case 0:return yg(t=um(um({},mg),t)),n=eg(e),r=n[0],a=n[1],s=ag(t.internalResolution,this.baseModel.outputStride,[r,a]),o=og(e,s),i=o.resized,u=o.padding,l=Ba(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=sg(o,[r,a],s,[[u.top,u.bottom],[u.left,u.right]]);return e=l,{segmentation:ym(Jl(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,ig([p,h,f,m])];case 1:return g=N.sent(),y=g[0],b=g[1],x=g[2],v=g[3],w=ug(w=Km(y,b,x,v,this.baseModel.outputStride,t.maxDetections,t.scoreThreshold,t.nmsRadius),[r,a],s,u,!1),[4,Dm(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=eg(e),s=a[0],o=a[1],i=ag(t,this.baseModel.outputStride,[s,o]),u=og(e,i),l=u.resized,c=u.padding,d=Ba(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=sg(t,[s,o],[f,m],[[c.top,c.bottom],[c.left,c.right]]),y=sg(a,[s,o],[f,m],[[c.top,c.bottom],[c.left,c.right]]);return{partSegmentation:bm(ym(Jl(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=fg),lm(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 cm(this,function(v){switch(v.label){case 0:return gg(t=um(um({},fg),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,ig([a,s,o,i])];case 2:return f=v.sent(),m=f[0],g=f[1],y=f[2],b=f[3],x=ug(x=Km(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=mg),lm(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 cm(this,function(S){switch(S.label){case 0:return yg(t=um(um({},mg),t)),n=eg(e),r=n[0],a=n[1],s=ag(t.internalResolution,this.baseModel.outputStride,[r,a]),o=og(e,s),i=o.resized,u=o.padding,l=Ba(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=sg(n,[r,a],s,[[u.top,u.bottom],[u.left,u.right]]),m=sg(h,[r,a],s,[[u.top,u.bottom],[u.left,u.right]]),g=o;return{segmentation:ym(Jl(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 Ba(function(){var t=gm(e),s=Li(Nl(0,a,1,"int32"),1),o=Ls(No(t,s),"int32");return vo(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,ig([p,h,f,m])];case 1:return y=S.sent(),b=y[0],x=y[1],v=y[2],w=y[3],k=ug(k=Km(b,x,v,w,this.baseModel.outputStride,t.maxDetections,t.scoreThreshold,t.nmsRadius),[r,a],s,u,!1),[4,Mm(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 xg(e){return lm(this,0,void 0,function(){var t,n,r,a,s,o;return cm(this,function(i){switch(i.label){case 0:if(t=e.outputStride,n=e.quantBytes,r=e.multiplier,null==Ah)throw new Error("Cannot find TensorFlow.js. If you are using a